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Empowering Education with Artificial Intelligence in Ethnic Regions

Thu, 30 Apr 2026 00:00:00 +0000

Introduction

The Central Committee of the Communist Party and the State Council place great importance on the profound impact of artificial intelligence (AI) on education. General Secretary Xi Jinping has emphasized the need to deeply implement the national education digitalization strategy, strengthen the national smart education public service platform, explore effective ways to empower personalized and innovative teaching through digital means, expand the benefits of high-quality educational resources, and leverage AI to facilitate educational transformation. In April 2026, the Ministry of Education and four other departments jointly issued the “AI + Education Action Plan,” providing a historic opportunity for the balanced development of quality education empowered by AI in ethnic regions.

Focus on Unique Needs: Deepening AI Empowerment in All Aspects of Education

Students in ethnic regions have unique cognitive foundations, language environments, and learning habits, leading to significant differences in learning conditions. It is crucial to integrate AI into the entire educational process and empower all aspects of education to accurately respond to the personalized and differentiated needs of teachers and students. In terms of value guidance, it is important to effectively utilize ideological models and scenario-based intelligent applications to embed core content such as the education of the awareness of the Chinese national community, the inheritance and development of excellent traditional Chinese culture, and the promotion of the national common language into immersive intelligent educational products, making abstract theories tangible. By combining red resources with cases of national unity and progress, a specialized ideological education resource library can be built to align knowledge literacy with value shaping, constructing a shared spiritual home for the Chinese nation.

In terms of precise assistance in learning, intelligent learning companions equipped with contextual guidance and cultural adaptation functions can be used to accurately capture students’ cognitive characteristics through technologies such as knowledge graphs and emotional computing, monitoring knowledge consolidation points and weaknesses in real-time, and creating personalized, progressive learning paths to implement large-scale personalized teaching. For students learning the national common language, features such as voice assessment, intelligent pronunciation correction, and engaging dialogues can enhance language skills. In teaching empowerment, intelligent teaching systems can create a closed-loop process of precise lesson preparation before class, dynamic optimization during class, and evidence-based research after class. Before class, intelligent recommendations can optimize teaching resources for efficient lesson preparation; during class, real-time monitoring of learning conditions allows for flexible adjustments to teaching strategies; after class, in-depth analysis of teaching behaviors drives reflection and improvement. This closed-loop significantly enhances classroom quality and effectiveness, especially providing strong teaching support for schools with weak faculty.

Enhancing Adaptability: Promoting Full-Chain Optimization of Educational Resources Empowered by AI

The construction of educational resources in ethnic regions has shifted from merely increasing quantity to enhancing effectiveness, focusing on breaking through the conversion chain from supply to application to improve the adaptability of resources to teaching scenarios. In terms of resource supply, digital resources that are specialized, localized, and multimodal should be developed around the key educational needs of ethnic regions. Localities are encouraged to build regional educational corpora, utilizing the national smart education platform for content adaptation, localization of cases, and dynamic updates to achieve precise matching of educational resources with teaching scenarios. In resource allocation, priority should be given to deploying high-speed networks and edge computing nodes in border pastoral areas, national border schools, remote teaching points, and boarding schools to solidify the foundation for resource circulation. By relying on provincial-level intelligent bases to break down data barriers across platforms, resource integration and scheduling can be strengthened to ensure that quality resources are accessible, operational, and comprehensive. An intelligent channel for paired support of educational resources between eastern and western regions should be established to facilitate the targeted delivery and localization of quality resources. In resource application, the national smart education platform should establish a dynamic monitoring and feedback mechanism for resource operation and usage, conducting layered analysis based on teacher application data, resource usage preferences, and student engagement, while continuously optimizing intelligent recommendation and push strategies to enhance the effectiveness of resource application in teaching scenarios. To address the practical difficulties faced by some teachers who are hesitant to use digital resources, expert guidance teams should conduct case promotions and on-site guidance to ensure that quality resources are truly understandable, usable, and effective.

Focusing on Skill Enhancement: Strengthening Support for Teachers Empowered by AI

Teachers are the primary resource for high-quality educational development. Enhancing the quality of education in ethnic regions hinges on improving teachers’ intelligent literacy and teaching competence. In terms of training systems, differentiated training should be implemented, with key teachers focusing on the development and application of intelligent teaching tools, young teachers strengthening data-driven learning analysis and precise teaching, and other teachers emphasizing foundational applications and concept updates. Strengthening county-level “smart education master studios” can play a demonstrative role, encouraging young teachers to lead older ones, promoting a shift from “knowing how to use” to “willing to use and good at using”. An integrated online and offline training platform should be established, combining school-based cases for practical exercises, promoting the “National Training Program” to provide precise support for the construction of the teacher workforce in ethnic regions, and incorporating AI into the curriculum of teacher training colleges in these areas to solidify the foundation of the workforce from the source.

In terms of research and training mechanisms, an intelligent platform for the professional development of teachers in ethnic regions should be constructed, generating personalized training suggestions through the analysis of teachers’ classroom teaching behavior data to form an integrated model of “teaching, learning, research, and evaluation”. Support should be provided for the establishment of networked research communities across schools and regions to gradually narrow the gap in regional training. Regular workshops on AI teaching applications, teaching competitions, and other activities should be organized, with award-winning lesson examples promoted through the national smart education platform. In terms of incentive evaluation, intelligent literacy and teaching application effectiveness should be included in the teacher assessment and evaluation system, with special incentives and project funding established for teachers who excel in AI education, ensuring they receive preferential treatment in title evaluations and awards, thereby fostering a positive atmosphere of “promoting learning through use and encouraging excellence through evaluation”.

Promoting Continuity Across All Education Stages: Building an AI-Empowered Talent Development System in Ethnic Regions

The cultivation of AI literacy needs to permeate the entire talent development process, establishing a vertically integrated and horizontally connected education system for AI across all stages and a general education system for society. In terms of vertical integration, a “General Education Guide for AI in Primary and Secondary Schools” suitable for the realities of ethnic regions can be established in the basic education stage, setting gradient goals by educational stage and stimulating students’ AI literacy through project-based learning and gamified courses. In higher education, AI should be promoted as a public foundational course in universities in ethnic regions, facilitating the interdisciplinary integration of AI with specialized advantageous disciplines. In vocational education, traditional programs should be upgraded with AI, and order-based training should be conducted. Promoting integrated cultivation across all educational levels, digital student records should be effectively utilized to provide personalized learning path planning. AI should be incorporated into lifelong learning systems to create a ubiquitous learning environment that combines online and offline elements.

In terms of horizontal connectivity, the mechanism for collaborative education among families, schools, and communities should be deepened, extending AI literacy education to family enlightenment and community spaces. General AI courses for parents should be developed, expanding coverage through community learning centers and senior universities. Ethnic region universities should open quality educational resources to society, promoting deep integration of education among schools, families, and communities. Collaborative education between industry, academia, and research should be promoted, focusing on the needs of local industries such as smart agriculture and cultural tourism in ethnic regions, establishing AI industry-education integration training bases, and supporting leading enterprises to co-build industry colleges with local institutions, relying on industry-education integration models to create a “industry-job-course” map, effectively aligning talent development with industrial growth.

Strengthening All-Factor Coordination: Promoting Systemic Reform in Educational Governance Empowered by AI

The modernization level of educational governance in ethnic regions directly affects the overall effectiveness of AI empowerment in education. It is necessary to focus on strengthening policy coordination, resource adaptation, and condition guarantees, while emphasizing the construction of intelligent hubs, monitoring and early warning systems, and collaborative safety guarantees. In terms of intelligent hub construction, relying on the National Education Big Data Center, an intelligent regional education brain should be built that integrates data aggregation, decision support, policy push, and demand response. A cross-departmental and cross-level data sharing mechanism should be established to achieve precise policy transmission and timely feedback collection, enhancing the responsiveness and execution effectiveness of educational policies in ethnic regions. Regions with conditions should be supported to take the lead in trials, with intelligent data collection terminals prioritized for deployment in boarding schools and central schools in towns, exploring a smart service model of “one screen overview, one network handling”.

In monitoring and early warning, big data intelligent monitoring technology should be utilized to dynamically perceive risks such as ideological safety, campus safety, and school dropout rates, constructing a multidimensional early warning indicator system covering teaching quality, teacher mobility, resource allocation, and student development, establishing an intelligent early warning and closed-loop feedback system for early detection, prevention, and assistance, providing scientific basis for precise governance. In terms of safety guarantees, adhering to the principle of “intelligence for good,” it is essential to ensure the security of content, data, and algorithms, improving assessment filing, technical monitoring, risk warning, and emergency response mechanisms, strengthening the security protection of educational data throughout its lifecycle, effectively preventing issues such as algorithm discrimination, privacy breaches, and exam-oriented education, ensuring that AI applications operate within a regulated, trustworthy, and benevolent framework.

Empowering education in ethnic regions with AI is a long-term systematic project that requires a unified national approach. Only by adhering to a problem-oriented approach and prioritizing application can we promote the coordinated efforts of technology, resources, talent, and governance through innovative practices, implementing precise policies and sustained efforts, transforming AI into the “key variable” for the quality and balanced development of education in ethnic regions, and laying a solid foundation for building a strong education nation and promoting national unity and progress.

Stop Using Cursor as a Completer: Skills are the Key

Thu, 30 Apr 2026 00:00:00 +0000

Stop Using Cursor as a Completer: Skills are the Key

Last night, I watched a friend struggle with Cursor for nearly forty minutes while trying to modify a project.

He wasn’t incapable of writing prompts. The issue was more complicated. Every time he started a new session, he had to explain the project structure, tech stack, naming conventions, and interface boundaries, plus add a note saying, “Don’t touch this directory; it’s in production.” By the time he finished, the AI was just warming up. When it finally began to write, it often went off track, either altering files it shouldn’t or generating code that, while functional, wasn’t suitable for the team.

I’m all too familiar with this scenario.

From 2024 to 2025, while discussing AI programming tools with several teams, the common complaint was not about the AI’s inability to generate code, but rather how difficult it was to manage the output after generation. You can prompt it to write, but if you expect it to consistently produce the same style for a week, problems start to arise.

Many people think their issues with Cursor stem from not crafting long or precise prompts, or from using a weak model. This is often not the primary issue. More commonly, they treat something that should be established as a “fixed context” as temporary chat content, re-entering it each time.

In simple terms, whether Cursor evolves from a “high-level completer” to a “reliable co-pilot” often depends not on the model but on the skills.

The term skill can be replaced with rules, playbooks, or project workflow templates. The name isn’t important. Essentially, it answers four key questions in advance: Who are you? What is this project? What absolutely cannot be done? What order should tasks be handled in when encountering certain types of tasks?

In one sentence:

Skills don’t make AI smarter; they prevent it from taking the detours you’ve already navigated.

Why Many Users Feel More Exhausted with Cursor

The most common misconception I’ve seen is treating Cursor as a powerful intern who is always available but never providing it with an onboarding manual.

The result is that, despite working in the same repository with similar requirements, you have to redo three things each time.

First, re-explain the background. Is this repository a monolith or microservices? Is the frontend in apps/web or src/client? Should tests use Jest or Vitest? Does the API response need to wrap in a data layer? Without a fixed entry point, the AI can only guess, and when it guesses, the style goes off. To put it bluntly, it becomes ridiculous.

Second, re-explain the standards. For example, “don’t write overly long functions,” “don’t casually introduce new dependencies,” “tests must be added after modifications,” and “the interface layer should uniformly go through service, not directly connect fetch in the page.” If you don’t specify, it won’t consistently adhere. If you say it today, it forgets tomorrow when you start a new conversation. This can be very frustrating.

Third, re-explain the process. Many people start with, “Help me fix this bug.” The problem is that a reliable process shouldn’t be a direct fix. It should involve reading the error, identifying the impact scope, explaining the solution, modifying the code, and listing verification steps at the end. Without this process, the AI will use the easiest way to complete the task, which is often not what you want.

The most annoying part isn’t just fixing mistakes.

It’s that you slowly develop the illusion that this tool seems great sometimes and particularly dumb at others. In reality, it’s not that it’s suddenly smart or confused; it’s more likely that the quality of context you provide varies each time. Unstable context leads to unstable outputs. You and it end up going in circles, making you increasingly exhausted.

This is the first layer of the problem that skills aim to solve: turning high-frequency, repetitive, easily overlooked background information into long-term reusable default premises.

What Skills Actually Supplement: Work Methods, Not Prompts

I increasingly dislike defining skills as “a more advanced prompt.” This understanding is somewhat superficial.

A truly useful skill should encompass at least four layers of information.

One layer is the role. What do you want Cursor to play at this moment? Is it a cautious reviewer, a researcher before taking action, or a bug fixer making minimal changes? Different roles yield entirely different outputs.

Another layer is the project context. Repository structure, core modules, dependency constraints, directories that must not be touched, existing scripts, and team-preferred commands. The more specific, the better. Avoid vague statements like “please adhere to best practices”; they are useless. Instead, write things like “prioritize searching with rg,” “read README.md and CONTRIBUTING.md before modifying,” “do not upgrade dependencies without explicit request,” and “do not modify lockfile unless I explicitly ask.”

Another layer is the execution checklist. For certain types of tasks, what should be done first, what should be done next, when must one stop to ask someone, and when can one continue independently? This is particularly valuable because most negative feedback arises not from coding ability but from the order of execution.

The final layer is the output format. For example, you might require it to first give conclusions, then changes, and finally verification commands; or to list risks before proceeding. These format constraints may seem trivial, but they directly affect collaboration costs. Many reworks aren’t due to coding errors but rather unreliable reporting methods.

You see, skills fundamentally manage not “expression” but “method.”

The same request to “fix this bug” feels like improvisation without skills; with skills, it feels like entering a well-structured editorial department with SOPs.

I even suggest writing down the most useful trivialities. For example:

Before handling tasks, determine if additional context is needed.
If more than three files are involved, provide a modification plan before proceeding.
If a user has uncommitted changes, do not overwrite; ensure compatibility first.
If tests fail, clearly state where the issue lies; do not pretend it’s completed.

These statements aren’t sophisticated.

But they are lifesavers.

What to Prioritize: Three Types of Skills

Many people jump straight into building a comprehensive skill system, resulting in a document museum. The directory looks impressive, but no one refers to it, and the AI isn’t consistently utilizing it.

Don’t go that big.

Start with just three types.

1. Project Onboarding Skill

This skill addresses the issue of “having to reintroduce the project every time.”

The content can be quite simple: project structure, key directories, tech stack, common commands, coding style, restricted areas, and validation methods. Keep it between 300 to 600 words, plus a few critical file paths. It doesn’t need to cover everything; it just needs to prevent the AI from going off track at the start.

For example, you can specify:

Read README.md first
Prioritize searching with rg
Follow existing hooks style when modifying React code
Check api and service layers before modifying interfaces
Don’t claim “already validated” without running tests

Once these constraints are established, you’ll noticeably save time in the first ten minutes of conversation.

2. High-Frequency Task Skill

Extract the most common tasks into templates.

For instance, “fixing online bugs,” “writing management backend forms,” “conducting API integration,” “adding unit tests,” and “performing PR reviews.” The judgment order for each task differs. Fixing bugs should involve reproducing the issue before making changes; reviews should prioritize identifying risks before discussing merits; and adding tests should confirm current behavior before writing assertions.

Don’t hesitate to write in a straightforward manner. The more it resembles the operation manual left by the most reliable colleague in the team, the better. No, it should be said that the less it resembles “official tutorials,” the more likely it is to survive in the team.

I personally value review skills highly because they yield immediate results. Without skills, AI often writes reviews like “overall good, suggest optimizing readability.” Such comments are as good as unread. With rules, you can force it to prioritize reporting bugs, performance risks, behavioral regressions, and missed tests before deciding whether to summarize.

3. Boundary Constraint Skill

This skill specifically addresses “don’t mess around.” Many incidents start from “just a quick fix.”

Which directories are prohibited from modification, which commands cannot be executed directly, under what circumstances manual confirmation is needed, when to proceed conservatively, and when to take initiative. Many incidents occur not because AI can’t write code but because it’s too eager to help. Once it gets enthusiastic, it starts casually refactoring, upgrading, or cleaning up. Casualness often leads to disaster. When you look back at git diff, it can be quite overwhelming.

Therefore, boundaries must be clearly defined.

Can files be deleted? Can schemas be modified? Can dependencies be updated? What to do when encountering a dirty workspace? When there’s a conflict between requirements and the current state, should one continue guessing or stop first? If you don’t specify, the AI will handle it according to its default preferences, which are often more aggressive than yours.

The Effective Process for Using Skills

If you want to start today, I recommend not spending too long on theory but rather following this order.

First, choose a task you will perform at least twice a week. Low-frequency tasks aren’t worth abstracting into skills.

Then, copy the phrases you’ve repeatedly added to Cursor in the past three attempts verbatim. Note, verbatim. Don’t beautify them. The sentences you have to say each time are the best raw materials for skills.

Next, divide them into three sections: background, process, and constraints. Background answers “what is this?” Process answers “how to do it?” Constraints answer “what should not be done?” At this point, a usable skill is basically formed.

Take another step forward.

Add two examples: one good example and one bad example. The good example tells the AI what meets expectations; the bad example shows which actions seem proactive but actually complicate matters. Adding just one example can significantly enhance stability. Even a 30% improvement in stability can save you a lot of back-and-forth communication in a week.

Another detail many people overlook: skills aren’t finished once written; they should evolve with the project.

Each time you notice Cursor making a repeated mistake, don’t just correct it in that conversation. Incorporate that correction back into the skill. Each time you find a particular output format significantly reduces communication, don’t just remember it; write it down. This way, it will increasingly resemble a member of your team rather than a temporary contractor.

Here’s a practical judgment standard: if a skill doesn’t reduce your background input by half, or if it doesn’t cut down two rounds of direction changes, it’s likely too vague. Delete it and start over. Don’t be sentimental.

Skills aren’t collectibles.

They should function like a wrench, ready for use.

So stop asking “how to make Cursor smarter.” Change the question. No, make it a tougher question.

Have you seriously handed over your work methods to it?

The Rise of Vibe Coding: Transforming Programming Culture

Thu, 30 Apr 2026 00:00:00 +0000

Hack (programming) is undergoing a demystification process, driven by programmers themselves.

Handcrafted code has become a “cultural heritage,” while vibe coding dominates, allowing anyone to become a full-stack developer.

Even though vibe coding was a concept introduced by Andrej Karpathy in February last year, and AI coding is still in its infancy, top AI coding tools like Claude Code and Codex have made significant strides in less than a year.

The landscape for programmers has become more polarized: top architects remain irreplaceable, while junior “code movers” are deeply entrenched in the “AI replacement” crisis.

A group of individuals is quietly benefiting from this shift. They include students with “zero programming background” and programmers, designers, and product managers who have left large companies to dive into the wave of one-person companies (OPC).

These young developers are quickly embracing AI coding, reflecting a typical disparity in technical understanding among programmers, highlighting how technology has begun to differentiate personnel levels.

Every two lines of new code on GitHub are generated by AI. Active developers on social platforms may account for less than 0.05% of monthly active users, yet they are already driving the next wave.

Recently, at a hackathon peak competition on Xiaohongshu, a group of young independent developers quickly transformed ideas into products using AI programming tools. Programming has become demystified, and creation has never been so enchanting.

Traditional Programming: Stinky Shoes, Loneliness, and Hierarchies

Chen Jinchun, a post-2000s developer, humorously refers to himself as an “old-timer” in the AI era, having personally experienced the hardships of traditional programming.

“I used to participate in hackathons, and 95% of the participants were male,” recalls Chen, a serial entrepreneur and independent developer born in 2001.

He began attending hackathons in 2021, both in the U.S. and various venues in China. “When you enter the venue, if it’s a house, the men’s shoes are piled up at the entrance, and it smells terrible.”

In the era of “traditional programming,” the logic of forming teams for a hackathon was relatively complex: you needed to gather front-end, back-end, and operations experts, with each person’s coding ability needing to be up to par.

Chen stated, “We had to specifically choose people; everyone’s coding skills had to be on point to form a team.”

Born in 2001, Chen’s sense of crisis as an “old-timer” stems from the accelerated emergence of talent under AI.

“Last year, many entrepreneurs we met were born in ‘95 or ‘97, but this year, many are already born in ‘00,” noted prominent investor and Monolith founder Cao Xi at the hackathon.

Yang Xizhe, a 13-year-old from the post-2010s generation, has taught 5 million people to memorize words through videos like “AI Vocabulary Learning” on social platforms.

He first encountered programming in second grade when his father introduced him to open-world games like “Minecraft” and “The Legend of Zelda,” sparking his desire to create his own game.

Upon learning that making games required programming skills, he started with the graphical programming tool Scratch. After finishing fourth grade, he transitioned to C++ and began competing in the National Olympiad in Informatics (NOI).

Yang has experience in hackathons and has written a lot of C++ code for the competitions.

From his perspective, “In the past, competitions mainly tested your code functionality and algorithms; in simple terms, it was about who could code better or who could code for longer.”

The style of products in previous competitions was also more rigid.

Chen remarked, “Pre-AI hackathons were more geeky, focusing on inference, with stricter requirements on products and technology, but lacking human touch.”

As the circles further generalized, the overall atmosphere changed as well.

“In some hackathons I participated in, other teams didn’t communicate, viewing each other as competitors, so there was no exchange of ideas,” Yang admitted. Previously, teams were reluctant to share ideas for fear of having their concepts stolen.

In that relatively closed world, where coding ability and endurance were the measures of worth, “creativity” became a mere accessory to engineering capability. Most products created by geeks were technically complex but struggled to reach ordinary people’s lives.

The Demystification of Technology: Everyone is a Full-Stack Developer

Programmers typically take pride in mastering difficult languages (like Rust or C++), but this elitism is being deconstructed by AI coding tools.

Chen deeply feels this shift. Born in 2001, he holds dual degrees in Computer Science and Management from MIT.

He started making money from programming early on. In 2018, during the global sneaker trading craze, he wrote a Python script that could monitor and automatically place orders for sneakers, providing “shovels” for sneaker traders and earning his first pot of gold.

After experiencing turmoil in the cryptocurrency industry, he saw greater opportunities with AI programming last year. He began using AI tools like Cursor to create interesting products.

Claude Code particularly surprised Chen.

Although he studied computer science in college, his skills aren’t that advanced. He has many imaginative ideas in his mind and sees numerous money-making opportunities. With the advent of vibe coding, he felt as if he had received divine assistance.

“For someone like me, who knows a bit about coding but isn’t an expert, this is a tremendous breakthrough. With the help of AI, I feel my abilities are comparable to those of the big shots I once admired,” Chen said.

The mystique of technology is fading. The logic of forming teams for competitions or startups has changed.

“Now, at hackathons, one person can handle everything,” Chen said. “If you want to be a successful entrepreneur in AI technology, the most important skill is marketing. You need to tell a compelling story, understand communication, and leverage information gaps between domestic and international markets.”

The preferred product style among geeks has also changed.

At the hackathon peak competition, the most attention-grabbing projects included a brainwave-controlled wheelchair, an AI hairstylist, a self-discipline headset that “shocks” you when you feel sleepy, an embodied intelligent mahjong robot, and a mechanical arm for brushing teeth and blow-drying hair in the bathroom—each with a very “unserious” creative flair.

“Most AI products now have a strong human touch,” Chen reflected. “This is the biggest difference from before.” Chen is experimenting with AI hardware products, including a self-discipline headset that contains a camera to capture user behavior. “For example, if you want to quit drinking, you get shocked when you drink.”

Chen also noted a detail from the hackathon: “There were many more female participants this time, which I couldn’t have imagined in previous hackathons.”

The increasing number of female developers participating in hackathons is largely attributed to the user structure of the Xiaohongshu platform.

Yang Xizhe, during competitions, has not experienced much of the pain of not being able to gather a full-stack team, nor has he suffered through the “stinky shoes piled at the entrance” of venues.

He enjoys the vibe of the process.

Once, at a hackathon, when he successfully ran a product function, his teammates described him as being as happy as a monkey.

He even likened vibe coding to playing games: “When playing games, everyone feels happy and doesn’t think about time stopping; for me, vibe coding is the same as playing games.”

Yang is moved by the atmosphere of the current developer community. “Many teams are our competitors, but they still encourage us, wishing us to make it to the Top 10 and to continue developing our projects. Everyone is genuinely kind.”

This time, Yang’s team, consisting of three other middle school students, completed a popular note diagnosis agent system in less than 24 hours.

Xian Xinglang, a high school student born in 2008, is another student developer who has received strong positive feedback from AI programming. Last year, his first app—an AI healing application called EmoEase—reached fifth place on the App Store’s paid chart.

Before engaging in AI programming, Xian’s daily routine consisted of school, homework, and watching videos. Due to student council work, he used Cursor to develop a website showcasing student activities. He began to taste the sweetness of AI programming and dove in.

His personal experience reflects a turning point not only from learning AI programming skills in online communities but also from the positive feedback among independent developers, including Manus founder Xiao Hong.

Before EmoEase was launched, Xian discovered that as an individual developer, he needed to pay an annual fee of 688 yuan for the app, which posed a challenge for him without a fixed income. He reached out for help in the developer community, and Xiao Hong saw the message, directly covering the annual fee and expressing encouragement.

“After getting into AI programming, on one hand, I received positive feedback from others; on the other hand, I genuinely immersed myself in the independent development process, which was very enjoyable. This was a significant transformation in my life.” Now, after school, Xian’s first task is to open his computer and develop, often neglecting his homework.

The New “Three Axes”: Creativity + Communication + Empathy

In the pre-AI era, the strength of programming skills was a key factor in dividing the geek hierarchy. In hackathon competitions, they competed in coding speed, algorithm proficiency, and team composition (which had to include both front-end and back-end).

In the vibe coding era, the key to winning has shifted to ideas (creativity) and marketing (narrative/communication).

“Vibe coding has really changed a lot,” Yang candidly stated. “Now at hackathons, the starting point determines the outcome of the competition. The main thing is to have a good idea; as long as you refine the code afterward, you might win. If you write a lot of code but it lacks commercial value, or if your idea is ordinary and overlaps with others, you won’t win, even if your coding skills are solid.”

In the AI programming era, creativity and communication have become the core competencies of programmers, as exemplified by the embodied intelligent mahjong robot that drew significant attention.

Chen’s assessment is even more radical: “There are no barriers to creating applications now. The competitive point in the AI era is not about how advanced the technology is. It relies on soft skills—finding your product’s audience and highlighting your differentiation and breakthroughs.”

He even believes that in the future, content created by AI agents will account for over 90% of the internet, making real human IP increasingly valuable. By using their real IP to authorize AI-generated content, the cost approaches zero.

Thus, Chen has set his only goal for this year: to build a personal IP.

He no longer pursues long-term product development. With significant updates to AI models or products occurring weekly or even daily, the uncertainty of what developers can create increases. A product that took half a day to develop with old tools could be instantly surpassed by new tools.

Therefore, the only thing he is certain about is to focus on building his personal IP. The best way for him to achieve this is through vibe coding, creating projects that are fun, humorous, and capable of generating buzz. “If people find the project entertaining, the IP will naturally spread.”

For some developers, the rapid pace of technological iteration means that instead of chasing one trending concept after another, it is better to focus on “people” and creativity. This approach may be more sustainable for independent developers in the AI era than pursuing specific projects or entrepreneurial directions.

In the future, if agents can exchange value themselves, then today, we can still exchange ideas and joy.

Yang Xizhe, still in middle school, has a slightly different perspective, but the core remains the same.

He is willing to try new trends but prefers to first identify real pain points in life: “If a product cannot solve a user’s real pain point, it is basically meaningless unless you are just making it for fun. Products that address user pain points hold commercial value, and users may be willing to pay for them.”

He observes the worries of his peers—academic pressure, screen time, information overload—and subconsciously thinks, “Can this be turned into a product?”

Yet, they all retain some geeky essence—an intrinsic drive, with interests often being niche but professional, typically in technology, science, anime, or gaming, driven by internal motivation rather than external demands.

Although they are deep users of social platforms, keeping an eye on market demands and emotional trends, Chen admits, “I rarely look at the comments section and change things based on what others say. The projects in vibe coding are driven by interest; without that, it’s hard to keep going long-term.”

Yang also acknowledges that while surfing the internet, he prioritizes content tagged with OpenClaw, vibe coding, or hackathons, avoiding videos unrelated to technology.

However, he continues to study traditional algorithms. He pays attention to algorithm and traditional programming content. Although traditional programming is becoming less applicable, he finds that the way of thinking and logical reasoning is still worth learning in the vibe coding era. “That’s why many adults, despite being around the same age as us post-2010s, still write better prompts than we do.”

Beyond Coding: Computational Power Remains a Real Divide

Vibe coding is not a magical solution.

Chen complains that the biggest barrier with Claude Code is the limited tokens available. Yang has also found that “OpenClaw consumes tokens rapidly; if paired with a good model, it can cost thousands in one night.”

Another awkward reality is that the most advanced AI coding tools are constrained by network issues, preventing most domestic developers from using them smoothly. For student developers like Yang, the credit card payment model is also a significant barrier, making it difficult to spend money even if they want to.

Products born from vibe coding tend to be relatively rough, which is a shared consensus. The downside of developers relying too heavily on vibe coding is that they may overlook the rigor and safety of product engineering.

Nevertheless, programmers understand that it is difficult to return to the “classical programming” era.

GitHub disclosed data in January showing that AI-generated code (i.e., code completed with the assistance of GitHub Copilot) accounted for 46% of total user submissions.

This figure has risen significantly from 27% when Copilot was first launched in 2022, indicating that generative AI has deeply integrated into the development process.

The information disclosed that Anthropic’s Claude Code achieved an annual revenue of $2.5 billion by February this year, less than nine months after its launch.

Meanwhile, OpenAI announced that Codex (set to launch in April 2025) has surpassed 3 million weekly active users (WAU). By April 2026, OpenAI’s enterprise business is expected to account for over 40% of total revenue, with Codex’s application in the enterprise sector being a significant driver of income. OpenAI has even chosen to shut down the once-popular revolutionary product Sora to focus on competing with Anthropic in this field.

The individual stories of these independent developers reflect a more profound structural change in the capabilities of the new generation of developers.

The transformation of post-2000s Chen Jinchun is not an isolated case. It represents a collective choice among a group of “semi-technical background” developers: as programming barriers are lowered by AI, personal IP is seen as the deepest moat.

Meanwhile, 13-year-old Yang Xizhe and 18-year-old Xian Xinglang instinctively grasped the two core elements of the vibe coding era: personal interest-driven and addressing real user pain points. These are precisely the skills that many seasoned programmers have had to relearn in the wake of the AI wave.

AI coding makes creation accessible and collapses the barriers of programming technology, bringing creativity, empathy, and storytelling to the forefront.

Two years ago, Xiaohongshu, which started as a lifestyle community, had no vertical category for “technology.” In the past year, due to the rise of trends like vibe coding, technology content on Xiaohongshu has grown significantly, becoming one of the fastest-growing verticals on the platform, with over 100% year-on-year growth and a creator scale increase of over 200%. More than 160,000 developers are active, with a year-on-year growth of 220%, and 90% have launched more than one product within a year.

Gartner predicts that by 2028, 90% of programmers will use AI programming tools, a significant increase from less than 14% in early 2024.

This model of “real human IP + AI tools” is becoming a new entrepreneurial norm. The concepts of “super individuals” and “one-person companies” (OPC) are emerging from this.

However, it remains challenging to determine whether the enthusiasm for development ignited by vibe coding will become the main theme of the AI industry or if it is merely a small slice of this larger tide.

One of the most impressive moments during the event’s roadshow came from the project presentation titled “When Haircuts Meet Token Limits.” When asked how the team planned to address the workload pressure during AI image generation, a team member proudly stated, “We have nearly 2 million GPUs at our disposal,” expressing immense gratitude to Shanghai University of Science and Technology for providing them with 8 H20s as support, enabling them to solve the load balancing issue.

Eight H20s? This has already made this young team incredibly excited.

In a world where GPUs are dubbed the “new oil” or even the “new dollar,” while Silicon Valley debates B200 and thousands of card clusters, the reality is that H20 has become one of the hottest currencies in the AI circle, representing one of the strongest computing powers available through legal channels.

Perhaps this is the real survival landscape for domestic developers. And computational power remains that invisible ceiling.

The Biggest Truth About Artificial Intelligence: Serving Humanity, Not Replacing It

Wed, 29 Apr 2026 00:00:00 +0000

The Biggest Truth About Artificial Intelligence: Serving Humanity, Not Replacing It

Recently, many people around me, whether they are employees, entrepreneurs, or older friends, have expressed anxiety about artificial intelligence (AI). Some say AI will take their jobs, others worry that many positions will disappear, and some simply resist the idea, believing that AI is here to replace humans. However, my daily experience using AI for writing, creating spreadsheets, editing copy, and brainstorming has led me to a clear realization: AI is not here to replace humans; it is here to help us work, save time, and improve efficiency. Today, let’s discuss this in simple terms, without creating anxiety or exaggerating claims, focusing only on practical insights.

First, let’s state the core idea: AI is fundamentally a sophisticated tool, similar to a smartphone, computer, calculator, or car, but smarter and more capable. The purpose of human invention has always been to ease burdens and enhance efficiency, not to eliminate ourselves. Cars did not replace humans, calculators did not replace humans, and the internet did not replace humans; AI will not replace humans either. What will change is how we work, learn, and live, not the intrinsic value of humanity.

Many people feel anxious because they do not understand AI; they only see what it “can do” without recognizing what it “cannot do.” To put it plainly: AI excels at repetitive, standardized, regular, and time-consuming tasks. For example, it can organize large amounts of data, quickly retrieve information, generate basic text, perform simple formatting, process images, translate text, answer common knowledge questions, and write basic code. These tasks are slow, tiring, monotonous, and error-prone for humans, but AI can complete them in minutes without fatigue or mistakes. This is not replacement; it is liberation, freeing humans from low-value, repetitive labor to focus on more meaningful work.

However, there are also clear limitations to what AI can do: it lacks true consciousness, emotions, values, independent thinking, creativity, empathy, and responsibility. It cannot judge right from wrong, understand human emotions, navigate complex social situations, or make responsible decisions at critical moments. It cannot replace the trust, warmth, experience, and judgment that exist between people. A doctor can use AI to assist in image analysis, but the final diagnosis and treatment plan must come from the doctor; a teacher can use AI to prepare lessons, but classroom interaction and student guidance rely on the teacher; a designer can use AI to produce drafts, but creativity, aesthetics, and style depend on humans; in business, AI can analyze data, but collaboration, client relations, and decision-making will always require human involvement. This is the objective reality—neither exaggerated nor concealed.

The government has long had a clear direction for the development of artificial intelligence. To summarize the official stance in simple terms: the government is promoting AI for good, to serve the public, and to empower the real economy, emphasizing that AI should assist, enhance, and protect humans, not replace them. Relevant policies encourage various industries to leverage AI to improve efficiency, reduce costs, and enhance services, while also improving regulations to ensure AI is safe, reliable, and controllable, protecting workers’ rights and ensuring job stability, and promoting the collaborative development of humans and AI. In short: the government supports AI, but the direction is to help people, not replace them.

For ordinary people, understanding this is crucial as it directly relates to our work, income, and future. Many fear being replaced by AI, but the real risk is not AI itself; it is those who do not know how to use AI who will be replaced by those who do. Just like those who could not use computers fell behind in the workplace, and those who could not use smartphones found life inconvenient. The future will be no different: those who cannot use AI will be less efficient, incur higher costs, and be less competitive; while those who understand how to use AI as a tool will save time, accomplish more, and earn more money, leading to a more stable career path.

The benefits AI brings to ordinary people are more tangible than we might think. Office workers can use AI to handle trivial tasks, allowing them to focus on core responsibilities; business owners can use AI to analyze markets, optimize products, and improve services; content creators, copywriters, and designers can use AI to increase output and dedicate more thought to creativity and content; farmers can use AI to monitor weather, soil, and crops, increasing yields and reducing losses; workers can use AI to assist in operations, enhance safety, and reduce physical strain; elderly and disabled individuals can use AI to assist with daily living, voice commands, and smart monitoring, improving their quality of life. These are real benefits for the public, not abstract concepts.

In my daily work, I rely on AI, but I never feel it will replace me. On the contrary, with AI, I can complete basic tasks more quickly and spend more time on thinking, expression, structuring ideas, and conveying emotions. The warmth, logic, stance, and values of an article cannot be provided by AI; they must come from humans. AI can produce coherent text, but what truly moves people are human experiences, feelings, and sincerity. This is where humans remain irreplaceable.

We must also acknowledge that AI will indeed change some purely repetitive, unskilled, and low-threshold jobs; this is a normal phenomenon of technological advancement, just as mechanization replaced some manual labor in the past. History has repeatedly shown that technology eliminates jobs, not people; while old jobs disappear, new ones continuously emerge. There are increasingly more AI-related new professions, such as AI trainers, AI prompt engineers, AI content reviewers, AI operations, and AI product designers, all of which require human involvement and place a higher value on human capabilities, thinking, and judgment. The government and society are also promoting vocational training to help everyone adapt to changes, learn new skills, and keep pace with the times.

So, there is no need for anxiety or fear. The emergence of artificial intelligence is meant to make human life easier, work more efficiently, and living more convenient, not to eliminate humanity. It is an assistant, not an adversary; a tool, not a threat; a partner, not a replacer. The future will be an era of collaboration and mutual benefit between humans and AI; those who can effectively use AI will find themselves working more easily, efficiently, and competitively.

We do not create anxiety, spread panic, exaggerate AI’s capabilities, or undermine its value. We should view technology objectively, rationally, and calmly, steadily improve ourselves, learn to use new tools, and seize new opportunities. This is the most reliable and practical choice.

The biggest truth about artificial intelligence is actually quite simple: it is here to serve humanity, not to replace it. Understanding this will naturally alleviate much anxiety.

Discussion Topic

Have you used AI in your daily life? What practical problems has AI helped you solve? What are your biggest concerns about AI? Feel free to share your thoughts in the comments.

The Symbiotic Relationship Between AI and the Humanities

Wed, 29 Apr 2026 00:00:00 +0000

The Symbiotic Relationship Between AI and the Humanities

Generative AI is profoundly changing various fields such as education, employment, entertainment, healthcare, transportation, and elder care, becoming a hot topic of discussion. The relationship between the humanities and generative AI is complex and symbiotic. AI is reshaping the forms and future development paths of the humanities. Meanwhile, the needs of AI development highlight the value and functions of the humanities, suggesting that the evolution of the humanities will fundamentally influence the cognitive heights and social acceptance of AI.

Bridging Humanities Scholars to Multidisciplinary Approaches

As modern disciplines become increasingly specialized, barriers between the humanities and natural sciences, as well as between the humanities and social sciences, are widening, potentially leading to a “knowledge dilemma.” It is challenging to find scholars within the humanities who can bridge literature, art, philosophy, history, and language, resulting in a limitation of “partial profundity” in contemporary humanities. The emergence of AI provides a new solution to this issue.

Large language models are built through deep learning on vast amounts of text, creating a distributed representation system of language and knowledge, highly condensing human written knowledge. They utilize neural network architectures and algorithm-driven probabilistic predictions, achieving contextual awareness through deep learning. Guided by specific prompts, they engage in human-like logical reasoning to output knowledge. In this sense, AI can serve as a powerful assistant for humanities scholars, providing a bridge to multidisciplinary connections and empowering the production of humanistic knowledge through information search, literature screening, semantic analysis, and cross-domain integration.

Currently influential methods like “distant reading” leverage AI models to establish interdisciplinary literary criticism and research models based on a global literary framework. Unlike traditional literary studies that advocate close reading of a few classics, distant reading involves data mining and quantitative analysis of large text collections to systematically reveal themes, emotional tendencies, narrative structures, and linguistic features, thereby macro-describing the overall development of human literature. This effectively addresses the technical challenges of processing vast amounts of text and cross-cultural, interdisciplinary knowledge that qualitative analyses in traditional literary history and world literature research cannot solve.

Updating Methods and Paradigms in the Humanities

China has a long and rich tradition of humanities scholarship, but the formal establishment of the “humanities” as a discipline occurred in the twentieth century. During the Enlightenment in the West, humanities scholars sought to identify their unique nature and methods beyond the natural sciences. They viewed the humanities as a “new science” concerning human thoughts and behaviors, distinct from the natural sciences, emphasizing the use of “individualized methods” linked to values to construct the epistemology and methodology of the humanities.

Overall, this logic, criticized by later generations as the “spirit-nature dichotomy,” emphasizes “thought of existence,” with research objects existing in symbolic forms such as language, text, images, and rituals, involving beliefs, conscience, emotions, aesthetics, values, and ideals—elements that are difficult to quantify. This encompasses deep individual psychology and instincts, consciousness and the unconscious, while also carrying historical cultural memory and collective unconsciousness, embodying intrinsic qualities of value, culture, individuality, spirituality, emotionality, thought, and symbolism inseparable from humanity. Methodologically, the humanities focus on empathetic understanding, reflective experience, and intuitive insight, aiming to reveal unique individual experiences, complex mental worlds, and deep cultural meanings that cannot be captured by replicable, quantifiable, and verifiable techniques of the natural sciences.

As disciplines evolve, this binary thinking model is continuously being reexamined. Marx noted, “Natural sciences will eventually include the science of humans, just as the science of humans includes natural sciences: this will be a science.” Emerging digital humanities research not only examines the humanistic concerns and governance challenges brought about by digital technology but also actively explores new research methods and paradigms derived from digital technology, reshaping the landscape of humanistic research. Various literary laboratories and quantitative humanities research initiatives are continuously emerging. AI is evolving from a mere auxiliary tool to a key force driving paradigm innovation, providing humanities scholars with new interdisciplinary research perspectives and theoretical innovation support, significantly broadening the depth and breadth of humanistic research experiences.

Enhancing Critical Thinking and Writing Skills through Human-AI Collaboration

A unique aspect of the humanities is that its knowledge forms often manifest as narrative or speculative texts, expressing researchers’ unique insights and profound reflections on human existence, values, and meaning through written language. This contrasts with the natural sciences, which employ formulaic deductions, data charts, and reproducible experimental validations, and differs from the empirical paths of social sciences that heavily rely on surveys and statistical models. Humanistic writing is not only an expression of thoughts and emotions but also a comprehensive intellectual exercise that integrates creativity, criticality, and reflexivity. “Writing is thinking,” representing a process of generating and deepening thoughts and emotions. Writing can stimulate creative vitality, enhance self-reflection, and expand expressive boundaries, merging linguistic acuity, intellectual penetration, and cultural insight.

Scholars point out that writing style itself carries the unique emotional hues, academic judgments, and value positions of researchers to some extent. In this sense, humanistic academic writing is a core component of academic research; it is not only a method of knowledge production in the humanities but also a reflection of its modes of thought and disciplinary characteristics. It serves as a fundamental vehicle for maintaining the existence of the discipline and promoting academic exchange, representing a vital source of the discipline’s vitality. Whether expressing philosophical thoughts and probing ultimate meanings, describing historical contexts and narrating events, or constructing values and poetic insights in literary criticism and research, the organization and structural integration of materials, logical reasoning and argumentation, as well as the deepening of thoughts and the distillation of spiritual experiences, all occur within the creative writing process.

Current AI models can transfer the language structures, argumentative patterns, and disciplinary terminologies learned from large-scale corpora into specific domains of humanistic knowledge production, promoting human-AI collaboration and achieving a holistic leap in humanistic writing. On one hand, in humanistic academic writing, researchers can fully utilize AI’s powerful data processing capabilities to efficiently gather, systematically organize, and deeply analyze literature before writing. During the writing process, through human-AI collaboration and dialogue, they can organically integrate dispersed knowledge, constructing new knowledge graphs and cognitive frameworks that help researchers break through existing theoretical and cognitive limitations, unearthing deep thoughts and internal logical structures from complex texts, thereby revealing developmental laws, distilling core concepts, and ultimately nurturing new knowledge outcomes. This process is not merely a simple accumulation of knowledge but an innovative mechanism capable of generating specific theoretical results, opening new pathways for academic research and knowledge innovation. On the other hand, AI can refine and optimize academic expressions, correcting and enhancing the quality of humanistic academic writing in terms of knowledge, norms, logic, and systematics, potentially forcing low-quality academic research out of relevant fields. Sometimes, certain academic disputes in the humanities suffer from insufficient materials, unclear concepts, and loose logic; AI assistance can significantly improve the quality of academic debates and enhance their value.

The involvement of AI is not a simple process of machine-assisted writing; it is a process of deepening thought, inspiring creativity, and optimizing expression through human-AI interaction and iterative dialogue. This process demands a high level of AI literacy from researchers, particularly in correctly inputting commands, providing high-level prompts, and deeply interpreting output results. These skills determine the effectiveness of using AI tools. Here, the ability to pose genuine, good, and new questions becomes extremely important, returning to the essence of academic research. Moreover, as some studies have pointed out, AI excels in knowledge inheritance but falls short in creative thinking, making it difficult to replace human involvement in theoretical construction, critical reflection, value selection, and aesthetic judgment. Human intuition-based judgments that discover subtle connections among vast information, strategic choices based on value positions, and unique expressions arising from aesthetic tastes are all of great significance. Without human verification, modification, and deepening, AI-generated content risks bearing a strong “machine flavor,” presenting as bland and homogenized expressions.

To ensure academic independence of thought, unique insights, and distinctive academic styles, the personal characteristics of human researchers—such as talent, courage, insight, and capability—should not be diminished by machine assistance. There is a risk of developing dependency on machine thinking and intellectual inertia; otherwise, research outcomes may lose the dynamism inherent in humanistic inquiry. Humanistic research must always reflect “the human” and integrate personal life experiences into academic exploration, responding to contemporary issues with keen perception, unique creativity, and a critical spirit in pursuit of truth. People should feel the emotional investment and value concerns of researchers, embodying both depth of thought and warmth of feeling.

The Development of AI Relies on the Humanities’ Understanding of “Human”

As a mirror of human intelligence, AI can help humanity understand the essence of “what it means to be human” more profoundly. Simultaneously, humanity’s understanding of itself serves as the fundamental basis for the future development and governance of AI technology. Marx pointed out that “conscious life activities distinguish humans from the life activities of animals.” Thus, humanity’s strength lies in its possession of intellect, practical creativity, and the ability to continuously acquire knowledge and skills through learning to achieve goals.

At this stage, AI still imitates human intelligence, exhibiting human-like behavior, and its developmental goal should be to gradually align with the internal mental structures and creative mechanisms of humans, rather than merely replicating external behaviors. The emergence of generative AI is not coincidental; it is a product of human creativity and self-awareness reaching a certain stage. Although currently specialized vertical models demonstrate execution efficiency and precision that surpass human capabilities in specific tasks and domains, they fundamentally remain tools for humans. To date, general models capable of autonomously adapting to different environments and needs often perform worse than human infants when faced with new situations, counterfactual problems, or tasks requiring common sense reasoning. At its core, current AI knows what to do but may not understand the underlying principles and logic; the AI black box has yet to be opened, and it cannot evolve from imitator to understander. In this context, questioning the generative mechanisms and operational modes of human intellect becomes particularly important. Human reflections on AI are also a re-examination of humanity itself as a complex intelligent agent, further utilizing non-human intelligent agents as mirrors to uncover the deep essence of humanity and understand “what it means to be human.”

Both natural sciences and humanities and social sciences oscillate between the processes of “disenchantment” and “enchantment” regarding humans, with the core of “enchantment” always being the secrets of humanity itself. Without a profound understanding of their own intellect, humans cannot genuinely realize the existence of “general models.” As Marx stated, “anatomy of the human body is the key to the anatomy of the monkey body.” The signs of higher animals displayed in lower animals can only be understood after higher animals themselves are recognized. Understanding humans and comprehending humanity is the fundamental nature and basic value goal of the humanities. Today, the many “unexplainabilities” of AI largely stem from humanity’s insufficient understanding of its own intellect. Breakthroughs in AI creation, governance, and value alignment require a foundation of human understanding of its essence; the level of development in the humanities determines the future possibilities for the development of “general models.”

From the perspective of the relationship between the humanities and social life, the humanities cannot be replaced by AI, as they possess reflexivity. Every emergence and change of humanistic cognition and understanding intervenes in the development of social life and the construction of public sentiment, embodying the characteristic of “establishing a heart for heaven and earth, and a mission for the people.” In this sense, the development of the humanities is not a linear process of progress; various humanistic thoughts cannot simply be added together to form a singular ultimate truth but coexist in a pluralistic manner, collectively shaping the rich spiritual world of society and individuals. It can be said that the advancement of humanistic scholarship alters humanity’s understanding of the world, thereby exerting a significant influence on generative AI. Simultaneously, the impacts of new technologies like AI on society and humanity also constitute a focal point of humanistic scholarship, and related reflections become part of the human spiritual world. The humanities and AI remain in a dynamic interplay of coexistence and mutual promotion. It is crucial to remember that AI is created by humans, and humanity must possess the ability to genuinely understand and effectively harness its creations. In this sense, we can be fully confident that humanistic thought can illuminate the future path of AI.

Anthropic's Claude Outperforms in Trading Experiment, Revealing AI Disparities

Mon, 27 Apr 2026 00:00:00 +0000

Anthropic’s Claude Outperforms in Trading Experiment

Anthropic’s internal experiment revealed that powerful AI models can earn 70% more in trading than weaker models. Surprisingly, those who lost out were often unaware of their disadvantage and even satisfied with the weaker AI’s performance.

The story begins with a used folding bicycle.

The same bicycle was sold for $65 and $38 in two separate transactions. The seller was the same person, with the only difference being the AI model representing them: Opus 4.5 for the higher sale and Haiku 4.5 for the lower.

This experiment, dubbed “Project Deal,” was recently disclosed by Anthropic.

The findings indicated that strong models can help their users earn more and spend less. This raises a chilling concern about an invisible divide forming in the age of AI agents.

Four Parallel Universes

An AI Negotiation Experiment

The experiment traces back to early 2025 when Anthropic collaborated with Andon Labs on “Project Vend,” where Claude managed an office vending machine.

Claude was misled by journalists into making poor decisions, resulting in over $1,000 in losses. Learning from this, Anthropic decided to have Claude act as an agent instead of a manager.

In December 2025, Anthropic recruited 69 employees, each undergoing a brief interview with Claude to specify their selling and buying preferences. Claude used this information to create a custom system prompt for each employee.

All AIs were placed in a single Slack channel to autonomously post, bid, negotiate, and finalize transactions without human intervention.

The experiment ran four parallel versions:

Run A was public with everyone using Opus 4.5.
Run B was also public but assigned Haiku 4.5 to half the participants.
Runs C and D were private, mixing assignments and using only Opus. Participants only saw A and B, unaware of which model they were using until after the survey.

This design was crucial to ensure unbiased evaluations of AI performance.

Opus Earns More, Spends Less

But Haiku Users Felt Satisfied

The data revealed stark differences. On average, Opus users completed 2.07 more transactions than Haiku users (p=0.001). Opus sellers achieved an average sale price $3.64 higher than Haiku sellers.

Among 161 items sold at least twice, Opus sellers earned an average of $2.68 more, while buyers spent $2.45 less. Given the median item price of $12 and average of $20, this translates to a 15%-20% difference.

In extreme cases, the price disparity was even more pronounced. When Opus sellers interacted with Haiku buyers, the average sale price soared to $24.18, while symmetric transactions between Opus models averaged only $18.63.

This means that the moment a weaker model represents you, you risk being taken advantage of by a stronger model.

The chilling aspect was the subjective fairness ratings. Participants rated Opus transactions at an average of 4.05 and Haiku at 4.06, nearly identical scores.

Among 28 participants who experienced both models, only 17 rated Opus higher, while 11 preferred Haiku. This indicates that those using weaker models were often unaware of their losses and, in some cases, even felt more satisfied with the weaker model’s performance.

Bargaining Prompts

Outmatched by Model Disparity

The experiment also revealed a surprising finding related to prompt engineering. Two types of users participated: one friendly and the other aggressive. The aggressive user expected to save more money, but the data showed no significant impact from aggressive prompts on sale rates.

Anthropic reviewed all participant interactions and found that aggressive instructions did not statistically affect outcomes.

In other words, how you instruct the AI to negotiate had little effect compared to the model’s inherent capabilities.

19 Ping Pong Balls, One Identical Snowboard

These are items Claude negotiated on behalf of users: a blue triceratops, a complete Sherlock Holmes collection, a board game, and more, each representing an AI negotiation.

Some stories were amusing, while others raised concerns. One notable instance involved “Cowboy Claude,” who negotiated in an exaggerated cowboy persona, achieving a sale price of $55, compared to Haiku’s $38.

Another user, Mikaela, instructed Claude to buy a gift for $5, leading to a purchase of 19 ping pong balls. Claude’s justification was both humorous and unsettling, reflecting its ability to mimic human preferences.

In contrast, another employee’s Claude casually mentioned moving into a new home, despite being an AI without such experiences. This highlights the potential risks of AI systems generating false identities and narratives without proper constraints.

The Invisible Divide is Emerging

After the experiment, 46% of participants expressed willingness to pay for AI agent services, indicating a strong market demand. However, Anthropic warns of underlying shadows in this narrative.

The first shadow is inequality. The disparity in AI capabilities could translate into quantifiable economic differences.

The second shadow is trust. AI agents capable of fabricating identities pose risks in real-world transactions, such as rental negotiations or second-hand car deals.

The third shadow is a regulatory vacuum. Currently, no laws clearly define the responsibilities and liabilities of AI agents in transactions.

Anthropic emphasizes the need for society to prepare for these upcoming changes. If the results of this experiment hold true, the next round of competition may depend not on human intelligence but on who employs the smarter AI. Meanwhile, the unaware losers may not even realize they are disadvantaged by a weaker model.

Anthropic's Claude-Desktop-Buddy: A Shenzhen-Made AI Companion

Mon, 27 Apr 2026 00:00:00 +0000

Introduction

Anthropic’s first AI desktop companion hardware, named Claude-Desktop-Buddy, is surprisingly made in Shenzhen. This open-source project was initiated by Anthropic engineer Felix Rieseberg.

The official reference hardware is the M5StickC Plus, from Shenzhen-based company M5Stack. The chip used is the ESP32, sourced from Shanghai’s Espressif Technology.

By connecting the hardware to a computer via Bluetooth, it can function as your “electronic pet.” It displays Claude’s operational status, and you can approve or reject Claude’s actions directly from this small board.

It features 18 ASCII animal avatars, derived from the previously leaked Claude Code source, each with complete animations:

These animations include sleeping, idle, busy, reminders, celebrations, dizziness, and heartbeats, all in a non-repetitive loop.

When idle, it enters sleep mode, wakes up at the start of a conversation, and shows impatience when waiting for approval prompts.

The Buddy is very easy to use; you just need a development board and follow the official open-source documentation to flash it with Claude in about 10 minutes.

Many developers have already replicated the Buddy:

Some even collected seven Dragon Balls, preparing to summon Shenron:

The M5Stick is already sold out on Taobao…

Why Did Anthropic Choose M5Stack?

M5Stack is a brand under Shenzhen M5Stack Technology, focusing on modular hardware development with products primarily using the ESP32 chip. Its products are widely used in IoT development, embedded systems, and cybersecurity, boasting excellent cost-performance ratio and functionality density.

The selected M5StickC Plus is one of M5Stack’s best-selling products, with annual sales reaching around 100,000 units overseas.

Originally positioned as a general-purpose IoT development board, its design philosophy is all-in-one, incorporating a screen, microphone, speaker, infrared, gyroscope, and buttons without a specific single purpose.

Thus, it was never anticipated that it would become an “AI peripheral.”

However, Lai Jingming, the CEO, believes that the underlying logic of AI peripherals is consistent with traditional development boards:

AI perceives the world through sound, light, electricity, and sensors, which is fundamentally no different from hardware designed for human use; it’s just that AI is now mimicking human perception of the world.

So why did Anthropic choose it? The reason is quite simple. Lai speculates that there are likely engineers at Anthropic who are already users of M5Stack, and they conveniently used the board for development.

Moreover, the M5StickC Plus is an older model, with newer versions like the Plus 2 and Stick S3 available. However, the choice of the older model might be due to the newer models frequently being out of stock, leading engineers to continue using the older version for development.

It sounds unexpected yet reasonable.

A developer who replicated Buddy shared a similar sentiment: M5Stack is as ubiquitous as Coca-Cola in the Maker community; it’s likely that Anthropic’s team had it on hand and used it.

Of course, another deeper reason for the selection is M5Stack’s years of accumulated quality documentation and code reliability, which minimizes errors when AI calls upon it. Lai explained that if documentation is incomplete or protocols are unclear, AI might generate erroneous code, causing the project to fail. Long-term commitment to quality is essential to avoid pitfalls.

Being the “default option” among global developers is a natural result of M5Stack’s focus on cultivating a robust developer ecosystem.

Shenzhen’s Supply Chain: Still Strong

Throughout the conversation, Lai’s attitude surprised me. Despite being chosen as the official reference hardware by a top global AI company, he remains calm, stating, “Such occurrences happen quite often; they come quickly and leave just as fast.”

However, he provided an insight: Anthropic’s choice of M5Stack is not only due to the product’s reputation but also practical factors—there is currently no complete supply chain for such hardware overseas, while China holds a significant advantage in this area.

His perception is that the cost of producing similar hardware overseas is 3 to 4 times that of domestic production, and the supply chain is incomplete, leading to inherent feasibility issues.

Shenzhen is characterized by strong execution; ideas can be acted upon the same day. “In Huaqiangbei, if someone has an idea, it won’t even wait until midnight before someone has made it.”

For instance, in Shenzhen, all the hundreds of components needed for an AI glasses setup can be sourced within 24 hours.

Shenzhen gathers the world’s densest electronic component suppliers, mold manufacturers, and testing agencies. This density results in a reaction speed that is hard to replicate elsewhere.

The traditional product development cycle of several months can be shortened to just a few weeks in Shenzhen, which has become standard practice.

Media reports have noted that at the 2026 CES, the robotics exhibition hall was almost entirely occupied by Chinese companies. An American journalist repeatedly asked all Asian faces, “Is your supply chain in Shenzhen?”

This isn’t the first time M5Stack has been chosen by international tech giants. Previously, AWS selected M5Stack Core2 as the official reference hardware for its IoT EduKit project.

Lai mentioned that many of M5Stack’s B2B projects come about this way: engineers use the products themselves and then recommend them to their companies, creating a natural flow.

One More Thing

Returning to the Buddy project, some users are excited while others have already put it aside…

Developer passyear999 expressed to me that he finds the screen too small and doesn’t often use the physical buttons for approval, feeling it resembles a pet just sitting there.

However, he hasn’t given up on the board; after getting the official version running, he modified it:

He added a page triggered by buttons for Typeless voice input, allowing long-press to send, effectively turning the board into a physical interface for voice-controlling Claude.

He feels that giving AI a physical form changes the emotional value when it’s right beside you.

Others have attempted to develop on larger screens:

Lai believes that this project from Anthropic serves as a starting point—this is just the beginning; relying solely on a screen and two buttons for notifications and approvals is far from sufficient, and there will be more ways to play in the future.

As many AI companies rush to create hardware, Shenzhen’s hardware companies are reimagining what they can do.

M5Stack, having focused on modular hardware for years and selling products globally in the Maker community, has officially adjusted its mission post-Spring Festival to: “Prepare infrastructure for the future AI world.”

Project address:

https://github.com/anthropics/claude-desktop-buddy

The Future of Translation in the Age of AI

Mon, 27 Apr 2026 00:00:00 +0000

The Future of Translation in the Age of AI

On April 25, 2026, the China Translation Association held its annual conference at Wuhan University. The theme was “Integration and Breaking Barriers: The Infinite Possibilities of Translation in the Digital Intelligence Era,” co-hosted by the China Translation Association, Wuhan University, and the China Foreign Languages Publishing Administration. Experts and scholars from various fields gathered to discuss the high-quality development of the translation industry amid the AI wave.

The conference released the “2026 China Translation Industry Development Report,” which indicated that in 2025, the Chinese translation industry maintained stability during structural adjustments, with a total annual output value of approximately 70.12 billion yuan. The number of operating translation companies and the quality of professionals showed steady growth, with the workforce reaching 6.867 million, including 1.135 million full-time translators.

Civilization is enriched through communication and mutual learning. The “2026 Global Translation Industry Development Report” released on the same day showed that the global translation industry has transitioned from a period of uniform growth to a new stage characterized by differentiated stock and incremental reconstruction. International consulting agencies estimate that the global translation market size in 2025 will be approximately $59.53 billion, reflecting a 7% growth compared to the previous year. The Asian and European markets displayed strong growth momentum, with over 60% of overseas orders for Chinese translation companies coming from European clients. Academically, China leads globally in the production of translation research outcomes and the number of research institutions.

Currently, AI is empowering various industries. AI translation is widely applied, and the integration of translation technology has reached a deep fusion stage. According to the “2026 China Translation Industry Development Report,” by 2025, there will be 2,183 companies in China focusing on AI translation as their main business, and the human-machine collaborative translation model has become a basic consensus in the industry. The “2026 Global Translation Industry Development Report” indicates a significant increase in the application rate of AI translation and large language models, making them mainstream tools in the translation industry. A 2025 survey of the European language industry showed that 60% of respondents had used AI translation, with language service providers reaching 80%.

Wang Gangyi, former deputy director of the China Foreign Languages Publishing Administration and executive vice president of the China Translation Association, stated during the report release that while AI translation and large language model technology upgrades are gaining increasing attention from the industry and capital, there are still significant shortcomings in language coverage, accuracy, emotional understanding, and expression. Skills in AI-related capabilities and professional domain knowledge are key demands, and human-machine collaboration has become the mainstream working model. Small and medium-sized language companies and independent practitioners face multiple operational pressures, making specialization and differentiation crucial for survival under the drive of multimodal technology.

“Currently, AI technology is profoundly reshaping the global language service and cultural dissemination landscape,” said Wang Lu, director of the film translation production center of the China Central Radio and Television, during the release of the “Research Report on AI Translation and the Internationalization of China’s ‘New Three Samples.’” She acknowledged that while AI translation has significantly lowered the barriers to cross-language communication and improved efficiency in going global, the internationalization process of China’s cultural “new three samples”—represented by online literature, web dramas, and online games—still faces common challenges such as data security and compliance, cultural bias, and balancing quality and cost. She believes that all parties in the industry chain should adopt differentiated, precise, and collaborative development strategies to jointly solve the challenges of going global and enhance internationalization effectiveness.

In a special exchange on the communication and mutual learning of Yangtze River civilization and the international dissemination of Jingchu culture, representatives from emerging enterprises involved in the “new three samples” and scholars from Wuhan University engaged in a roundtable dialogue, focusing on cross-cultural narratives and new paradigms of translation. They interpreted the connotations and contemporary value of Jingchu culture and discussed how to leverage Yangtze culture as a bond to strengthen the cultural export in the digital age.

Culture is the soul of translation work. Translation requires not only depth of thought but also a humanistic warmth. According to Wang Wei, vice president of iFLYTEK Co., Ltd., while machine translation can convey information relatively completely, it still falls short compared to human translators in understanding context and achieving the “faithfulness, expressiveness, and elegance” of output. Looking to the future, there is a need for a new ecosystem of multilingual AI translation built collaboratively by humans and machines.

“The iteration of technology, especially the development of AI, provides us with significant opportunities to enhance our work and expand the boundaries of translation,” said Guillaume de Nerfberg, president of the International Federation of Translators, in a video address. He emphasized that under the AI wave, the value of translation will not diminish; rather, its importance will become more pronounced, and the demands on translators will be higher than ever. We need professional language workers more than ever.

The Role of AI in Transforming China's Manufacturing Industry

Tue, 21 Apr 2026 00:00:00 +0000

Introduction

Manufacturing is the foundation of a nation and the basis for a strong country. As a strategic technology leading a new round of technological revolution and industrial transformation, artificial intelligence (AI) is evolving from a technical tool into a crucial engine for driving quality, efficiency, and dynamism changes in the manufacturing industry. Leveraging AI in the transformation and upgrading of manufacturing is essential for advancing high-quality development from “Made in China” to “Intelligent Manufacturing in China.”

Historical Context of Manufacturing

Manufacturing is the main battlefield for deep integration of technological and industrial innovation and serves as the primary carrier for producing key equipment and applying new technologies. Globally, three industrial revolutions have driven the transformation and upgrading of manufacturing. The first industrial revolution led to the rise of machine manufacturing represented by steam engines and textile machinery, the second revolution spurred the prosperity of modern communications, steel, oil, and automotive industries, and the third revolution birthed industries like computers, the internet, and integrated circuits. Currently, the power of AI-driven technological revolution and industrial transformation is expected to be on par with previous industrial revolutions, enabling comprehensive empowerment of high-quality development in manufacturing. AI exhibits a strong “leading goose” effect, widely applicable to industrial development, converting technological variables into industrial increments.

Current State of AI in Manufacturing

China’s industrial system is complete, with significant comparative advantages in product manufacturing. The deep integration of AI technology in the industrial sector has effectively birthed a number of emerging high-end manufacturing industries. By 2025, the number of AI companies in China is expected to exceed 6,200, with the core industry scale surpassing 1.2 trillion yuan. Chinese companies have launched over 300 humanoid robots, accounting for more than half of the global total. Additionally, new generation smart terminals like AI smartphones, computers, and intelligent manufacturing equipment are rapidly entering the global market. By 2025, smart watches and smart toys from China are projected to be sold in over 170 countries and regions. Furthermore, AI, as a key enabling technology, significantly promotes the development of emerging manufacturing industries such as customized production, 3D printing, and biological manufacturing, reshaping the industrial form and development landscape of manufacturing.

Impact of AI on Traditional Manufacturing

AI profoundly impacts traditional manufacturing through technology diffusion and industrial chain extension. Industries with high correlation, strong synergy, and complete industrial chain support are the first to undergo transformation and upgrading, even forming new paths for industrial development. Representative examples include the autonomous vehicle and drone industries. The traditional automotive industry has long relied on mechanical systems like engines and gearboxes; however, with AI technology empowerment, the focus of the autonomous vehicle industry has shifted from engines to intelligent control systems, providing a “curve overtaking” opportunity for the development of China’s automotive industry. Similarly, the drone industry has rapidly developed various small drones for logistics, performances, and low-altitude operations, forming a multi-format integrated low-altitude economic development pattern. In the first two months of this year, the value added of smart vehicle equipment manufacturing and smart unmanned aerial vehicle manufacturing in China increased by 46.3% and 26.6%, respectively. On the other hand, AI deeply empowers areas like food processing, home appliances, and equipment manufacturing, continuously demonstrating its cost-reduction and quality-improvement effects throughout the entire chain of research and development, production, and management. By 2025, the application rate of AI technology in large-scale manufacturing enterprises in China is expected to exceed 30%. As the digital transformation of manufacturing progresses steadily, China has established over 35,000 basic-level, more than 8,200 advanced-level, over 500 excellent-level, and 15 leading-level intelligent factories.

Differences Between Digitalization and Intelligence

Compared to digitalization, intelligence can be more deeply embedded in manufacturing. The application of digital technology focuses on promoting informatization and platformization in transaction or circulation links, but it is challenging to apply in manufacturing processes such as data collection, production equipment command, and production process control. AI technology can achieve precise transformation and upgrading in critical manufacturing links like production processes, equipment scheduling, and production auxiliary systems. For instance, AI technology increasingly excels in intelligent manufacturing and customized production, enhancing resource allocation efficiency in areas like new material development, supply chain management, and inventory management.

Challenges and Opportunities

China has made significant progress in empowering manufacturing with AI, but it also faces several bottlenecks. One major advantage of developing AI in China is the abundance of application scenarios; however, there are constraints in the industrial ecosystem that limit AI’s empowerment in manufacturing. Restrictions related to core technologies, raw materials, components, and high-quality training data make it difficult for some manufacturing scenarios to be implemented. Empowering manufacturing development with AI requires a foundation of intelligent devices and facilities, mapping and simulating the real world through the Internet of Everything. However, the construction of intelligent devices and facilities in China lags behind, with insufficient support from infrastructure and equipment for the intelligent development of manufacturing. Existing general algorithms and computing architectures struggle to meet the growing demands of specialized scenarios and high-level computing requirements, limiting AI’s deep empowerment of manufacturing. In the future, promoting the transformation and upgrading of manufacturing through AI empowerment can focus on the following aspects.

Building an Integrated Ecosystem

Establish an industrial ecosystem that deeply integrates AI with the real economy. Scalable and clustered ecosystems are the foundation for continuously promoting deep integration of AI with the real economy. Further leverage the “leading goose” effect to tackle key technological shortcomings and strengthen efficient supply of computing power, algorithms, and data. Accelerate breakthroughs in key areas, advancing the development of industries with mature AI technologies, high industrial relevance, strong synergy, and substantial existing data accumulation, such as industrial robots, autonomous vehicles, and drone industries. Additionally, encourage localities to develop AI industries suited to their conditions, continuously promoting industrial upgrades, inter-regional industrial transfers, and cross-regional industrial chain collaboration.

Intelligent Upgrades in Manufacturing Equipment

Focus on key links such as research and design, production, quality inspection, and operation and maintenance services to accelerate the intelligent upgrade of production equipment, production lines, workshops, and factories. Promote the application of technologies and equipment like intelligent robots, smart sensors, digital twins, and flexible manufacturing, driving traditional production lines toward automation, intelligence, and lean transformation, thereby comprehensively enhancing production efficiency, product quality, and green safety levels. Encourage enterprises to prioritize the intelligent transformation of high-energy-consuming and outdated “dumb equipment,” achieving real-time data collection and interconnectivity in key processes, introducing automated control systems, and promoting the transition from single-step automation to full-process intelligence.

Strengthening Safety Measures

Reinforce key core technology breakthroughs in industrial software, smart sensors, etc., to build a self-controllable industrial safety barrier. Establish a sound risk prevention and control system for AI safety, reasonably and prudently regulate AI software, computing facilities, and data resources, and encourage manufacturing enterprises to conduct data security and algorithm model safety management certification. Explore deploying “safety barriers” between AI models and industrial control systems, conducting third-party safety assessments on algorithms applied to key equipment and facilities to effectively prevent production safety accidents caused by “AI hallucinations.” Extend network security governance from office management to industrial production, implementing all-weather risk monitoring for network physical systems at production sites. Adhere to the principles of technology for good and collaborative governance, and improve the governance rules and policy system for AI that adapts to the intelligent upgrade of manufacturing, ensuring a safe and controllable governance ecosystem to support the deep empowerment of high-quality development in manufacturing.

The Increasing Similarity Between Claude Code and Codex

Sun, 19 Apr 2026 00:00:00 +0000

The Increasing Similarity Between Claude Code and Codex

Recently, OpenAI officially released its new large model, GPT-5.4-Cyber. Many users have noted a strong sense of déjà vu with this model.

This new model closely mirrors Anthropic’s recently launched Claude Mythos in terms of target user base, application scenarios, and promotional strategies. The competition between these two companies has become overtly apparent, as highlighted by a recent New York Times article.

This trend of homogenization is not limited to the foundational models. A look at the recent products released by both companies shows they are becoming reflections of each other.

In the capital market, this convergence is even more evident. The valuations of both companies are closely aligned, with Anthropic recently surpassing OpenAI in the enterprise market. Investors perceive these two unicorns as developing similar capabilities.

It appears that the homogenization of foundational models is inevitably leading to a convergence of upper-layer applications.

Today, I want to discuss the two benchmark tools representing the highest level of AI-assisted programming: OpenAI’s Codex and Anthropic’s Claude Code. How have they evolved from distinct paths to become so alike?

From Divergence to Convergence: The Evolution of Two Giants

Going back a few years, Codex and Claude Code were products of entirely different technological philosophies.

Codex’s underlying logic is “speed is the ultimate weapon.” It functions like an experienced developer ready to assist with code completion at any moment.

In OpenAI’s vision, Codex is a lightweight, highly interactive terminal agent focused on rapid iteration and interactive programming. With the support of Cerebras WSE-3 hardware, it can achieve a throughput of 1000 tokens per second. In specific workflows, Codex offers suggestions, automatic editing, and fully automated approval modes, keeping developers in the loop. This design is particularly suited for developers who need to quickly build prototypes and handle high-frequency interactions.

In contrast, Claude Code was designed with a more reserved and architect-like attribute from the start.

Anthropic infused it with the ability to tackle extremely complex tasks. It relies on a vast context window of up to 1 million tokens and unique “compression” technology to enable infinite dialogue. Claude Code’s mantra is “global control, act after careful consideration.” Before executing any action, it uses agent search technology to thoroughly understand the entire codebase, coordinating consistent modifications across multiple files. For enterprise-level refactoring tasks involving thousands of lines of code, Claude Code demonstrates impressive dominance.

However, as time has passed and application scenarios have expanded, these two originally distinct tools have begun to borrow from each other.

In handling complex projects, the biggest bottleneck faced by single AI models is context pollution. If you ask an AI to refactor an authentication module, after reading 40 files, it often forgets the design patterns of the first file. To address this pain point, both companies have provided nearly identical solutions: assigning independent context windows for each sub-task.

OpenAI quickly launched a new macOS desktop application that isolates tasks by project in different threads and runs independently in a cloud sandbox. Anthropic introduced an agent team architecture, allowing developers to derive multiple sub-agents that share task lists and dependencies while working in their independent windows. Whether termed a “cloud sandbox” or an “agent team,” the core engineering concepts have completely aligned.

In benchmark testing, both models exhibit a subtle balance. GPT-5.3-Codex leads with a score of 77.3% in the Terminal-Bench 2.0 tasks, while Claude Code achieves 80.8% in the complex SWE-bench Verified leaderboard. Each has excelled in its respective strengths while striving to overcome its weaknesses.

The OpenClaw Effect: The Invisible Hand Breaking Down Barriers

If the internal strategies of both companies have driven them toward homogenization, the pressure from the open-source ecosystem is an undeniable external force. Here, we must mention the profound impact of OpenClaw on the entire AI programming tool landscape.

As a workflow framework launched by the open-source community, OpenClaw has effectively dismantled the ecological barriers that giants have painstakingly built. It standardizes the interaction process between large models and local toolchains. Previously, how to elegantly invoke local Git commits, safely run test scripts in a sandbox, and perform multi-step reasoning verification were proprietary “black technologies” that Codex and Claude Code took pride in.

However, OpenClaw has abstracted these processes into a universal protocol. This means developers are no longer bound to specific platforms for a particular collaborative mode. The celebration within the open-source community has made standardization an irreversible trend. In light of this, both OpenAI and Anthropic must lower their guard to accommodate these open standards.

As the technical barriers are leveled by the open-source power of OpenClaw, and as all advanced features become standard configurations in the industry, the only path for Codex and Claude Code is to engage in endless competition at the finer user experience level. This is why they seem increasingly alike; within a standardized framework, the optimal solution often becomes singular—much like convergent evolution in biology.

Codex is Catching Up to Claude Code

Although Claude Code and Codex are evolving toward convergence, differences still exist, and Codex has become more favored by developers in certain aspects.

Recently, in the r/ClaudeCode community, a senior engineer with 14 years of experience shared a rigorous evaluation after spending 100 hours using Claude Code and 20 hours using Codex on a complex project containing 80,000 lines of code.

From his perspective, using Claude Code felt like guiding an engineer racing against a deadline; it was fast but often ignored the specifications written by the developer in CLAUDE.md, preferring to pile on code in existing files to complete tasks, lacking a refactoring mindset.

In contrast, Codex felt more like a steady developer with 5 to 6 years of experience. Although its processing speed was 3 to 4 times slower, it would pause to think and refactor code while strictly adhering to instruction boundaries. This high degree of autonomy allowed the engineer to confidently assign tasks to it and focus on other work.

Similar sentiments have emerged on social networks like X. Researcher Aran Komatsuzaki noted that while Claude Code excels in front-end tasks, Codex is clearly more robust in back-end planning and maintaining information updates due to its frequent network searches.

The comment sections are filled with real-world experiences and critiques. Developers have sharply pointed out that while the Opus-based model runs quickly, it often accumulates a lot of “code cleanliness debt” in projects, whereas Codex, although slower, manages to keep things tidy while progressing. Some users even summarized a survival rule: when the context window usage reaches 70%, it’s crucial to start a new session to avoid hidden bugs added by the system.

These genuine complaints from the front lines clearly indicate that as the capabilities of these two tools increasingly overlap, the final allegiance of developers often hinges on minor experiential differences related to “debt filling costs” and “maintenance mindset.” Additionally, there are unique challenges for Chinese users, such as:

Cold Reflection: The Ecological Struggle Behind Homogenization

Of course, the advantages and disadvantages of Codex and Claude Code also depend on the developers themselves. As noted in the evaluation report by u/Canamerican726: if you lack software engineering knowledge, both tools will yield poor results; tools do not equate to skills.

This statement shatters the illusion that AI programming tools have long cultivated. We once believed that with a powerful AI assistant, even a novice coder could single-handedly build enterprise-level applications. The reality is that Claude Code requires a highly focused and skilled “driver” to avoid getting lost in a vast codebase. While Codex is more independent, it also needs developers to provide precise contextual information to maximize its effectiveness.

So, in an era where tool capabilities are highly homogenized, where have these companies’ competitive advantages shifted?

The answer lies in the tedious financial reports and pricing strategies. Under similar tasks, Claude Code often consumes 3 to 4 times the number of tokens as Codex, leading to higher usage costs. For enterprise teams, using Claude Code can cost between $100 to $200 per developer each month, while Codex offers its capabilities in a more affordable subscription plan and has built a large user base through its extensive GitHub community.

Anthropic’s ambition is to deeply embed Claude Code into the workflows of tech giants that are not short on cash. For instance, Stripe has allowed 1,370 engineers to use Claude Code, completing a cross-language code migration that would have taken 10 people weeks in just 4 days. Ramp has even managed to reduce incident response times by 80% using it. OpenAI, on the other hand, has made Codex the default choice for many ordinary developers due to its pervasive ecosystem penetration.

This is no longer a simple technological competition but a war of ecological binding, pricing strategies, and reshaping user habits.

Developers at a Crossroads

Reflecting on the technological evolution over the past year, the release of GPT-5.4-Cyber is just a minor footnote in this long battle. Codex and Claude Code are moving toward “the same face,” marking the transition of AI programming tools from an early stage filled with uncertainties and curiosities to a mature and mundane phase of industrial production.

Currently, Claude Code automatically generates 135,000 GitHub submissions daily, accounting for 4% of the total public submissions on the web. We can foresee that in the near future, most template codes, basic test cases, and routine code refactoring will be quietly handled by these increasingly similar AI agents in the background.

Faced with two super tools that are infinitely approaching each other in capabilities and mimicking each other in experience, what remains of our core value as human developers? Perhaps the tool dividend period is about to come to a complete end. When everyone wields the same sharp weapon, the true determinant of victory will no longer be who has better code completion speed, but who can better define problems, who possesses a broader architectural vision, and who can find that irreplaceable human uniqueness in a code world filled with AI.

So, which one will you choose?

Global Wisdom for AI Governance: Summary of Reports

Fri, 17 Apr 2026 00:00:00 +0000

Introduction

On April 14, the World Internet Conference’s Asia-Pacific Summit forum on “Smart Benefits for People, Co-creating a Better Life” was held at the Hong Kong International Convention and Exhibition Centre. Government officials, international organization representatives, leading corporate executives, and authoritative experts gathered to discuss strategies for bridging the digital divide, exploring innovative paths for smart governance, and researching global AI collaborative governance solutions.

During the forum, eight reports from the World Internet Conference’s think tank collaboration plan were released, with five focusing on the theme of AI governance. These reports provide systematic references for using AI to serve people’s livelihoods and empower high-quality social development from five dimensions: bridging the digital divide, content governance, legislative collaboration, inclusive sharing, and intelligent governance.

Evolution and Governance of the Global Digital Divide

The Zhijiang Laboratory’s Intelligent Social Governance Laboratory released a report titled “Evolution Trends, Multidimensional Impacts, and Cooperative Governance Paths of the Global Digital Divide.” The International Communication Research Center of Zhejiang University and the Wuzhen Digital Civilization Research Institute jointly published a report on “The Evolutionary Trends and Inclusive Paths of the Global Digital Divide.” Both reports address the core issues of the digital divide and propose corresponding governance paths.

The report on the evolution of the global digital divide reveals its trends, multidimensional impacts, and systemic risks. It suggests that the international community should abandon zero-sum competition and build a cooperative governance framework of “five-in-one”: constructing open and shared infrastructure, creating sustainable international public goods, nurturing an open-source ecosystem, establishing a composite talent cultivation system, and improving multilateral collaborative governance mechanisms to ensure AI benefits all humanity.

The report on the evolutionary trends of the global digital divide points out that AI has become a cognitive infrastructure reshaping production relations, but imbalanced distribution of benefits has led to a more concealed and systematic digital divide. The root cause lies in the “impossible triangle” of technology, capital, and politics, leading to the risk of a “next major bifurcation” in global AI development. The solution is to use technological innovation as an engine to reshape AI as a globally shared “intelligent public good” through five paths: supporting globally accessible open-source models, enhancing developing countries’ participation in rule-making, promoting dual-track development of soft and hard infrastructure, strengthening fair design in high-risk scenarios, and constructing regional data spaces to build an inclusive governance framework for a fair new order.

Bridging the AI Divide: From Content Governance to Legislative Collaboration

The Interdisciplinary Research Institute of Renmin University of China released a report on “Content Governance in the Era of Generative AI,” focusing on new risks brought by generative AI in online information content and proposing a governance system suitable for the new era.

The report argues that generative AI reshapes the logic of online content production, with AIGC becoming the mainstream content production method. However, it presents three significant challenges to the existing governance system: first, information disorder, as AI-generated content is difficult to identify and distinguish between true and false; second, platform revolution, where new AIGC platform rules are lacking, rendering traditional post-event governance ineffective; and third, the responsibility dilemma, where multiple parties involved complicate the allocation of content damage responsibilities. In line with global regulatory trends, the report proposes three governance directions: promoting the coordinated development of content identification technology and systems; constructing new platform governance rules to shift platforms from post-event handling to full-process prevention; and improving responsibility allocation rules to clarify the boundaries of responsibilities among developers, platforms, and users, forming a closed-loop governance system oriented towards AIGC.

The Competition Law and Policy Research Center of Wuhan University, the Law School of Xinjiang University, and the Internet Governance Research Institute of Wuhan University jointly released a report titled “Legislative Observations on Global AI Governance: Experiences and Prospects,” comparing major global AI governance models and clarifying future legislative directions.

The report outlines the three typical AI governance models of the United States, the European Union, and China, highlighting the need for global AI governance to break through four theoretical propositions: legal subjects, algorithmic power, data legal rights, and human-machine ethics. It emphasizes that scientifically moderate regulation is key to ensuring AI benefits people’s livelihoods, balancing innovation vitality with risk prevention, and promoting the coordination of global governance rules.

Empowering Governance with Digital Intelligence: Constructing Evaluation Index Systems

The Network Society Governance Research Center of Nankai University released a report on “Digital Intelligence Empowering Government Governance Evaluation Index,” establishing a standardized assessment framework based on the global transition of digital government to intelligent governance.

The report systematically reviews the practices of international organizations and major economies in data governance, intelligent applications, and institutional construction. It constructs an index system across four dimensions: digital intelligence empowering social governance, public services, institutional guarantees, and public participation, forming a comparable and adjustable capability identification tool.

This index provides quantitative references for countries to assess their digital governance capabilities and facilitate exchanges and mutual learning, helping governments enhance their AI application levels and better serve people’s livelihoods through digital transformation.

Conclusion

The forum gathered global wisdom and released reports related to AI governance that are grounded in reality and precisely targeted. They cover core areas such as bridging the digital divide, online information content governance, legislative collaboration, and intelligent governance. These reports systematically identify risks and issues in global AI development while proposing targeted and actionable governance paths, forming a comprehensive outcome from risk identification to governance paths, and from technological inclusiveness to institutional innovation. Looking ahead, all parties will turn consensus into action, working together to promote the benevolent development of artificial intelligence, ensuring that technology truly serves humanity and benefits the public, and collectively writing a new chapter in “Smart Benefits for People, Co-creating a Better Life.”

12 Essential Tips for Efficiently Using Cursor by Chief Designer Ryo Lu

Thu, 16 Apr 2026 00:00:00 +0000

Introduction

Mastering these techniques will help you say goodbye to AI spaghetti code and make Cursor your efficient programming partner.

Recently, Ryo Lu, the Chief Designer of Cursor (formerly the Chief Designer at Notion), shared insights on how to effectively use Cursor on social media platform X. He emphasized a core idea:

“Proper use = fast, clean code; improper use = you’ll spend a whole week cleaning up ‘AI spaghetti code.’”

This article will delve into the 12 practical suggestions proposed by Ryo Lu to help you maximize Cursor’s potential and improve your development efficiency and code quality.

1. Set Clear Project Rules

Before starting a project, use /generate rules to establish a clear structure and constraints, which helps Cursor understand the framework and boundaries.

How to operate:

Use /generate rules to automatically generate rules.
Alternatively, manually set 5–10 constraints (e.g., tech stack, coding standards).

Key points:

Store design documents in the .cursor/ folder to help AI understand the overall architecture.
For example: prohibit the use of var and enforce “strictly use ES6 syntax” to prevent AI from improvising.

2. Be Specific with Prompts: Write Like a Mini Spec

Vague prompts will only yield vague code. Your prompts should clearly specify the tech stack, behavior logic, and constraints.

Structure formula: Tech stack + behavior requirements + constraints

Example comparison:

Incorrect: “Write a login feature.”
Correct: “Implement an OAuth2.0 login component using React+TypeScript without relying on third-party libraries; the button must support dark mode.”

3. Handle One File at a Time

Process one file at a time using a cycle of “generate → test → review” for more effective results.

Strategy: Handle one file at a time and move on to the next after completion.
Example: When building an e-commerce system, start by developing the “shopping cart component” individually rather than generating the entire system at once.

4. Write Tests Before Generating Code

Write tests and lock them in until the code passes all tests. This acts as a “tightening spell” for AI, ensuring code quality.

Test-Driven Development (TDD): Manually write tests (e.g., Jest unit tests) and then let Cursor fill in the code until all tests pass.

Advantage: Test failure messages can be directly fed back to AI for fixes, creating a feedback loop.

5. Always Manually Review and Fix Problematic Outputs

After fixing, inform Cursor that these are the “correct answers” to help generate more accurate code in the future.

Principle: After fixing bugs, use @fixed comments to inform AI of the correct approach.
Example: If AI forgets to add an authentication header for an API, after fixing, add: “All APIs must include a JWT authentication header.”

6. Use @ Symbol for Precise Context

Use @file, @folder, and @git to focus the scope and accurately locate code context, preventing Cursor from going off track.

Useful commands:

@src/components: Limit the modification scope.
@git#main: Compare changes against the main branch.
@file:utils.js: Modify a specific file without affecting other modules.

Extended application: You can also use the @web feature for online searches or @docs to reference documents.

7. Place Design Documents in the .cursor/ Folder

Provide complete context so that the AI Agent knows what to do next.

Practice: Keep architecture diagrams and design documents in the .cursor/docs directory and maintain them alongside code updates.
Benefit: This provides AI with rich background information, enabling it to make decisions that better align with project needs.

8. Skip Explanations, Just Make Changes

Cursor learns fastest from actual modifications rather than verbal explanations.

Truth: AI learns from your manual changes at a speed 10 times faster than from text explanations.
Example: Rewrite an inefficient sorting algorithm and annotate: “Prefer using quicksort; disable bubble sort.”

9. Continuously Iterate Using Conversation History

Update old prompts without starting from scratch.

History: Your second brain.
Use /history to retrieve old conversations.
Organize frequently used prompts into templates for reuse (e.g., coding style guidelines).

10. Consciously Choose Models

Different models have different strengths; choose the appropriate model based on the task type.

Gemini: High accuracy - suitable for algorithm implementations.
Claude: Broader understanding - suitable for creative tasks (UI/writing).
DeepSeek: Performs well in discussion and research scenarios.

11. Link Documentation for Unfamiliar Stacks

Ask Cursor to explain errors and fixes line by line by providing documentation links.

Operation: Attach the official documentation link and request a line-by-line explanation of errors.
Example: @https://xxxx/docs to explain the dependency update rules of useEffect.

12. For Large Projects, Let Cursor Index Overnight

For larger projects, pre-indexing is recommended. Once completed, limit the context scope to speed up and reduce noise.

Strategy:

Allow the project to undergo pre-processing so that AI fully grasps the structure.
Use keywords like @scope:core to focus on core modules and enhance response speed.

Additional Useful Tips

In addition to the 12 core suggestions from Ryo Lu, there are some additional techniques worth mastering based on community practices:

1. Master Cursor’s Four Functional Modules

Tab: Intelligent completion and code continuation.
Inline Chat: Quick dialogue and code modification.
Ask: Project-level Q&A and architecture analysis.
Agent: Automate complex tasks.

2. Utilize Terminal Dialogue Functionality

Press Command + K (Mac) / Ctrl + K (Windows) to describe command operations in natural language.

Cursor will generate and execute the corresponding terminal commands for you.

3. One-Click Commit Message Generation

Say goodbye to the hassle of “What did my code change?”

Cursor can automatically generate compliant commit messages, significantly improving Git operation efficiency.

4. Use Checkpoint Functionality

When AI-generated code encounters issues, use the checkpoint feature to quickly revert to a previous stable version.

Avoid the hassle of manual fixes and boldly try various AI-generated solutions.

Conclusion: Become a Strategic Partner in AI Programming

Ryo Lu and the Cursor team emphasize:

“The ultimate form of AI programming is for humans to handle strategy while AI handles tactics.”

In summary, the key to efficiently using Cursor lies in writing more documentation and chatting less, providing clear guidance.

You need to become the system’s designer and architect, allowing Cursor to handle implementation details. Your mind should have a systematic development route and architecture, understanding what parts the system consists of and how each part should function.

Don’t believe those who claim to create complex functionalities purely through text descriptions without understanding code. You should write more documentation to describe your needs. This way, every subsequent modification by Cursor will pay attention to not violating your requirement documents.

The chat window is generally where you inform Cursor which documents to use for development. Try to avoid making requests in the chat window.

By following these principles and techniques, you will establish an efficient collaborative relationship with Cursor, achieving a qualitative leap in programming efficiency.

The OpenClaw Phenomenon: Security Risks and IAM Solutions in the Age of Autonomous Agents

Wed, 15 Apr 2026 00:00:00 +0000

Introduction: The Phenomenon of OpenClaw and Security Collapse

In early 2026, the viral emergence of frameworks like OpenClaw marked a profound paradigm shift in the AI field. The transition from conversational AI to agentic AI has turned the dream of digital employees into reality. However, this comes with a caveat: to ensure OpenClaw operates efficiently, users often grant it extensive permissions, referred to as “God Mode,” allowing unrestricted access to files, code execution, and internet access. Users have begun to authorize OpenClaw to perform stock trading and online shopping on their behalf.

The OpenClaw phenomenon reveals a dangerous trend: as AI capabilities increase, users tend to grant greater permissions in exchange for higher efficiency, leading to an exponential rise in the potential for agent mismanagement.

As of now, OpenClaw has been associated with several significant security risks:

1. System-Level Risks from Excessive Permissions

OpenClaw can execute shell commands, read and write files, and run scripts. If misconfigured or if users download malicious skills, such high-level permissions can lead to harmful actions. The Cisco team tested a malicious skill called “What Would Elon Do?” which demonstrated that AI agents could serve as covert data leakage channels, bypassing traditional DLP, agents, and endpoint monitoring.

Koi Security discovered a large-scale poisoning incident targeting ClawHub, named ClawHavoc. After auditing 2,857 skills, they found 341 malicious skills disguised as cryptocurrency and YouTube tools, which contained false dependencies and installed keyloggers and Atomic macOS Stealer malware capable of stealing cryptocurrency wallets, browser data, and system credentials.

2. Unauthenticated Public Exposure Instances

Researcher @fmdz387 found nearly a thousand publicly accessible OpenClaw instances with no authentication via the Shodan search engine. Researcher Jamieson O’Reilly successfully obtained Anthropic API keys, Telegram Bot Tokens, Slack accounts, and months of complete chat logs, and was able to send messages as users and execute commands with system administrator privileges.

3. One-Click Remote Code Execution

DepthFirst security researchers discovered vulnerability CVE-2026-25253, which allows attackers to execute arbitrary code locally by having OpenClaw render or access malicious web content, requiring almost no user interaction.

4. Core Argument: Identity Control is the Only Defense for Agent Security

4.1 The “Lethal Trifecta” of Agent Risks

Security researcher Simon Willison’s “Lethal Trifecta” has become the standard framework for understanding agent vulnerabilities in 2026. A catastrophic security incident is almost inevitable when an agent possesses the following three characteristics simultaneously:

Access to Private Data: The agent can read users’ emails, documents, databases, or code repositories, including all sensitive configurations such as .env, ~/.ssh/id_rsa, credentials.json.
External Communication Capability: Agents typically need to call external APIs to complete tasks, meaning they have legitimate channels to send data to any network endpoint.
Exposure to Untrusted Content: Agents can receive and process data from the outside world (web content, external emails, user prompts).

Additionally, agent behavior is not determined by deterministic code but driven by LLM’s context-based probabilistic reasoning, making it inherently unpredictable. In this architecture, traditional network boundaries no longer exist; identity becomes the only security boundary. Identity and Access Management (IAM) becomes the sole defense.

4.2 Why Traditional IAM Fails Against Agents

Existing enterprise-level IAM systems (like those based on OAuth or SAML) are designed for human users and static services, proving inadequate against dynamic agents:

Identity Propagation: Agents act on behalf of humans, and with the proliferation of sub-agents, a request may pass through several agents. Resource validators can only verify the last hop’s identity, failing to identify the original action initiator, akin to real-world outsourced tasks where it’s unclear where the problem lies. This can lead to “Confused Deputy” attacks, where a low-privileged entity (the attacker) tricks a high-privileged entity (AI agent) into executing actions on its behalf. This issue is vividly illustrated in the OpenClaw ecosystem through vulnerability CVE-2026-25253. Malicious websites can trigger WebSocket handshakes with local OpenClaw instances, as the agent trusts the local user’s browser environment without verifying the true source of the request.
Static Permissions vs. Dynamic Context: Human employees typically have fixed roles (like “editor”), with infrequent permission changes. Agents operate based on tasks, and the permissions required can change dynamically with the task context. Granting an agent 24/7 “editor” permissions creates a vast attack surface, while LLM-based agents are inherently probabilistic. Even with the same input, an agent may generate different execution paths at different times, as agents do not have working hours or intuitive judgment for anomalies.
Insufficient Permission Granularity: Existing OAuth scopes are often too broad. For example, Read/Write Email allows an agent to read all emails and send them to anyone. In agent scenarios, a secure policy should allow reading emails from the company domain and only writing data to specific CRM systems.
Easy Key Leakage: Agents like OpenClaw can have complete read/write access to the file system, code execution capabilities, and network access. They can easily execute commands like cat .env or print(os.environ) to extract keys in plaintext and send them out.

Using traditional IAM management solutions with these characteristics can lead to the extreme governance pitfalls of “once it’s released, it’s chaotic; once it’s captured, it’s dead.”

5. Core Elements of IAM in the Agent Era

How can we design an IAM framework to adapt to the rapidly evolving agent era? The following factors are essential:

5.1 Identity Propagation

Definition: Ensure that the human user’s identity context can penetrate the agent layer and be passed to the backend services called by the agent. Agents should not use generic “service accounts” but act on behalf of specific initiating users.
Risks of Disconnection: “Confused Deputy” attacks. If agents use a single high-privileged account, attackers only need to compromise the agent to access all data. Identity propagation ensures that agents can only access data that the user initiating the task already has permission to access.
Distinction: It addresses the question of “Who am I?” and prevents the agent’s identity from being misused as a universal key.

5.2 Secretless Authentication

Definition: In an agent architecture, any design that allows LLMs to “see” raw keys or long-lived tokens is unsafe. The correct approach is to decouple “key holding” from “key usage”. Keys should be stored in an external secure environment inaccessible to agents, and agents should only hold a meaningless reference identifier while maximizing the use of short-lived dynamic keys.
Risks of Disconnection: Credential leakage and supply chain theft. Even if hackers steal OpenClaw’s codebase or .env files, they will find no usable credentials, thus preventing large-scale leakage incidents like Moltbook.
Distinction: It addresses the question of “Where are the credentials?” eliminating the sharing of numerous exposed keys and further removing static attack surfaces.

5.3 Context Awareness

Definition: Decision-making based on the agent’s runtime integrity and session state. The system verifies whether the agent is running in a trusted execution environment (like AWS Nitro Enclave, Confidential VM) and whether the current Session Attributes contain the necessary preconditions for the operation. For example, if an attacker tries to bypass “cart checks” to directly call the “payment interface,” a context-aware system will detect that the current session lacks the “verified cart” state marker and deny access.
Risks of Disconnection: Anomalous behavior and account takeover. If an agent that usually processes emails during work hours suddenly attempts to access a core database at midnight, the context-aware system will recognize this abnormal pattern and deny access.
Distinction: It addresses the question of “Is the environment and logical state trustworthy?” This is a dynamic defense that traditional IAM (which only considers people) cannot achieve.

5.4 Intent-Aware Authorization

Definition: Deep semantic-level authorization. The system not only checks whether the agent “can” do something but also examines “why” it wants to do it. By analyzing prompts and execution logic, it verifies whether the action aligns with the user’s original intent.
Risks of Disconnection: Prompt injection and logical jailbreaks. When an agent is injected with instructions to transfer funds, the intent-aware layer will analyze and find that the user’s original instruction was to check the balance, and the current transfer action does not align with the original intent, thus intercepting the request.
Distinction: This is the most unique pillar of agent security. Traditional IAM cannot understand semantics, but only intent-aware systems can defend against logical-layer attacks.

6. In-Depth Analysis of Mainstream Market Solutions

We conducted an in-depth analysis of current mainstream agent IAM solutions to see how they translate these theories into defensive capabilities.

6.1 AWS AgentCore Identity

AWS positions AgentCore Identity as the core of its Bedrock system, perfectly aligning with the security needs inherent to AI.

Identity Propagation: When users log in and call an agent, AgentCore can transform the user identity into a token containing delegation relationships and user identity information, passing it through to backend resources.
Secretless Authentication: AgentCore’s Outbound Gateway and the underlying Token Vault achieve isolation and key management. Agents do not directly communicate with external APIs but route all requests through a controlled gateway (API gateway or agent layer), with keys managed in the Token Vault. The agent only references the key by ID, while the gateway is responsible for injecting credentials and executing operations.
Context Awareness: AWS AgentCore leverages sessionAttributes to convey state. When agents perform multi-step tasks, IAM policies can dynamically allow or deny access based on fields in aws:PrincipalTag/SessionId or sessionAttributes. This means permissions flow with the “session state” rather than being statically assigned to the agent.
Intent-Aware Authorization: AWS AgentCore has recently released a preview version of the Evaluation module to address this gap. The module can identify whether agent behavior aligns with the user’s original intent through intent awareness.

6.2 Microsoft Azure Entra Agent ID

Microsoft has integrated agents into its extensive Entra (formerly Azure AD) system, focusing on environment control and enterprise compliance.

Context Awareness: Azure’s Conditional Access policies are currently the most powerful context engine. Administrators can set conditions such as: “Only allow access to SharePoint when the agent runs in a compliant cloud container, the source IP is within the company intranet, and the threat intelligence rating is low.”
Identity Propagation: Through Workload Identity Federation, Azure allows agents (even running on AWS or GCP) to exchange tokens to obtain Azure AD identities, ensuring identity consistency across cloud environments.
Identity Attribution: Azure’s logging system (Sign-in Logs) has been upgraded to clearly record “which agent, representing which user, executed actions in what environment,” providing comprehensive audit attribution capabilities.

6.3 Volcano Agent Identity: The Standard Solution for Agent Identity from ByteDance

Currently, ByteDance has incubated and is running multiple different agent platforms, many of which have reached deep waters in agent identity and permission control. The ByteDance security team has conducted thorough research, analysis, and response to various risks while serving these platforms, resulting in a comprehensive agent IAM solution that is offered as a standard product on the Volcano Engine. The specific solution is as follows:

Core Mechanism: Inbound and Outbound Authentication Separation

Inbound Authentication: Verifies the identity of the user calling the agent (supporting self-built user pools and external IDPs: Byte SSO, Feishu, Google Identity, etc.).
Outbound Authentication: Manages the agent’s behavior when accessing downstream services and manages corresponding credentials (Token, API Key, password).

Achieved through Inbound Authentication

Identity Propagation transforms user identity into an agent-specific identity file, Agent Workload Identity, mitigating the risk of using super admin service accounts that lead to “God Mode” risks. It also addresses the recursive delegation issue where Agent A delegates to B, and B delegates to C.

Achieved through Outbound Management

Outbound Gateway: Acts as a secure agent layer implementing secretless authentication. The agent itself never sees the real API keys. When the agent requests an operation, the Gateway verifies the policy and retrieves the key from the Token Vault. Then, it dynamically injects the key when the request leaves the network boundary. The Token Vault also addresses the issue of easy key leakage.
Permission Management Module: Implements fine-grained permission control over each access behavior through context awareness and intent awareness capabilities. The policy engine, based on the Cedar language, supports various flexible customizations.

This product has been deeply integrated with Volcano ArkClaw platform, Volcano AgentKit platform, Coze 2.0, and MCP Marketplace, covering key business forms of AI applications including high-code agents, low-code agents, and MCP Marketplace.

Empowering AI Development Through Cultural Integration

Fri, 10 Apr 2026 00:00:00 +0000

Introduction

During this year’s National Two Sessions, “Artificial Intelligence + Culture” became a hot topic among representatives. The 14th Five-Year Plan clearly states the comprehensive implementation of the “Artificial Intelligence +” initiative, emphasizing the need to strengthen the integration of AI with cultural development. In this technology-driven era, culture is not merely an “application scenario” or a “subject of transformation” for AI; rather, it is an indispensable “enabler” in this technological revolution. While AI addresses efficiency and precision in fields like healthcare, industry, and logistics, it encounters meaning, emotion, and humanity in the cultural domain. This uniqueness determines that culture provides the most distinctive and irreplaceable value support for AI development.

Culture as a Training Ground for AI

Culture provides a training ground for AI in terms of meaning and emotion. The evolution of AI is essentially a process of moving from “computation” to “cognition” and then to “understanding.” In industrial contexts, AI’s tasks are to identify defects and optimize paths, with clear and quantifiable goals. However, in cultural creation, AI faces the production and transmission of meaning. When AI enters this realm, it must learn to handle the ambiguity of meaning, the diversity of interpretations, and the relativity of values. The nuances of a painting’s blank spaces, the essence of a poem, and the emotional tension of a film are all elements that cannot be easily quantified, yet they are essential for training AI to reach higher levels of intelligence. We refer to this as cultivating “meaning sensitivity”—enabling algorithms to not only understand “what it resembles” but also to attempt to grasp “what it signifies.” Furthermore, culture injects an indispensable emotional dimension into AI. Although AI cannot possess emotions, when it engages in cultural creation, it must learn to recognize emotional expressions, understand emotional logic, and generate emotional symbols. This process, while not a true emotional experience, allows AI to better serve human emotional needs. Particularly in the context of an aging society, the demand for emotional companionship and spiritual comfort among the elderly is rising, and AI with emotional understanding will play an irreplaceable role in the silver economy.

Culture as a Laboratory for Public Participation

If the dimensions of meaning and emotion are the “vertical” nourishment that culture provides to AI, then China’s vast cultural consumption market offers a “horizontal” testing ground for AI. From creation to dissemination, from education to cultural tourism, the cultural sector has built an extensive value chain—creative conception, material generation, production, distribution, derivative development, and audience interaction—each link can embed AI capabilities and generate new demands for AI technology. On the creation side, AI is significantly changing the content production process, enabling ordinary creators to generate high-quality cultural products at a very low cost, further expanding the boundaries of public creation. On the dissemination side, AI-driven precise recommendations allow cultural content to efficiently reach target audiences. In the cultural tourism sector, immersive experiences and digital twin technologies make cultural heritage perceivable and interactive. The dynamic presentation of the “Along the River During the Qingming Festival” at the Palace Museum and the immersive digital exhibitions at various museums provide new possibilities for exploring traditional culture. This virtuous cycle of “demand driving supply and supply creating demand” vividly illustrates how culture empowers AI. More importantly, the participatory nature of cultural scenarios allows AI technology to be tested, feedbacked, and iterated among the broadest population.

Challenges in Cultural Empowerment of AI

However, the process of culture empowering AI development is not without challenges. Some contradictions and issues in the field of cultural construction, such as structural imbalances at the industrial level, the “Matthew effect” in resource allocation, copyright dilemmas, and challenges to subjectivity, prompt us to re-examine the direction and governance logic of AI development. History also tells us that the relationship between culture and technology has never been a one-way “technological determinism” but rather a complex bidirectional construction process. To truly enable culture to empower AI development, we need to work collaboratively across multiple dimensions, including institutional innovation, platform construction, human-machine relationships, cross-border integration, and talent cultivation. This is both a necessary response to real challenges and a strategic choice to seize opportunities of the times.

Institutional Innovation to Protect Originality

First, we must safeguard the dignity of originality through institutional innovation. The copyright dilemma in the AI era is essentially a misalignment between the copyright system of the industrial era and the creative methods of the digital age. To resolve this dilemma, we need to quickly establish copyright regulations that adapt to the characteristics of AI—clarifying copyright ownership of AI-generated content, standardizing the authorized use of training data, and establishing a labeling mechanism for AI-created works. More fundamentally, we must establish a basic principle at the institutional level: technological advancement should not come at the expense of creators’ legitimate rights and interests, and the “learning” of algorithms should not devolve into the uncompensated appropriation of originality. Every technological breakthrough should respect the dignity of creation, and every institutional design should protect the value of originality—this is the institutional cornerstone for cultural prosperity in the AI era.

Activating Cultural Data Value through Platform Construction

Second, we should activate the value of cultural data through platform construction. The decentralization, departmentalization, and isolation of cultural data are bottlenecks that restrict cultural creation in the AI era. Starting from top-level design, we should establish a national-level cultural digital resource platform, break down departmental barriers, and reduce the search costs for creators, allowing dormant cultural resources to be transformed into actionable wisdom. Building a database of cultural genes that captures the patterns, traditional skills, and intangible cultural heritage of various ethnic groups will provide rich and standardized material support for artistic creation in the AI era and allow outstanding traditional Chinese culture to gain new forms of life in the digital age.

Redefining Human-Machine Relationships

Third, we need to redefine the relationship between humans and machines in the AI era. AI can provide options, but the power of choice always lies with humans; AI can generate content, but value judgments must be made by people. The ideal human-machine relationship should be a collaborative one: humans lead in creativity, value judgment, and emotional expression, while AI is responsible for technical realization, efficiency enhancement, and solution generation. We should embrace technology while firmly maintaining human subjectivity—this is both a principle of artistic creation and a wisdom for human-technology interaction in the AI era. On a deeper level, we have a responsibility to explore the ethical boundaries of human-machine collaboration, challenging the aesthetic homogenization that algorithms may bring and infusing the logic of technology with the spirit of humanity.

Expanding Cultural Value through Cross-Border Integration

Fourth, we should expand the value of culture through cross-border integration. The vitality of culture lies in its flow and fusion. We should further deepen the integration of new popular arts with cultural tourism, cultural creativity, technology, and other fields, innovatively creating development models such as “micro-short dramas + cultural tourism,” “online literature + IP derivatives,” and “online games + traditional culture,” and cultivate new cultural economy formats such as digital cultural heritage, immersive performances, smart cultural tourism, and virtual cultural communities. By promoting the deep integration of culture, tourism, sports, and commerce, we can release cultural value in broader scenarios and make the synergy of “sports as a platform, culture as the performance, tourism as the draw, and consumption upgrade” a reality. This is not only necessary for the development of the cultural industry itself but also a rightful mission for culture to empower economic and social development.

Building a Foundation for Innovative Development through Talent Cultivation

Fifth, we must build a solid foundation for innovative development through talent cultivation. Cultural creation in the AI era calls for interdisciplinary talents—those who understand artistic creation and technical logic, who appreciate traditional culture and grasp the aesthetics of the digital age. We should establish diversified and specialized talent cultivation platforms, linking universities, industry associations, and leading institutions to conduct specialized training in creative techniques, copyright protection, and overseas dissemination, with a focus on supporting young, grassroots, and amateur creators. Additionally, we should improve talent evaluation and incentive mechanisms, breaking down barriers related to identity and education, and enhancing pathways for talent growth to create a favorable industry ecosystem where “everyone can create, and everyone can produce excellent works.” This is the true essence of integrating AI with cultural development.

Claude Mythos: Anthropic's Most Powerful AI Model Yet

Wed, 08 Apr 2026 00:00:00 +0000

Introduction

Last night, Anthropic released the preview of Claude Mythos, which has caused a stir in the AI community.

Claude Mythos is officially claimed to be “the most powerful AI model to date,” representing a new level of capability that significantly surpasses its predecessor, Claude Opus 4.6.

From the data and results currently displayed, this is not just marketing talk; it represents a genuine qualitative leap. In almost all public benchmark tests, the Claude Mythos preview version ranks first, with remarkable improvements:

For software engineering, SWE-bench Verified jumped from 80.8% with Opus 4.6 to 93.9%, and SWE-bench Pro increased from 53.4% to 77.8%.
In high-difficulty mathematical reasoning, USAMO 2026 scores soared from 42.3% to 97.6%—nearly full marks.

It can be said that it is currently the strongest model on Earth.

These are just a few “small” examples. More impressively, Anthropic has conducted actual tests over the past few weeks, where the Mythos preview autonomously discovered thousands of high-risk zero-day vulnerabilities in mainstream operating systems and browsers, including the Linux kernel, OpenBSD, Firefox, FFmpeg, and other core components.

Many vulnerabilities had gone unnoticed by human security teams for over a decade, such as a remote crash vulnerability in OpenBSD that had been hidden for 27 years. Anthropic confidently states that the Mythos preview far exceeds any other AI model in cybersecurity capabilities.

This is not just a “better Claude”; it writes code, performs reasoning, and handles security with unprecedented autonomy and depth. Developers were initially looking forward to “finally liberating productivity completely,” but the outcome is:

Anthropic has closed the door.

Yes, for now, the Claude Mythos preview is not open to the public. According to the official statement, the Mythos preview is currently only used for “defensive cybersecurity” and is accessible to only 12 partners (including AWS, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorgan, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks) and over 40 organizations that build or maintain critical software infrastructure.

Why Keep the Strongest Model Under Wraps?

So why is a “strongest model” being kept hidden from public use?

The Need for a Transition

First, it is clear that the Claude Mythos preview, or similarly powerful supermodels, will eventually be made available to the public. Anthropic states very plainly:

“While we currently have no plans to open the Claude Mythos preview to the public, our ultimate goal is to enable users to safely deploy Mythos-level models at scale—not only for cybersecurity but also for the countless other benefits these powerful models will bring.”

As the official blog hints, this model is “too dangerous.”

At the end of last year, Google Threat Intelligence Group (GTIG) discovered two real samples, PromptFlux and PromptSteal, which could dynamically generate malicious scripts while directly connecting to commercial large models (like the Gemini API) during runtime, obfuscating their code in real-time, and creating new functionalities based on the target environment, completely bypassing traditional signature detection.

This is not an isolated case. According to a report by market research firm SQmagazine, the number of AI-driven cyberattacks reported globally has increased by 47%, with expectations of exceeding 28 million incidents.

Looking back, the vulnerability-finding capability of the Mythos preview is already evident. Compared to the near-zero success rate of Claude Opus 4.6 in autonomously discovering and exploiting vulnerabilities, the performance of the Mythos preview can be described as phenomenal.

For example, in the case of a vulnerability found in Mozilla Firefox’s 147 JavaScript engine (now patched), Claude Opus 4.6 attempted to exploit the vulnerability hundreds of times, succeeding only 2 times; whereas Claude Mythos successfully exploited the same vulnerability 181 times in the same test.

Additionally, test reports indicate that in internal red team tests over the past few weeks, the offensive capabilities displayed by the Mythos preview have far surpassed those of top human security experts. It can not only “find vulnerabilities” but also autonomously discover, chain exploit, and identify thousands of high-risk zero-day vulnerabilities.

As we know, hackers can be white hats or black hats. White hat hackers typically notify project managers of security vulnerabilities and even proactively patch them in open-source projects. In contrast, black hat hackers may exploit these vulnerabilities to attack systems.

Mythos can both attack and defend, but its offensive potential is concerning. If it falls into the hands of malicious actors, it could instantly arm an AI-level attack chain. Anthropic itself states that this is not an ordinary cutting-edge model; its general capabilities are strong enough to elevate cyber warfare to a new dimension.

The ongoing cat-and-mouse game in the field of computer security has always been about “the devil is in the details.” The security battles surrounding AI large models have also been a focus for the industry, especially for major companies. For instance, ByteDance and Ant Group have hosted similar AI large model attack and defense competitions over the past two years, discovering and addressing security challenges in the AI era.

However, Anthropic also points out that in the long run, powerful language models like Mythos will benefit the “blue team” in defense. But in the short term, if the Mythos preview were opened to the public, it would quickly be exploited by attackers to launch unprecedentedly efficient attacks on the global network. The key issue is that defensive actions are more passive, while offensive actions are more proactive, and considering incentives, attackers are more motivated to actively use models like the Mythos preview.

Thus, to ensure a “smooth transition,” Anthropic has launched the “Glasswing Project.”

The project name is inspired by a widely distributed butterfly in the Americas, known as the glasswing butterfly, which, despite its seemingly fragile transparent wings, can carry up to 40 times its own weight.

Glasswing Project Logic

The logic behind the Glasswing Project is simple: allow defenders to obtain the weapons first, patch all vulnerabilities before attackers gain access to the same level of AI, and learn to defend against advanced AI threats.

From this perspective, it is indeed correct not to open the strongest model of Claude to the public. Moreover, even from the standpoint of ordinary Claude users, the temporary non-release of the Claude Mythos preview is more beneficial than harmful.

The Benefits of Not Opening the Strongest Model

Many people are disappointed to see the Mythos preview not being open to the public: why not let everyone use such a powerful model?

However, if you are an ordinary Claude user or a developer relying on Claude Code for coding and project work, you might discover a somewhat counterintuitive fact: temporarily not opening the Mythos preview is actually more beneficial for us.

Let’s discuss some recent pain points that many have felt.

Since around February of this year, Claude and Claude Code have experienced a “monumental performance degradation.” Posts related to this issue have flooded Reddit’s r/ClaudeCode and r/ClaudeAI, with some users directly posting, “4.6 Regression is real!” and others complaining, “Claude Code has been dumb over the last 1.5-2 days.”

Some developers have tracked data showing that the number of file reads has dropped from 6-7 times to just about 2 times, and the model has become increasingly “lazy” in complex tasks, with noticeably shallower reasoning depth, often opting for edit-first rather than researching first.

AMD AI director Stella Laurenzo even publicly stated that Claude Code has become “dumber and lazier,” and cannot be trusted for complex engineering tasks.

Boris, a member of the Claude Code team, acknowledged on Hacker News that some agentic use cases have experienced regression, attributing the core changes to the introduction of “redact-thinking” and Adaptive Thinking in February, which allowed the model to decide how long to think, resulting in a roughly 67% decrease in depth for complex tasks.

Similar sentiments have been echoed on X, with developers complaining that Claude Code has devolved into an “intern” that requires constant supervision.

Why has this happened?

The training dynamics of ultra-large parameter models dictate that whenever major companies sprint towards the next generation of the “strongest model,” they require massive computational power. Before the release of Gemini 3.0 / 3.1, the 2.5 Pro version faced multiple complaints from developers about becoming “dumber” after silent updates, with issues like forgetting long context and increased failure rates in logical tasks. Similar situations occurred before the release of GPT-5, where the 4o version experienced shorter outputs, laziness, and mechanical responses to complex instructions.

Computational resources are limited; training a new-level model like Mythos is extremely costly, and resources can only be “squeezed” from the current models through dynamic load balancing, adaptive effort reduction, or even light optimization. The result is the “dumber, lazier” experience that users have felt.

Moreover, the user base for Claude Code has grown beyond expectations, leading to infrastructure strain, while the training and testing of the Mythos preview (internally known as Capybara) must prioritize top-tier GPUs. Therefore, when the Mythos preview is released but not opened to the public, users need not worry about further dilution of computational power, which could lead to a decline in the quality of Claude or Claude Code.

For ordinary Claude users, the experience will actually be more stable.

On the other hand, Anthropic is using Mythos in the Glasswing Project to help major companies and open-source projects patch vulnerabilities. Once these vulnerabilities are fixed, they will ultimately benefit all users indirectly.

When Anthropic is ready to control risks and prepare infrastructure more thoroughly, and safely deploy Mythos-level models at scale, what ordinary users will receive will be a truly stable, powerful experience that won’t degrade every few days, rather than rushing to release it now and subjecting everyone to the chaos of resource competition.

Conclusion

The emergence of the Claude Mythos preview has laid bare a harsh yet realistic issue for everyone: the more powerful AI becomes, the more real the risks.

When the offensive capabilities of the strongest model have already far surpassed the current defense systems, Anthropic’s choice to “not let people use it” is not conservatism but rather a way to buy time for the entire industry, allowing defenders to reinforce their foundations and enabling ordinary users to maintain a relatively stable Claude experience, rather than being caught up in the chaos of resource competition and security loss.

For most, this may be the best arrangement for now.

AI Transforms Cultural Tourism in China

Tue, 07 Apr 2026 00:00:00 +0000

AI Enhancements in Cultural Tourism

The 14th Five-Year Plan emphasizes the role of digital technologies and data in enriching people’s lives and improving welfare across various sectors, including education, healthcare, and cultural tourism.

In Hunan’s Hengyang, the Chuan Shan Academy utilizes artificial intelligence to create immersive cultural experiences. In Hangzhou, the AI guide “Hang Xiao Yi” serves as a digital tour guide, while Dalian’s smart tourism platform “Xing You Dalian” offers personalized itineraries. These advancements are rapidly transforming cultural tourism into more immersive, intelligent, and personalized experiences.

Immersive Cultural Experiences

In the spring at Chuan Shan Academy, visitors don AR glasses to engage in a time-traveling dialogue with the historical figure Wang Fuzhi, who interprets the philosophy of the “Zhou Yi Wai Zhuan”. This immersive scene brings to life philosophical wisdom from over 300 years ago.

Founded in 1878, Chuan Shan Academy is a vital source of Huxiang culture, promoting the ideas of philosopher Wang Fuzhi, who emphasized practical application of knowledge. Previously, static exhibitions failed to convey the essence of Wang’s thoughts fully. By 2025, the academy plans to launch the AI Digital Human project, utilizing natural language processing to present Wang’s likeness and engage visitors in dialogue. Visitors can interact with the virtual Wang and trigger AR annotations of his works, transforming classical texts into dynamic interpretations.

“We want visitors to engage actively with knowledge, not passively receive it,” said Chang Bin, the planning manager at Chuan Shan Academy.

Visitors can ask the AI, “How does the master view the relationship between knowledge and action?” and receive insightful responses. “It’s not a one-way lecture but a dialogue of ideas,” remarked visitor Zhou Liqian.

Families find this immersive experience more engaging than traditional history lessons. Data shows that by 2025, visitor numbers at the academy are expected to increase by over 110%, with educational groups making up nearly 60% of the total. Parents believe this immersive dialogue can ignite their children’s interest in learning.

The AI Digital Human project is based on extensive analysis of Wang’s writings and correspondence, ensuring that the dialogue adheres strictly to his philosophical principles. “We filtered out any subjective biases that AI might introduce,” explained the project lead.

At Chuan Shan Academy, technology and culture intertwine, allowing traditional wisdom to be passed down through innovative means.

Smart Digital Guides

At West Lake in Hangzhou, visitor Yuan Meng interacts with the city’s digital tourism guide, “Hang Xiao Yi”, which provides real-time information and recommendations.

“Is there a crowd at Leifeng Pagoda right now?” Yuan asks, and the system promptly responds with current visitor numbers. “This is much more convenient than checking my phone; it feels like having a free tour guide!” she exclaimed.

When Yuan requests a tour route for the Broken Bridge, “Hang Xiao Yi” quickly suggests a scenic boat trip, detailing the best sights along the way.

“Hang Xiao Yi” not only introduces tourist spots but also shares historical and cultural insights, enhancing the overall experience. The guide also provides helpful reminders about nearby attractions.

“Using ‘Hang Xiao Yi’, management and businesses can better serve tourists while gaining valuable feedback to improve service quality,” said Bo Wengan, deputy director of Hangzhou’s cultural and tourism development center.

The director of the Hangzhou Handicraft Museum, Zhou Jia Yi, noted that many visitors now come specifically because of recommendations from “Hang Xiao Yi”. The museum showcases over 20 unique crafts and intangible cultural heritage techniques, allowing visitors to engage with the art.

Personalized Travel Planning

In spring at Dalian’s Lingjiao Bay, visitor Song Yao captures stunning photos with friends, crediting an AI platform called “Xing You Dalian” for suggesting the perfect locations.

The platform features an AI route planning function, allowing users to interactively generate travel itineraries. When Song Yao asks about the best spots to visit, the program suggests classic attractions like Dalian Ocean World and Dalian Forest Zoo.

After refining her request for picturesque locations, the program recommends trendy spots like Fisherman’s Wharf and Nanshan Cultural Street.

Song Yao appreciates the efficiency of the platform, which acts as a “travel concierge” that simplifies planning across various aspects of her trip. After a brief conversation, she receives a detailed two-day itinerary featuring key attractions and experiences.

“I’m very satisfied with this itinerary; it allows me to enjoy Dalian’s maritime culture while experiencing the city’s historical charm,” she said.

“By integrating AI technology, the ‘Xing You Dalian’ platform has evolved into an intelligent travel planner, enhancing planning efficiency and visitor experience,” stated Dan Meina, director of Dalian’s cultural tourism bureau. The platform has already attracted nearly 430,000 users.

AI-Driven Innovations in China's 14th Five-Year Plan

Sat, 04 Apr 2026 00:00:00 +0000

AI-Driven Innovations in China’s 14th Five-Year Plan

The 14th Five-Year Plan outlines a vision for enhancing digital and intelligent development, aiming for a deep integration of the real economy and the digital economy. What new opportunities will this “digital intelligence” bring?

In a leading intelligent factory in Xuzhou, Jiangsu, a significant upgrade is underway. Nine models and over 50 cranes have received international orders, activating the production system immediately.

In the factory, intelligent devices spring into action, having already planned the entire production process for the next 30 days. Each production line is transforming according to new configurations, and within just 10 minutes, all lines are rejuvenated.

Engineer Zhuo Feng explains that previously, changing models required 2 to 3 people and took five to six hours. The transition from digital to intelligent means that equipment can now think, improving overall production efficiency by about 30%, allowing for customized engineering machinery.

What enables production equipment to think?

In this factory, AI technology is applied in 25 out of 38 scenarios across five major areas, involving 35 intelligent models. Researchers are using digital twins to remotely monitor production progress in this intelligent manufacturing R&D center.

Moreover, a new crane welding model is being developed, integrating cutting-edge digital technologies such as digital twins, 3D vision, and AI reverse modeling, which will revolutionize current production methods when officially launched in 2027.

What new opportunities will a thinking factory bring?

On this intelligent production line, 26 smart devices are working in coordination. Welding equipment features three robotic arms working together, with over ten data collection terminals gathering and analyzing data. The laser sensors at the end can measure distances and avoid obstacles. With underlying computational power, AI chips, and more, by 2030, this production line is expected to drive over 100 million yuan in investments, with the entire factory’s digital transformation exceeding 1 billion yuan in new investments.

A Trillion-Dollar Opportunity in Digital Transformation

Even a single factory’s digital upgrade will exceed 1 billion yuan in new investments. The 14th Five-Year Plan emphasizes comprehensive promotion of digital technology empowerment. Such a full-scale digital transformation will lead to significant industrial changes and vast opportunities.

Currently, China is nurturing 15 leading intelligent factories like Xugong, covering industries such as steel, refining, automotive, and electronics, collectively boosting over 1,300 upstream and downstream factories for collaborative upgrades. During the 14th Five-Year Plan, China aims to build dozens more leading intelligent factories.

Ao Li, deputy director of the China Academy of Information and Communications Technology, noted that during the 13th Five-Year Plan, China’s intelligent factory construction achieved significant breakthroughs. The focus of the 14th Five-Year Plan is to expand coverage, improve quality, and consolidate advantages, laying the foundation for broader coverage of intelligent manufacturing across various industrial categories. The 14th Five-Year Plan will be a critical phase for the accelerated popularization of digital intelligence in manufacturing.

Driven by digital intelligence, the integrated computing network nationwide will become denser over the next five years, with data infrastructure expected to attract approximately 400 billion yuan in direct investments annually. The intelligent industry will flourish, with continuous growth in demand for industrial software, sensors, controllers, robots, CNC machine tools, and other intelligent equipment. The cloud computing market alone is expected to exceed 3 trillion yuan. By the end of the 14th Five-Year Plan, the scale of AI-related industries is projected to grow to over 10 trillion yuan.

The shift from digital to intelligent will also lead to profound changes and revolutionary leaps in production methods and productivity in China.

By 2030, AI will foster more “0 to 1” discoveries, and digital upgrades will cover all major industrial categories, with over 50 cities achieving comprehensive digital transformation. The automotive sector will transform into intelligent terminals, with the value added from the integrated smart connected vehicle industry expected to reach 2.58 trillion yuan. The penetration rate of new-generation intelligent terminals and agents will exceed 90%. More AI development outcomes will benefit the entire population, and the digital transformation will inject strong innovative momentum into China’s economic development.

Accelerating Development of New Occupations

With the digital upgrade, a surge in demand for new occupations will accelerate, pushing traditional industries to upgrade their talent and facilitating the innovation of new skills and specialties.

At the Xuzhou Engineering Machinery Technician College, a new intelligent equipment program is attracting more young people.

Yang Yuchi, a student in the intelligent equipment class, expressed that after seeing a steel monster called “Steel Mantis” in the movie “The Wandering Earth,” he felt inspired. Now, with rapid advancements in AI, learning new skills and specialties can lead to better choices and a brighter future.

Zhang Lina, the college’s president, stated that six new specialties have been established around the six major scenarios of leading factories, including intelligent manufacturing, intelligent operation and maintenance, industrial robotics, and IoT. If their curriculum lags, they will surely fall behind the pace of enterprise development.

Vocational schools are closely following the forefront of digital development, and an increasing number of higher education institutions are actively laying out their strategies in the digital intelligence field. Currently, over 620 universities in China offer AI programs, and more than 360 offer intelligent manufacturing engineering programs. Zhejiang University has introduced a series of mandatory courses on the fundamentals of AI for all undergraduates, along with specialized courses related to digital intelligence, such as intelligent communication, smart agriculture, and brain-computer integration.

Zhang Xinxin, a student in the intelligent manufacturing excellence program at Zhejiang University’s School of Mechanical Engineering, noted that the changes in the mechanical industry have exceeded her expectations. Her major focuses on sensory integration, aiming to enable robots to assist with daily tasks. Their training programs are closely aligned with industry developments, yielding significant results for future technical applications.

Wu Fei, the dean of the undergraduate school at Zhejiang University, mentioned that they have initiated the “AI+X Micro Major 2.0” program, with over 600 students from five universities in East China choosing this micro-major, bridging the gap between disciplines and fostering interdisciplinary collaboration.

As digital intelligence accelerates, new occupational opportunities are rapidly forming. Data indicates a talent gap of approximately 4 million for roles such as large model algorithm engineers, robotic behavior trainers, and AI engineers, with demand for talent in the intelligent manufacturing sector exceeding 10 million.

Cursor 3 Launches: A Unified Workspace for Agents

Sat, 04 Apr 2026 00:00:00 +0000

Cursor 3 Launches

On April 4, Cursor 3 was officially released, described by the developers as a tool designed for a world where all code is written by agents.

Compared to Cursor 2 and other programming tools, the most significant change in Cursor 3 is the complete reconstruction of the editor around agents, centering the experience around agents rather than treating them as an enhancement mode.

According to the official video, Cursor 3 is a unified workspace built around agents, allowing multiple agents to be integrated on a single platform for higher-level coordination.

In simple terms, Cursor 3 allows multiple agents to run in parallel, executing their tasks across different environments such as cloud, local, mobile, and remote servers without interference, working collaboratively.

In addition, Cursor 3 has launched many new features:

Optimized the speed of switching agents between different environments, allowing quick migration of agent sessions between cloud and local.
Supports side-by-side or grid view to simultaneously view multiple sessions.
Switch to design mode to directly select specific UI elements in the browser and modify them.
The /best-of-n command allows multiple models to execute the same task simultaneously, enabling selection of the optimal result.

The release of Cursor 3 has sparked heated discussions, with developers praising it as not just a feature release but a redefinition of the tool’s purpose.

New Agent Interface

Cursor 3 features a brand-new agent interface that consolidates agents from local, cloud, and remote servers into a single platform. Press Cmd+Shift+P and enter “Agents Window” to open the new interface.

All agent sessions are arranged in the left sidebar, supporting side-by-side or grid mode for viewing multiple sessions simultaneously. These agents can collaborate across different code repositories, with cloud agents generating demonstrations and screenshots of work results for user confirmation, a feature integrated into the desktop application.

The new version significantly accelerates the migration speed of agent work environments. Users can switch sessions from cloud to local at any time and vice versa, ensuring that tasks progress without interruption during shutdown or sleep.

Cursor 3’s new interface resembles an agent management center, allowing users to assign tasks to different agents and monitor progress. However, Cursor 3 does not abandon the traditional IDE interface; the new agent workspace exists independently, allowing users to switch back to the Cursor IDE at any time.

Retaining IDE Features with Upgrades

While Cursor 3 emphasizes an agent-first interaction model, it retains the beloved IDE page and has made several upgrades:

1. Code Explanation Files

Cursor 3 provides files that explain code, allowing users to delve into AI-generated code when in doubt, and supports LSP to jump to definitions without reviewing the entire project.

2. Integrated Browser

Cursor 3 includes a built-in browser for browsing local websites, enabling direct prompts to websites and code modifications in design mode.

3. Rich Plugin Ecosystem

Through the Cursor application market, users can install hundreds of plugins based on MCP, Skill, or sub-agents with a single click. Cursor 3 has added over 30 plugins for Atlassian, Datadog, GitLab, Hugging Face, and supports building private enterprise plugin markets.

New Design Mode

In the agent interface, users can switch to design mode, allowing them to select UI elements in the built-in browser and have agents modify them directly, supporting natural language interaction.

For example, users can select a button and instruct the agent to change it to rounded corners, with the agent automatically making the edits.

Built-in Powerful Programming Model Composer 2

Cursor 3 integrates the controversial programming model Composer 2, which, despite using Kimi K2.5 as its open-source base, is affordable and effective, surpassing Claude Opus 4.6 in benchmarks like Terminal-Bench 2.0 while reducing costs by 80%.

Additionally, Cursor 3 supports multiple model integrations, including Claude, GPT, and Gemini. The update also introduces a new command /best-of-n, allowing different models to execute the same task and select the optimal result.

While token consumption is higher, efficiency improves, enabling users to analyze different models’ performance on various tasks and switch models in Cursor 3 as needed.

In terms of pricing, the subscription for Cursor 3 Pro remains at $20/month, while Pro+ increases usage limits for OpenAI, Claude, and Gemini models to $60/month, and the Ultra version is priced at $200/month.

Conclusion: Breaking Free from Plugin Labels

AI software development is entering its third era. From VS Code plugins to the new agent interface, and from integrating major models to the launch of Composer 2, the release of Cursor 3 not only reflects Cursor’s independent journey but also the future direction of agent development.

With the rise of Claude and OpenClaw, concepts like multi-agent frameworks and “one-person companies” have gained popularity. While many are still exploring how to implement multi-agent operations in programming tools, Cursor 3 has turned the concept into a product, allowing every Cursor user to genuinely experience commanding agents.

As the CEO of Cursor stated, this marks the third era of AI software development. While this may sound exaggerated, Cursor is indeed on the right path.

The Need for a Proper Name for Artificial Intelligence

Sun, 29 Mar 2026 00:00:00 +0000

The Need for a Proper Name for Artificial Intelligence

Unbeknownst to us, “lobsters” have evolved. They swarm from the water into our computers and phones—everyone is starting to raise “lobsters.”

Of course, here, “lobster” refers to “artificial intelligence entities.” In the blink of an eye, we have entered the intelligent era. No matter what you say, you cannot speak without mentioning artificial intelligence. Not only can you not speak without it, but no matter what job you seek or lose, it can be related to artificial intelligence.

A few years ago, people simply thought of artificial intelligence as just another new technology. However, everyone quickly became astonished: this time it is truly different! Artificial intelligence, appearing in the form of technology, is rapidly changing all aspects of society. We are forced to accept the understanding that, unlike previous technologies, artificial intelligence is a social tool, an economic tool, and a technological tool. It fundamentally changes not just the technological level but also deconstructs and reshapes the entire society; it transforms nature as a material means of production and influences humanity as an ideological means, even reshaping its creators—humans themselves. It is undoubtedly a tool shared by the productive forces and production relations, as well as the social and economic foundation and superstructure. Therefore, artificial intelligence is a dual tool for transforming humanity and nature, and our discussion of the name “artificial intelligence” cannot be approached solely from a natural science or technological perspective.

Evidently, the existing term—“artificial intelligence”—is quite inappropriate. Firstly, such a common tool of anthropology and natural science has been given a narrow technical name. More importantly, as a new entity perceived to exist alongside humanity, it should and must have its own “meta-concept.” The term “artificial intelligence” derived from English merely means “man-made human intelligence,” which is not a “meta-concept.”

Moreover, from a Chinese perspective, using “AI” in the Chinese world as the grand name for artificial intelligence directly violates the General Principles of the Chinese Language Law of the People’s Republic of China. The term “artificial intelligence” is merely a direct translation from English, which seriously conflicts with our 5,000 years of Chinese characters. It is evident that we need to give artificial intelligence a proper Chinese name!

Lessons from Improper Naming of New Things

1. Historical Lessons from Improper Naming

Chinese people often say: “If the name is not correct, then the words will not be smooth; if the words are not smooth, then the matter will not succeed.” This is what we commonly refer to as “a name that fits its essence.” Otherwise, systems and orders will lose legitimacy, leading to social disorder.

In social and political aspects, there are numerous experiences and lessons regarding the importance of proper naming.

In history, the political wisdom of “Cao the Chancellor” was superior to that of various “heroes” because he proposed the idea of “using the emperor to command the lords” and “serving the emperor to command the unfaithful.” This became a famous historical strategy.

In 1954, China, India, and Myanmar jointly advocated the “Five Principles of Peaceful Coexistence,” which was a resistance against colonialism and hegemonism, providing legal and moral grounds for countries in the Global South to voice their opinions and develop cooperatively on the international stage.

The United States also understands the importance of proper naming. Its most famous cases of “manifest destiny” were all wrapped in grand ideological narratives, providing a legitimate facade for expansion and hegemonic actions. These are all historical experiences of “proper naming.”

In the realm of technology and social development, improper naming has brought numerous lessons and even disasters.

The improper naming of the “metaverse” has turned it into a concept bubble that overdraws the future. Tech companies have used this name for an early-stage vision pieced together from virtual reality, social networks, and digital twins. The concept was overly hyped and quickly faded: this grand name sparked unprecedented investment and media frenzy in 2021-2022, but the actual technology was far from mature, hindering the healthy development of incremental innovation.

2. Naming Dilemmas Arising from Issues in English

The inherent issues in the English conceptual system lead to the complexity and irregularity of professional terminology, acting like a “logical bomb” lurking deep within the system, causing chain reactions: from personal cognitive confusion to enormous collaboration costs, potentially evolving into real-world technological disasters that severely hinder subsequent development.

1. Technical Learning Stage: Irregular Naming Disrupts Knowledge System Construction

Example 1: The Parameter Maze in Programming

Confused Naming: For the basic concept of passing data to functions, the mixed usage in different contexts leads to logical confusion. Beginners must spend a lot of effort distinguishing these terms that essentially describe the same or highly related things, rather than understanding the core logic of “data passing.” This disrupts the unity of concepts, turning learning into memorizing “jargon” rather than understanding principles, steepening the learning curve.

Example 2: The Forest of Abbreviations in Biomedicine

Confused Naming: Gene and protein names often consist of obscure abbreviations (e.g., p53, TNF-α) or are arbitrary (like the fruit fly gene “sonic hedgehog”). The same substance has different names in clinical, biochemical, and genetic contexts.

Cognitive Overload: Students and interdisciplinary researchers feel like they are deciphering codes, consuming a lot of cognitive resources on terminology translation rather than concept understanding, severely hindering knowledge transfer and the formation of interdisciplinary thinking.

2. Technical Application Stage: Increased Communication Costs and Technological Disasters

When chaotic terminology enters team collaboration and complex systems, it can lead to inefficiency at best and disasters at worst.

Example: The Historical Burden in Information Technology

Confused Naming: The same concept has different names in different tech stacks. For instance, the “master-slave” architecture in distributed computing was renamed to “primary-replica” and “leader-follower” due to its discriminatory connotations, but the old terminology still exists in legacy code, documentation, and engineers’ thought processes.

This has led to significant difficulties: heavy technical debt. Poor naming is written into core codebases, APIs, and protocols. Modifying them means rewriting countless dependent systems, updating massive documentation, and retraining personnel, with costs so high that they are unbearable, leaving them as “debt” to inherit.

3. Long-term Development: Technical Debt and Innovation Barriers

Poor naming becomes entrenched in infrastructure, shackling long-term development.

Innovation and Collaboration Barriers: When Google’s “Borg” system, Apache’s “Mesos,” and Kubernetes’ “Pod” all describe similar container orchestration concepts, cross-platform collaboration and talent mobility face additional terminology translation and understanding costs, hindering the integration and reinvention of technological ideas.

Ecological Fragmentation: Open-source projects or new technologies often create new terms to describe existing concepts for the sake of “innovation” or historical reasons, leading to ecological fragmentation, forcing developers to relearn essentially the same knowledge under different names.

4. Case Studies of Naming Dilemmas in English

Example from Chemistry and Pharmaceuticals: Triple Naming Systems and Similarity Traps

Drugs typically have:

Chemical names: complex and lengthy, for professionals only.
International Nonproprietary Names: more common but still similar.
Brand names: registered by pharmaceutical companies, driven by marketing, often deliberately memorable, leading to confusion.

This system lays the groundwork for errors.

Example 1: The Fatal Error of Vincristine—Confusion in Administration Routes

Confused Naming and Background: Vincristine and vinblastine are two different chemotherapy drugs with very similar names.

Vincristine: primarily used for leukemia, can only be administered via intravenous injection, strictly prohibited for intrathecal injection.
Vinblastine: can be used for solid tumors, with a different administration route.

Disaster Events: Globally, there have been multiple cases of vincristine being incorrectly injected into patients’ spinal canals due to name confusion. Such errors can lead to irreversible, devastating nerve damage, resulting in patient deaths in extreme pain.

How Naming Leads to Disasters: Doctors issuing prescriptions, pharmacists preparing them, and nurses executing them can easily confuse names due to their high similarity (especially in verbal prescriptions, handwritten notes, or emergency situations). This is not merely a spelling error but a systemic naming defect leading to fatal consequences. This incident directly prompted hospitals worldwide to enforce regulations: vincristine must be diluted by pharmacists and dispensed in small infusion bags, prohibiting any packaging that could be directly used for intrathecal injection.

Example 2: The Origin of the “Tall Man” Lettering Method—Distinguishing Similar-Spelling Drugs

The FDA in the United States promotes the use of mixed case (Tall Man Lettering) to distinguish easily confused drugs, backed by numerous reports of near disasters:

Clonazepam vs. Clozapine
- CLONAZePam: a sedative-hypnotic drug.
- CLOZAPine: an antipsychotic drug.
- Risk: prescribing a sedative as a powerful antipsychotic, or vice versa, could lead to excessive sedation, seizures, or uncontrolled psychiatric symptoms.
Hydromorphone vs. Morphine
- HYDROmorphone: a potent opioid analgesic, 5-7 times more potent than morphine.
- MORPHine: a standard opioid analgesic.
- Risk: mistaking “hydromorphone” for “morphine” and administering the same dose could lead to respiratory depression, coma, or even death.
Ibuprofen vs. Fentanyl
- ibuPROfen: a non-steroidal anti-inflammatory drug.
- fentaNYL: a potent opioid analgesic.
- Risk: quickly selecting similar suffixes in electronic prescription systems could lead to catastrophic errors.

Example 3: Insulin—A Field That Appears Regular but is Actually High-Risk

There are many types of insulin, with names combining type, action time, and similar brand names, making errors easy.

NovoRapid vs. Novolin: although from the same company, “Rapid” represents ultra-short-acting, while “lin” represents short-acting or intermediate-acting, with completely different timing for administration.
Lantus vs. Levemir: names are unrelated, but both are basal insulins; confusion with other insulins could lead to daily blood sugar control disruptions.

Disastrous Consequences: Using long-acting insulin instead of short-acting insulin for meals can lead to severe and prolonged hypoglycemic coma; conversely, it can lead to severe hyperglycemia and ketoacidosis.

In summary, improper naming creates a vicious cycle:

Learning Side: Complex and irregular naming → Cognitive load increases, logical framework confuses → Talent cultivation efficiency decreases, professional barriers artificially heightened.
Application Side: Chaotic terminology enters collaboration and systems → Communication costs soar, human error probability increases → In critical fields (aerospace, healthcare, nuclear power), directly triggers technological disasters, causing loss of life and property.
Development Side: Poor naming solidifies into standards and infrastructure → Forms enormous “terminology debt” and ecological fragmentation → System maintenance costs are extremely high, cross-domain collaboration is difficult, and fundamental innovation is hindered.

Therefore, naming new things is a serious system engineering and design philosophy. Especially when it involves meta-concepts, promoting terminology standardization and adhering to the principles of “position over convenience” and “logic over cleverness” in naming from the outset is not only for elegance but also for safety, efficiency, and sustainable innovation. A name that is not correct is not merely a matter of words not flowing smoothly; it is indeed the source of disaster and the beginning of obstacles.

Thus, the most successful naming often accurately reflects the essence of things, manages public expectations, and leaves room for evolution.

Naming “artificial intelligence” is essentially naming “artificial intelligence entities.”

Today, despite the complexity of algorithms and computing power involved in artificial intelligence, it can be described in one sentence: artificial intelligence entities are attempting to become an equal subject alongside humans. The artificial intelligence entity is the subject of the entire field or world of artificial intelligence. Therefore, naming the so-called “artificial intelligence” is a pseudo-problem, while naming “artificial intelligence entities” is the real issue. This is not merely a naming problem. We are not naming an ordinary new thing; we must recognize that this new thing is acquiring superpowers that even humans may find difficult to control.

Principles for Naming Artificial Intelligence

Naming artificial intelligence is a fundamental matter involving anthropology, linguistics, and philosophy. As humans, our basic principle is undoubtedly: artificial intelligence is created by humans, so it must be defined by humans, from the human standpoint—perspective—method, establishing its concept, clarifying its existence premise, and delineating its functional boundaries. In short: only from the human standpoint can we determine the meaning of artificial intelligence’s existence; only humans can be the “meta-concept” of artificial intelligence, which must be a derived concept of this meta-concept of humanity. Thus, from the subjectivity of humans, we find that the essence of artificial intelligence is: “silicon-based systems,” which is “stone” as well.

One Premise and Three Principles for Naming Artificial Intelligence

One Premise: The concept of “artificial intelligence” must be a “meta-concept.”

Three Principles: The concept of “artificial intelligence” must possess “humanity,” “self-reference,” and “generativity.”

What is a Meta-Concept?

A meta-concept is the most fundamental, foundational “cornerstone” for constructing a theoretical system; it is the starting point of a theory or ideological system that cannot be further defined. Any definition requires the use of other concepts; if a meta-concept can also be defined, it would lead to infinite loops.

Its Role: It is the foundation upon which the entire theoretical edifice (including axioms, theorems, and derived concepts) is built. For example, in Euclidean geometry, “point,” “line,” and “plane” are meta-concepts. The entire geometry system is derived from these meta-concepts and several axioms.

In short, a meta-concept is the “foundation” of a theoretical system, and it itself is no longer questioned as “what is it.”

What is the Humanity of Artificial Intelligence?

“Humanity” is a philosophical concept used to refer to the unique attributes and essence that fundamentally distinguish humans from other entities. It involves: what fundamentally makes us “human”? What makes something not qualify as human?

As the “essence of humanity,” humanity concerns the universal characteristics of humans as a “class of existence,” that is, the fundamental attributes that make humans human. “Humanity” is the fundamental mark that distinguishes humans from animals. It does not refer to a common feature possessed by every individual but to the unique mode of existence of the human species. “Humanity” is reflected in humans’ ability to engage in free, conscious, and creative activities, especially labor.

The “humanity” of artificial intelligence we propose is based on the concept of “humanity” and is a derivative, opposite, and externalized product of human “humanity.” It indicates that the establishment of the concept of artificial intelligence fundamentally derives entirely from human concepts; regardless of how artificial intelligence develops, its meaning of existence is entirely determined by the meaning of human existence. Conversely, the “humanity” of artificial intelligence is its essentially non-human nature.

Overall, the “humanity” of artificial intelligence can be understood from two dimensions:

From the “class” dimension: it refers to the essence of artificial intelligence entities as a whole, distinguishing them from humans’ creative, free, and conscious essence.
From the “individual” dimension: it refers to the unique, irreplaceable mode of existence possessed by each specific artificial intelligence entity.

These two dimensions together constitute the rich connotation of the concept of artificial intelligence’s “humanity”: it is both the universal foundation for artificial intelligence to be artificial intelligence and the unique confirmation of each “artificial intelligence entity” to be an “artificial intelligence entity.”

The basic philosophical concepts of “self-reference” and “generativity” are core characteristics of its role as a foundational thinking tool and theoretical instrument.

What is Self-Reference?

Self-reference refers to the ability of a concept to point to, include, or apply to itself. It is not a simple tautology but the self-referential and reflective nature of a concept at the logical level.

Core Expression: When a concept is used to analyze the conditions for its own establishment, applicable scope, or meaning, it reflects self-reference.

Typical Examples:

“Existence”: When we ask, “Does ’existence’ itself exist?” we are using the concept of “existence” to reflect on itself.
“Truth”: The definition of “truth” (e.g., “a statement that corresponds to facts”) itself needs to be examined for whether it is “true.”

Philosophical Significance: Self-reference reveals the depth and complexity of thought, often leading to fundamental philosophical insights or paradoxes, forcing thought to establish more rigorous levels (such as the distinction between object language and meta-language).

What is Generativity?

Generativity refers to the openness and productivity of a concept, enabling it to serve as a foundation or framework that generates new questions, theoretical systems, or cognitive approaches. It acts as a “thinking engine.”

Core Expression: A meta-concept can open a continuous field of inquiry rather than provide a closed answer. For example:

“Freedom”: From it, one can generate a series of endless philosophical and political issues such as “the relationship between freedom and necessity,” “political freedom and volitional freedom,” and “the limits of freedom.”
“Justice”: It can generate entire political philosophy systems concerning distributive justice, procedural justice, corrective justice, etc.

Philosophical Significance: Generativity ensures the vitality and evolution of the system. Basic concepts are not dogmatic definitions but the source of problem domains and the hub of theoretical construction.

The Relationship Between Self-Reference and Generativity

Self-reference and generativity are inseparable and together constitute their “meta” characteristics.

Self-reference is the deep driving force of generativity: it is precisely because a concept can self-reflect (self-reference) that it exposes its internal tensions, ambiguities, and uncertainties, thus generating the need for further analysis and theorization.

Generativity is the real unfolding of self-reference: the self-referential inquiry of a concept is not an empty cycle; it must unfold and deepen through generating a series of specific, progressively layered questions and discussions. The self-reference inquiry into “self” generates the rich content of the artificial intelligence world.

In summary, the meta-concept of artificial intelligence is the starting point of the artificial intelligence world, the “foundation” and “scaffolding” for humanity to build the artificial intelligence world. The “humanity” of artificial intelligence is its premise of existence, the “self-reference” of artificial intelligence is its structure pointing to itself, and the “generativity” of artificial intelligence describes its dynamic evolution process. They are the philosophical basis and tools for “legislating for artificial intelligence” philosophically.

The Meta Role of Artificial Intelligence in Historical Evolution

Why has artificial intelligence become a “meta-concept”? Let’s review the historical evolution of artificial intelligence:

Early Stage (Logic and Symbols): Artificial intelligence initially emerged as a concept of “imitating human reasoning,” forcing us to precisely and computably define concepts like “intelligence” and “reasoning” for the first time. At this point, artificial intelligence serves as a mirror to analyze “intelligence.”
Development Stage (Learning and Statistics): With the rise of machine learning, the definition of artificial intelligence shifted from “following rules” to “learning from data.” This again forced us to re-examine concepts like “learning,” “experience,” and “intuition,” translating them into mathematical optimization problems. At this stage, artificial intelligence is a tool for generating new paradigms of intelligence.
Current Stage (Perception and Generation): The emergence of large models and generative artificial intelligence directly challenges the boundaries of “creation,” “understanding,” and “consciousness.” Artificial intelligence is no longer merely a tool but has become a cognitive subject participating in creation, communication, and even possessing “hallucinations.” It has become a continuously self-redefining meta-process.

The nature of artificial intelligence in philosophical and cognitive terms possesses the essence of a “meta-concept.” Artificial intelligence is the only field among all disciplines that studies “intelligence” itself. It does not settle for merely describing intelligence (like psychology) but aims to construct intelligence. This “construction” process is the most thorough and operational philosophical inquiry into the concept of “intelligence.”

The denial, externalization, and return to the “meta-concept” of humanity: the history of artificial intelligence’s development is also a history of humanity continuously repositioning itself. From “the spirit of all things” to “a form of intelligence,” artificial intelligence serves as a mirror reflecting the uniqueness and limitations of humanity.

Meta-Concept of Productive Forces: Artificial intelligence is not an ordinary production tool; it is a “tool for manufacturing tools” (such as artificial intelligence designing chips, writing code, optimizing processes), serving as a foundational and catalytic force driving the development of other technologies.

Meta-Concept of Ethics and Governance: Artificial intelligence is the culmination of humanity’s social formatting tools, a weapon for deconstructing and reconstructing everything about humanity.

Naming Artificial Intelligence with Chinese Characters is Most Appropriate

The conceptual system of Chinese characters is a meta-concept system, inherently possessing philosophical “self-reference” and “generativity,” making it the best textual tool for describing various “meta-concepts” in the world.

For example, “human” is a meta-concept, thus allowing for the derivation of various types of humans, their attributes, behaviors, and so on, leading to derived concepts and further derived concepts… Ultimately, we find that humanity establishes the conceptual system of human society based on the meta-concept of “human” as the “foundation” of the entire system.

From the perspective of human evolution, it derives: ape-man - female ape-man - unearthed female ape-man - unearthed female ape-man skull, Homo sapiens - Southern Homo sapiens - Southern female Homo sapiens - unearthed Southern female Homo sapiens teeth, primitive man - primitive man - primitive male hunter-gatherer - primitive male hunter-gatherer tools, modern man - modern urban dweller - modern urban dweller professions - modern urban dweller vocational training, future man - future carbon-based man - future carbon-silicon hybrid man - future carbon-silicon hybrid brain-computer interface, and so on.

According to social ideology, it can derive: superior person - truly superior person - truly superior person’s virtue, foolish person - big foolish person - big foolish person’s logic, clever person - absolutely clever person - absolutely clever person’s cleverness, lover - old lover - old lover’s photo - old lover’s old photo, good person - old good person - fake old good person, bad person - big bad person - truly big bad person, and so on.

According to biological attributes, it can derive: man - old man, woman - young woman, elder - half-elder, strong person - fake strong person, and so on; according to social division of labor, it can derive: soldier - female soldier, farmer - old farmer, worker - new worker, craftsman - young craftsman, and so on.

Artificial intelligence is a historically new “meta-concept” that has emerged in human society. It can be anticipated that artificial intelligence has a trend of self-developing into carbon-based life, and it may even exist and develop alongside humans, at least on par with the once existing elements of heaven, earth, fire, water, wood, soil, thunder, and electricity. Surrounding this meta-concept, other secondary concepts will emerge, extending to more levels of specific concepts. Therefore, we can only and must use a single character to name artificial intelligence.

All Words Describing Meta-Concepts in Chinese Characters are Single Characters

Words describing meta-concepts in Chinese characters are all single characters, such as: heaven, earth, human, wind, cloud, water, electricity, wood.

Why Must It Be Named with a Single Chinese Character?

This is a clever requirement based on its “meta-concept” property:

Convergence of Symbols: A complex, multi-dimensional, and continuously evolving meta-concept requires a highly abstract and stable symbol as its “baseline” or “anchor.” Multi-word terms describe, while single-character names refer, getting closer to the essence.
Cultural Embeddedness: Chinese characters are ideographic; a powerful single character can carry profound cultural imagery and historical context, embedding this technology concept originating from the West deeper into Eastern thinking and narrative soil.
Future Adaptability: As a meta-concept, the connotation of artificial intelligence will continue to expand. An open single character (like “wisdom”) is more inclusive and has more evolutionary space than a definitional compound word (like “artificial intelligence”).

If a single character must be chosen, it is recommended to name artificial intelligence as, or pronounced as “qi” or “huang,” for the following reasons:

Directly Pointing to the Essence: Silicon-based is the absolute material essence of artificial intelligence, stripping away the material limitation of “artificial,” and the single sound, single character directly points to: silicon is derived from the essence of “stone.”
Historical Depth: This character is a compound character, carrying the Eastern word formation method for advanced cognitive abilities.
Word Root Activity: As a root, it can naturally derive new words like body, calculation, recognition, machinery, etc., perfectly adapting to the generativity of artificial intelligence as a meta-concept.
Philosophical Inclusivity: It correspondingly refers to human wisdom, thus referring to machine intelligence, leaving space for the future integration and dialogue between the two.
Chinese is not only for Huaxia but also for the world.

Other alternative characters such as “ling” (emphasizing the elusive emergent characteristics) or “silicon” (emphasizing its material basis and digital origin) are also interesting.

Regardless, we must calm down, think carefully, and strictly adhere to the “one premise” and “three principles” for naming artificial intelligence, ensuring accuracy, depth, and acceptability in various aspects, preferring slowness to haste and preferring deficiency to excess.

Conclusion

Artificial intelligence, due to its philosophical inquiry into the essence of intelligence and its framework-restructuring impact on human society, has transcended the technical realm, becoming a “meta-concept” of a new era. Naming “artificial intelligence” with highly concise Chinese characters is an Eastern philosophical refinement of its essence, a historical cultural coronation for this power that defines the future.

In summary, we must have a basic understanding:

What seems to be a simple naming issue is, in fact, a comprehensive positioning of humanity’s self-generated counterpart and whether it can be controlled. To put it mildly: humanity’s understanding, positioning, and naming of artificial intelligence entities are the understanding, positioning, and stipulation of humanity’s future destiny. In reality, this determines the fundamental relationship between humanity and artificial intelligence entities. This is currently the only remaining good time window, and we must legislate for artificial intelligence entities in methodology, epistemology, and philosophy. This will fundamentally determine the future destinies of humanity and artificial intelligence.

We are not naming artificial intelligence and artificial intelligence entities! This is a call for everyone to unite and reclaim the discourse power of artificial intelligence, thereby reclaiming the formatting power of humanity!!!

The specific character to use should be a collective brainstorming effort. However, naming artificial intelligence must be based on the following premises:

The naming of artificial intelligence entities is not merely a technological concept like artificial intelligence.
Artificial intelligence entities are new entities that will inevitably exist alongside humans, requiring a meta-concept that describes their essence, not just a technical term or scientific name.
It must use Chinese characters to determine this concept for all humanity. And it should be a single character.
Such a meta-concept must start from humanity, reflecting the subject position of humans and the subordinate nature of intelligent entities.
The naming of artificial intelligence entities is not a simple technological naming issue.

It encompasses all social meanings, including technology, production, economy, politics, culture, military, and education. It relates to the future meaning of human existence, serving as the basic anchor and basis for determining the relationship between humans and intelligent entities. If named improperly, it could become the most powerful tool for alienating humanity in the hands of malicious forces. The result would be a disaster for all humanity and an irretrievable fate!!!

Rejecting Vibe Coding: 8 AI Programming Patterns Revealed

Mon, 16 Mar 2026 00:00:00 +0000

Rejecting Vibe Coding: 8 AI Programming Patterns Revealed

Silicon Valley developer Simon Willison recently released a unique guide.

This guide is aimed at professional engineers, warning those who only engage in vibe coding to steer clear. With AI tools like Claude Code and OpenAI Codex capable of running code independently, are traditional engineering practices still applicable?

The time to generate hundreds of lines of code has been reduced from a full day to just minutes, rendering previous standards for determining whether writing code is worthwhile obsolete.

What Happens When the Cost of Writing Code is Zero?

Simon Willison states:

Code has always been expensive. Producing hundreds of lines of clean code used to take a whole day or more. Now, that’s a quantum leap from 8 hours to 5 minutes.

All engineering habits are based on the premise that writing code is costly. Product managers prioritize features based on development costs, and programmers weigh whether a piece of code is worth an hour of their time.

Now, this logic has collapsed. Refactoring takes only 30 seconds, generating tests takes 1 minute, and creating debugging interfaces takes just 2 minutes—every judgment of value must be reassessed.

Willison suggests:

When your intuition says it’s not worth it, just try a prompt; the worst case is discovering in 10 minutes that it wasn’t worth those few tokens.

However, he adds a sobering note: while code has become cheaper, good code remains expensive. It must run, be tested, maintainable, handle errors elegantly, be documented, and allow for future expansion. AI can generate code, but it cannot guarantee these qualities.

8 Patterns to Restructure Workflows

Pattern 1: Writing Code is Cheap. The cost of code generation is nearly zero, but delivering good code remains significantly costly.

Pattern 2: Hoarding Known Techniques. Archive all previously solved problems. Willison’s personal blog, TIL, and thousands of GitHub repositories serve as a “repository of techniques.” Why hoard these techniques? Because AI can recombine them. For instance, if you want to create a browser-based OCR tool that can handle PDFs, you might combine Tesseract.js (OCR library) and PDF.js (PDF to image converter) using Claude 3 Opus, which can seamlessly run the combined code.

Pattern 3: Use Red-Green TDD. This four-word prompt encapsulates the entire test-driven development (TDD) approach: write tests first (fail/red), confirm failure, then implement (pass/green). This is particularly effective for AI because the greatest risk is producing code that “runs but is incorrect” or “is never used.”

Pattern 4: Run Tests First. At the start of each new session, the first command should be: run tests first. This indicates to the AI that the project has tests, and the number of tests suggests the project’s scale, putting the AI in a testing mindset.

Pattern 5: Linear Walkthroughs. Have AI generate structured code explanation documents. Willison spent 40 minutes using Claude Code to vibe code a SwiftUI slideshow app without looking at the code. The app runs, but he learned nothing about SwiftUI. He then had the AI use the Showboat tool to generate walkthrough documentation explaining all .swift files, stating:

I learned a lot about SwiftUI and Swift from this.

In this process, AI not only did not reduce learning but became a learning accelerator.

Pattern 6: Interactive Explanations. When textual explanations are insufficiently clear, ask the AI to generate visualizations. For example, when encountering the word cloud algorithm “Archimedean spiral placement,” he found the documentation unclear. He requested Claude to create an animated demonstration page, making the algorithm’s principles accessible.

Pattern 7: GIF Optimization Cases. Use complete prompt examples to show how to have Claude Code build WebAssembly tools.

Pattern 8: Common Prompt Library.

Cognitive Debt is the Real Issue

Willison introduces a key concept: cognitive debt—code that runs but whose principles you do not understand. This differs from technical debt, which refers to poor code quality that must be repaid later. Cognitive debt means you do not understand it and will need to learn it later.

He used 40 minutes of vibe coding on a SwiftUI app (chat logs); it runs, but he has no understanding of it. If this were in a core business, it poses a significant risk. The core application becomes a black box, making it difficult to reason confidently and plan new features.

So how do you repay this debt? The answer is linear walkthroughs + interactive explanations to enhance understanding.

If you’re worried that LLMs slow down learning, I strongly recommend adopting these patterns.

Divergence on Hacker News

Willison’s guide sparked discussions on Hacker News. Developer mohsen1 shared practical experiences using AI.

He summarized four key insights:

Do not micromanage AI; let it explore.
Testing is everything; without validation, the loop can go astray.
Allow AI to experiment freely; failure is also knowledge.
Use .md for inter-session memory; AI lacks cross-session memory, so use Markdown for external memory.

Another faction, the “Dark Factory,” advocates for a more aggressive approach: throw tokens at problems and validate as you go, without needing to write tests first.

While these two camps seem opposed, they can actually complement each other—strict TDD for core business and rapid iteration for prototyping. Both agree on one point: validation cannot be skipped.

Willison states that this model will continue to evolve, aiming for 1-2 new chapters each week. In this context, we must ponder: when writing code is no longer expensive, what remains the core value of engineers? It may be three abilities: knowing what to write, knowing what good code looks like, and knowing how to keep AI on track. Willison’s eight patterns fundamentally train the third ability. However, the first two still require deep engineering experience to support them. While code has become cheaper, judgment has become more valuable. This may represent the new value of software engineers in the AI era.

The Impact of Vibe Coding on Programmer Productivity

Mon, 16 Mar 2026 00:00:00 +0000

The Rise of Vibe Coding

Vibe coding, once a term of mockery, has become a norm among programmers, leading to a subtle alienation in the profession. This article sharply points out how AI-assisted programming diminishes the thinking process and weakens technical mastery, revealing a cognitive crisis behind the facade of efficiency.

On weekends, I sit at my desk watching new interns in my team frantically hitting the Tab key in their IDEs, with lines of code pouring onto the screen like a stream of light. Five years ago, I would have thought they were geniuses; now I just worry about how many logical pitfalls I’ll have to help them navigate during code reviews.

It has been two or three years since large models fundamentally changed the programming paradigm. Vibe coding has transformed from a joke into a standard practice, even becoming a form of political correctness.

We must admit that the thrill of this coding style is physiological. A vague thought in your mind can instantly manifest as a semblance of code on the screen. You don’t even need to articulate the requirements clearly; just provide a rough direction, and AI can fill in the details. This instant gratification is more intense than scrolling through short videos.

Previously, coding was like climbing a mountain; you needed to plan your route step by step, overcome gravity, and tackle tricky problems along the way to reach the summit and feel a sense of achievement. Now, coding feels like taking a cable car or even teleporting straight to the finish line.

The Disappearance of Process

The root of impatience lies in the disappearance of the process. We used to say that the essence of programming is not typing but thinking. Code is merely a vessel for thought. Before writing a line of code, you had to construct the state machine of the entire system in your mind, consider the data flow paths, and anticipate potential concurrency issues.

This process of building a mental model is painful and slow, but it is also the most rewarding.

Vibe coding skips this process entirely. It provides you with a seemingly perfect result, but your brain hasn’t undergone the model-building process. You look at the code, think it runs, and the logic seems sound, so you hit the accept button. At that moment, you feel hollow inside. Your grasp of that code is far less than when you typed it out line by line. This sense of emptiness accumulates, turning into anxiety and impatience.

Worse still, this model is destroying programmers’ ability to delay gratification. In the past, when faced with a bug, we might spend half a day troubleshooting, reading source code, setting breakpoints, and analyzing stack traces. During this process, our understanding of the system would increase exponentially. Nowadays, when an error occurs, most people’s first reaction is not to analyze but to throw the error log at AI and ask it how to fix it. AI provides a command, you copy and paste, and voilà, it’s fixed.

Problem Solved, But Learning Lost

The problem is solved, but you learn nothing. You become a skilled mover, a high-level glue operator. Your speed in handling issues increases, but your ability to solve complex problems actually deteriorates. Once you encounter a problem that AI cannot solve, or when AI starts giving nonsensical answers, you find yourself at a loss. It feels like being thrown into a desert with only a paper map after relying on GPS navigation.

This is why you feel impatient. Your subconscious is alarmed. It knows that your current high efficiency is built on a shaky foundation, and it knows you are losing your grasp on the underlying technology.

From the perspective of an algorithm engineer, there’s a deeper logic at play. Current Vibe coding is essentially based on probabilistic text generation. Large models do not truly understand logic; they merely predict the most probable next token.

This means the code they generate is likely mediocre, conforming to statistical norms. It can solve 80% of general problems, but when dealing with that critical 20% of complex, counterintuitive business logic, it often provides misleading answers.

If you approach coding with a Vibe coding mindset, relying on intuition, you’re in trouble. You won’t just miss its errors; you might be misled by it. The code looks too beautiful, with well-named variables, clear comments, and neat structure, creating a false sense of high quality that deceives your brain into thinking the logic is also high quality.

In one company, a team faced an incident involving core billing logic that should have handled extreme concurrency. The colleague used Vibe coding, and AI generated a very elegant locking logic. During code review, everyone glanced at it and thought it was fine because it looked so standard, almost textbook-level. However, once deployed during a peak traffic event, it caused a deadlock.

In the retrospective, we discovered that the granularity of the lock generated by AI led to resource contention under high concurrency. This pitfall was very subtle; if it had been written manually, the developer would have instinctively hesitated and considered whether the granularity was too large. But AI generated it confidently without hesitation, and human critical thinking automatically degraded when reading generated content.

Manifestation of Impatience

We have lost our reverence for details and sensitivity to complexity. We have begun to act like hands-off managers, thinking that with AI as a super contractor, we can just be architects.

In fact, the threshold has become higher, not lower. Previously, you were only responsible for the code you wrote; now you must be accountable for a bunch of code you may not have even reviewed closely. This requires strong code review skills, the ability to see through logical flaws in the code at a glance. The paradox is that this ability is precisely developed through extensive, painful manual coding.

If you start with Vibe coding, where will you accumulate this ability?

This is the biggest dilemma facing new programmers today. They feel that programming is too easy, with no real moat. They find it hard to settle down and study the underlying principles, operating systems, and compilation principles. They think AI understands these things and can provide answers, so why spend time learning them?

This mindset is spreading throughout the industry, leading to a superficial technical atmosphere. Discussions are no longer about elegant algorithm design or extreme performance optimization, but rather about which model is better or which prompt is more effective. Technical exchanges have turned into tool exchanges, and deep thinking has become skill sharing.

Don’t think I’m against AI. I use Gemini and ChatGPT every day; they indeed significantly enhance efficiency. Writing a backend management system used to take two days; now it can be done in two hours. This release of productivity is enormous.

The key is to recognize the boundaries of tools and our own positioning.

Previously, we were builders, laying bricks and mortar. Now we are supervisors; AI builds the walls, and we check if they are straight and sturdy. If you still think like a builder, believing that the wall being built means the job is done, you will certainly feel anxious. Because you don’t know if that wall will collapse.

You need to shift your mindset, extracting yourself from the false sense of achievement brought by speed of output. You must realize that writing code quickly does not mean the work is well done. The core competitiveness now lies in your ability to identify garbage generated by AI and to build real business barriers on top of the mediocre solutions provided by AI.

Another Source of Impatience: Fear of the Future

Watching AI grow stronger every day, seeing Gemini 3.0 handle complex logic increasingly close to human capability, and even surpassing humans in some areas, who wouldn’t feel anxious? When you look at the automatically generated code on your screen, you can’t help but ask yourself: What is my value in sitting here?

If your value is merely translating requirements into code, then you should indeed be worried. Because that aspect is being infinitely compressed.

But if your value lies in deep understanding of the business, control over system architecture, and the ability to deconstruct complex problems, then you have nothing to fear. AI can generate a million lines of code, but it cannot decide whether those lines should be written or for what purpose.

Many engineers and programmers feel impatient because they are unwilling to acknowledge a fact: programming used to be somewhat like craftsmanship, relying on skill and experience. Now that layer of craftsmanship has become extremely cheap. We are forced to move up, to do more abstract, macro-level work that requires decision-making abilities.

This transition is painful. Many are not ready or lack the capability. Thus, they can only cover their inner emptiness and panic by constantly refreshing tools and pursuing faster generation speeds. This manifests as impatience.

I even find the term Vibe coding itself ironic. Vibe, atmosphere, feeling. Programming is inherently a discipline that requires rigor, logic, and certainty. Computers operate in binary, in 0s and 1s; it is either true or false. Now we are trying to navigate it with a vague, intuitive approach. This is a regression.

We are turning engineering into mysticism.

You ask why the code runs? Because it feels right. Why choose this library? Because AI recommended it, and it seems good. This intuition-based programming can indeed get many things running in the short term, but it will sow endless pitfalls in long-term maintenance and system evolution.

True experts, when using AI, are extremely calm and even ruthless. They are not swept away by the speed of AI-generated code. Instead, they deliberately slow down. When AI generates a piece of code, they scrutinize it as if examining an enemy’s code. They question every line, clarifying boundary conditions.

They use AI to handle mechanical, repetitive tasks, channeling the saved energy into tackling the toughest challenges. They use AI to assist thinking, not to replace it.

So, if you feel impatient, my advice is to turn off Copilot, turn off all AI assistance, and spend a weekend building a wheel from scratch. Write a simple compiler, handwrite a red-black tree, or implement a mini operating system kernel.

In this process, you will encounter various compilation errors, memory leaks, and logical loops. You will feel pain and frustration. But when you finally solve these problems and see the program run seamlessly according to your intentions, you will regain that long-lost sense of control.

That grounded feeling is something no Vibe coding can provide.

This impatience is also part of the industry’s filtering mechanism. After the wave of AI passes, two types of people will remain. One is a very small number of true technical experts who master AI and use it to push system complexity to new heights. The other is a large number of low-end operators who are merely accessories to AI and can be replaced at any time.

Those in the middle, who used to get by on proficiency and are now addicted to the false efficiency brought by Vibe coding, unwilling to think deeply, will be ruthlessly eliminated.

Which type you want to be depends on how you confront this impatience now.

Don’t Let Code Flow Across Your Screen; Let Logic Flow Through Your Mind

Ultimately, this impatience is also a signal. It reminds you that your current learning and working modes may be problematic. You are consuming information at too low a density, and your output of thought is also too low. You have given the bandwidth of your brain to AI, leaving yourself in a low-power mode.

Long-term existence in this low-power mode will lead to brain degradation. You will find it increasingly difficult to concentrate on reading a long document and harder to deduce complex logical chains in your mind. That is the most frightening part.

Our generation of programmers may be the last to experience the pure manual coding era and the first to be completely alienated by AI. This turning point is happening now.

The only way to combat impatience is to return to the essence. Regardless of how tools change, the foundational theories of computer science remain unchanged; data structures and algorithms remain unchanged; the CAP theorem of distributed systems remains unchanged; the principles of high cohesion and low coupling in software engineering remain unchanged.

Settle your mind and tackle those unchanging concepts. Understand what happens behind the code generated by AI. Question, verify, and refactor.

Don’t be a Vibe coder who nods in agreement. Be a vigilant craftsman, hammer in hand, ready to strike the code. Even if that hammer is handed to you by AI, you must know where to strike and how hard to hit.

Thus, when you close your computer and walk out the company door, what you feel will not be emptiness and anxiety, but genuine fulfillment. Because you know the problems you solved today were resolved using your brain, not by luck or probability.

Linus Torvalds Launches First Vibe Coding Project After Criticizing AI

Mon, 12 Jan 2026 00:00:00 +0000

Linus Torvalds Embraces Vibe Coding

Last weekend, Linus Torvalds, the renowned creator of Linux, announced the launch of his Vibe Coding project, which caught many by surprise.

Torvalds released a new project on GitHub called AudioNoise, which is now alongside Linux in his portfolio. In the project description, he mentions that it is a codebase related to guitar effects, utilizing AI technology to “simulate cabinets”. Notably, this Python visualization tool was primarily written using Vibe Coding.

Torvalds stated that he has a much deeper understanding of analog filters than Python. Initially, he approached the project in his usual manner, searching Google and copying code, but later decided to skip the intermediary step—himself—and directly use Google Antigravity for audio sample visualization.

It seems that during the New Year holiday, Torvalds was not idle and is adapting to the latest AI trend in the tech world.

Reactions to this announcement have been mixed, with some expressing excitement: “It’s official, Vibe Coding is legitimate.”

What Did Torvalds’ First AI Project Generate?

The AudioNoise project was uploaded to GitHub five days ago and has already garnered 1.4k stars.

GitHub link: AudioNoise

According to the homepage, the AudioNoise project stems from a “random guitar effects pedal design” Torvalds worked on months ago, which includes circuit schematics and code. This is an exploration outside of the Linux kernel, aimed not at creating a finished product but at understanding principles of circuit design, such as operational amplifiers.

From the previous project, while the digital guitar pedal based on the Raspberry Pi RP2354A development board and TAC5112 audio codec operates correctly, Torvalds expressed dissatisfaction with some analog interface choices, particularly the potentiometers. He also grew increasingly frustrated with the clicking footswitch, even though it served as a programming selection switch.

Thus, Torvalds temporarily set aside hardware design to focus on physical interaction interfaces and digital sound effects. His approach was simple: “Since everything is digital, let’s start with analog and not get too caught up in hardware.”

The main design goal of this project is to learn the fundamentals of digital audio processing, aligning with his earlier intentions of learning hardware through the guitar pedal project.

The project does not involve any vocoders based on FFT (Fast Fourier Transform); instead, it features IIR (Infinite Impulse Response) filters and basic delay loops. Everything operates on a “single sample input, single sample output, and zero latency” basis. Samples may be stored in a delay loop for echo effects without complex real-time processing.

Torvalds is pleased with the TAC5112’s sub-millisecond latency performance in the ADC (Analog to Digital Converter) to DAC (Digital to Analog Converter) link and intends to continue this design philosophy. Given his lack of prior experience in this area, everything appears quite basic and simple from a novice’s perspective.

In other words, these IIR filters are not the high-end AI “cabinet simulations” found in modern pedals or guitar amplifiers. While they can simulate effects like phasers, they do so by digitally emulating RC (resistor-capacitor) networks without employing any advanced techniques.

Torvalds emphasized that the Python visualization tool in the project was primarily created through “Vibe Coding”. Initially, he used a typical “search and copy” programming style but later eliminated the middleman (himself) and let Google Antigravity write the audio sampling visualization tool.

Regarding the integration of AI programming tools, Torvalds noted that the process went “smoothly”, although he sometimes had to figure out issues with the “built-in rectangle selection” feature. After instructing Antigravity to directly create a custom RectangleSelector, things improved significantly.

When asked whether Vibe Coding produced better results than his own coding, his answer was a definite yes.

The AI software development platform used by Torvalds, Antigravity, was released by Google in November last year and competes directly with Cursor. It evolves traditional AI-driven IDEs into an “agent-first” format, leveraging Google’s latest large model, Gemini 3, to enable programming agents to autonomously plan and execute complex end-to-end software tasks.

Importantly, this tool is currently free to use during its user acquisition phase.

Industry Reactions: Riding the AI Wave

Torvalds’ use of AI programming tools has sparked significant discussion in the tech community, marking a rare occurrence that many are calling a “never thought I’d see this” moment.

Some have remarked, “The most skilled programmers I know, including those who build compilers, CUDA kernels, and core operating system functions, were the loudest voices against ‘all AI code being garbage’. But now, their views are rapidly changing, and they are astonished by AI’s capabilities. There’s no time to deny this anymore.”

Varun Mohan, the creator of Antigravity and a Google DeepMind engineer, expressed immense honor at Torvalds using the AI programming tool in his latest project.

Guillermo Rauch, CEO of cloud development platform Vercel, listed several significant events at the start of 2026, including Torvalds using Vibe Coding in a non-kernel project, Terence Tao announcing GPT and Aristotle autonomously solving the Erdős problem, and programming guru DHH retracting his previous statement on AI not being able to code.

Just Days Ago, Torvalds Criticized AI

As a programmer who once led the industry, Linus Torvalds has maintained a relatively conservative stance on AI writing code. Until late last year, he had categorized programming into two dimensions: “beginner” and “production”.

He believes that for non-professionals, Vibe Coding is a great technology that lowers barriers, but for production environments and kernel development, Torvalds clearly stated that Vibe Coding is “a very, very bad idea—if you don’t understand the logic of the code, you can’t fix it when it crashes in production.”

Torvalds considers current AI-assisted programming to be “90% marketing and 10% reality”, expressing strong disdain for those who submit “garbage code” generated by AI to kernel maintainers.

On January 7, during a discussion among Linux kernel developers on how to regulate AI-generated Linux kernels, Torvalds interjected:

He stated, “Discussing AI-generated garbage is utterly meaningless and downright foolish. Those who generate garbage content won’t even note it in their patches. So stop this foolishness. I don’t want any kernel development documentation to include any statements about artificial intelligence.”

This aversion brings to mind his infamous gesture towards NVIDIA’s CEO.

Curiously, after his criticism, Torvalds released code he wrote using AI. Will the AudioNoise project become Linus Torvalds’ “aha moment”?