Friday, April 24, 2026

Today's mess has exposed three absurd realities in the AI industry: engineering laziness is prevalent, safety streaking carnival, and capital bubble blowing competition.

Let's start with the project lying flat. The GitHub trend list is a large-scale performance art scene: andrej-karpathy-skills uses a single file to teach Claude to write code, which is essentially patching mentally retarded models; the Caveman project is even more outrageous, relying on primitive grammar to save tokens is like loading a donkey wheel on a sports car. What does it mean? The code generated by large models is simply not suitable for the production environment, and developers are forced to use evil ways to fill the holes. What's even more impressive is the emergence of Agent Context, a "reference project stitching device" from Product Hunt. Do you really regard AI as a code copying contractor? Combined with V2EX, we are still arguing about cross-domain issues-2026, brothers, front-end engineering is still eating shit!

The safety circle is completely magical. The news that Bitwarden CLI was breached by the supply chain and "AI security guards" such as Vector/Skill Inspector on Product Hunt were on the same day, full of sarcasm. On the one hand, basic tools are connected with thunder, and on the other hand, startups dare to sell security solutions with large model filtering tokens. Leaked system prompts on GitHub CL4R1T4S are flying all over the place. Hackers are now easier to steal AI than passwords. HuggingFace's privacy-filter project is the funniest-using AI to clean up AI's mess. Do you believe that someone will use confrontation samples tomorrow to make it vomit sensitive data?

Watching capital drama masters make my skull ache. The Chinese circles boast that thousands of miles of intelligent driving have installed 460,000 vehicles in half a year. In the press release, the shouts of "AI speed" and "intelligent driving duo" are loud, and the key indicator "accident rate" is not mentioned. Musk in North America next door plays even harder: SpaceX's IPO document directly writes "I am dictatorial in charge of the company," and Tesla copied the capital routine before it even wiped out its shit basin. The most magical thing is the "AI-free tractor" at the top of Hacker News-when all VCs are investing in AI to farm land, farmers use mechanical intelligence reduction solutions to save 50% of costs. There is also Netflix's repurchase of 25 billion shares, Hengdian Film and Television's transformation of IP + AI full link... Why don't you just change your profession to printing money?

The truth about entering AI now is: bottom-level players are acting as poop officers for large models (see token in Caveman Province), middle-level players are being chased by security flaws (Bitwarden victim +1), and top-level players are busy using PPT to harvest the secondary market (I am optimistic that Musk will only demonstrate once). Suggest to ordinary developers: The prompt leaked by GitHub is more useful than most paid courses. Open source AI security tools are more reliable engines that write rules themselves. As for the bubble blown by capital-look at Product Hunt's Free LLM API. Is it really a charity? Believe it or not, I will limit the current to you tomorrow!

Among today's technological signals, the most interesting trend is the struggle between fragmentation and anti-fragmentation of the AI development paradigm. On the one hand, we see the popularity of extreme fine-tuning techniques such as the Claude Code skill library and the Caveman project, and developers are using an almost behavioral art approach to tap the marginal benefits of LLM; on the other hand, OpenAI has released GPT-5.5 and Qwen has continuously launched the 35B-level model, which implies that the capability boundary of the basic model is still expanding rapidly. This contradiction reveals a key turning point: When AI development requires both piecing together scattered techniques and relying on super models, the tool chain at the middle level is growing explosively. For example, docker-based agents such as ML Intern and team coding session management tools such as Stash are essentially filling this emerging tool gap.

Another hidden but important signal is the politicized turn of the technology industry. Palantir employees 'doubts about their own moral positioning, SpaceX's insistence on retaining absolute control of Musk, and even the CL4R1T4S project that leaked prompts from major AI systems all point to the same problem: technological power is reshaping social rules. What is particularly noteworthy is that these disputes no longer remain at the level of public opinion, but are directly written into the corporate governance structure (such as SpaceX's IPO documents clearly stipulate the composition of the board of directors) and the technical architecture (such as the official release of the privacy filtering model). This "institutional confrontation" may spawn two by-products: one is to spawn more compliance-as-a-service products similar to Vector by zauth, and the other is to accelerate the parallel development of China's AI ecosystem, just like Hengdian Film and Television's full-link operation transformation."Deepening AI technology empowerment" is particularly emphasized.

The most dangerous new trends lie in seemingly harmless supply chains. The breach of Bitwarden CLI and the concentrated appearance of AI security products (MindFort, Skill Inspector) on Product Hunt suggest that the technology stack in the AI era is forming a new chain of vulnerabilities. When the code assistant can learn recursively and the development process relies deeply on third-party agents, traditional Code Audit and dependency management completely fail. This is not just a technical issue, it is also likely to trigger regulatory intervention in the next 12 months-especially when financial-grade applications such as FinceptTerminal begin to extensively integrate these high-risk components, and the regulatory boundary between technology and finance will be redrawn. Ironically, at this point, the Fed's decision to keep interest rates unchanged provided sufficient capital fuel for the entire risk game.

Among today's technological signals, progress in the AI field is particularly eye-catching. From open source code to new models to new commercial products, AI is penetrating into all fields at an unprecedented rate. However, under this craze, we also have to calmly think about the impact, value, problems and risks brought by these advances.

First of all, the activity of open source code reflects the openness and innovative spirit of the AI field. Whether it is Andrej Karpathy's Claude Code improvements or NousResearch's Hermes Agent, these open source projects are driving the advancement of AI technology. However, we should also note that open source does not always mean free and efficient. How to balance open source and commercial interests and ensure code quality and security is an urgent issue in the AI field.

Secondly, the emergence of new AI models indicates that AI will be more widely used in specific fields. From ML Intern's docker tools to the Kimi-K2.6 and Qwen3.6 series models, these AI models have demonstrated powerful capabilities in image recognition, text generation, etc. However, these models are costly to train and deploy and may face performance and scalability challenges in practical applications. In addition, the data bias and privacy issues of AI models cannot be ignored.

Finally, the emergence of new products reflects the prosperity of the AI market. From Agent Context's AI programming tools to Vector's AI security solutions, these products provide convenience for the application of AI. However, there are hidden risks behind this prosperity. How to ensure the safety and reliability of these products and avoid the abuse of AI technology is a challenge that enterprises and regulatory authorities need to face together.

In general, the rapid development of the AI field has brought unprecedented opportunities to mankind. However, in this craze, we should remain calm and pay attention to the impact, value, problems and risks brought by AI technology. How to balance innovation and security and ensure the sustainable development of AI technology will be an important issue that we must think about in the future.