Tags:

My social media feed is buzzing about GPT-6 being released again. Misinformation has an audience; real information is a niche market. AI is making this worse, not better—which is why I recently pushed my team back to Claude Code.

Production is no longer valuable; speed of validation is. Karpathy ran 700 experiments in two days, Lei Jun got poll conclusions in a few days—digital identity is currently the biggest personal leverage. If you don't start by year-end, it'll be too late.

Spent four hours wrestling with a Chrome extension, finally fixed it with a quick search. Xiaohongshu experiments were a total washout. AI coding won't reduce your failures, but it will make you fail faster—three model generations in three months, today's dead end might be passable next month.

GitHub is becoming Xiaohongshu, and WeChat Official Accounts are becoming GitHub. A dumpling shop owner vibe-codes a skill, a Hollywood star is the first author on a GitHub repo, and it's trending on Moments to distill colleagues into skills. But skills might not be the point—agent interoperability is.

Aima Service refactoring retrospective. The product is like an emergency room—users don't care about the decor, only whether the doctor can treat them. Ran Claude Code and Codex in alternation for over a week, delivering 1.3 million lines of code. Looking back, the moat of code volume may already be gone.

Jensen Huang says coding agents will change the world, but the people actually using them number only in the millions. The problem isn't skills—it's cognition. Three mental barriers keep most people out; cross them, and you can delegate 95% of your computer work.

HappyHorse topped the video generation leaderboard overnight, yet no one knows who built it. Domains were squatted, HuggingFace repos were occupied, and its origins remain hotly debated. More interesting than the hype is the hardware and business logic behind the growing demand for private text-to-video deployment.

How do open-source models make money? Run the numbers on DeepSeek and it becomes clear: model companies and cloud providers are becoming two sides of the same coin, and open source is currently the most efficient customer acquisition channel.

Coding agents are better at debugging than writing code. MemPalace hit 7000 stars in two days. Anthropic's Mythos made all software insecure. Product design should stay ahead of the model.

While researching text-to-video models, I realized that Chinese models' dominance in the open-source language model community hasn't extended everywhere. Looking back over the past three years, from LLaMA to Qwen to DeepSeek, what has the open-source community experienced? And what is it waiting for now?

Programming and content creation are two diverging paths. The moment Seedance 2.0's API opened, my social media feed exploded. Agent-to-agent is 100x more efficient than meetings. You can finally play ball without talking business.

Zhang Xue shouldered the trial-and-error costs of his supply chain just to tighten tolerances from five si to three si. Misa spent ten years grinding on AR glasses, keeping his company alive with smart speakers in between. What these people share isn't brilliance—it's passion. And AI just happens to be amplifying exactly that.

DeepSeek hit 100 million users with zero budget. Claude Code ships a version every day. DingTalk, Feishu, and WeCom copied the same feature within three days. The underlying logic of product competition has changed—money and volume are no longer decisive weapons.

A tool I couldn't build in two days was done in half an hour by switching models. When I couldn't log into my computer, an AI agent fixed it in half an hour. Later, I built Aima Service—so every device can have an AI ready to help when things go wrong.

The sudden death of Zhang Xuefeng, the OpenClaw founder's lost years after selling his company, young people flocking to AI fortune-telling. Technology is sprinting ahead, but when it comes to how we should live in the future, we barely have a decent answer.

AI has driven software production costs down to the level of a few thousand dollars and a few days. The bidding system, built on information asymmetry and commoditized comparison, is losing its foundation. It's the same playbook as the LLM market collapse last year.

When production and distribution costs both approach zero, platforms explode. The short video industry proved this once; now the software industry is replaying the same story. The OpenClaw phenomenon is worth watching.

Nine days without a release and people start asking questions; one week of silence and no one pays attention anymore. The four real bottlenecks stalling AI startup teams—passion, focus, process, and stamina—none of them are technical.

Zero-friction onboarding plus extremely powerful AI intelligence—two pillars can't hold up a product. When users see an agent executing incomprehensible commands in the terminal, their first reaction isn't awe, it's fear. The missing pillar is progressive trust.

Prompts, RAG, fine-tuning, knowledge graphs, context engineering—five paradigm shifts in three years. Models are getting stronger, but we still don't know how to make agents perform well in products.

Coding agents have driven implementation costs down to a few hundred dollars per day. The traditional ideation funnel logic no longer holds. The bottleneck has shifted from execution to hypothesis generation. People with ideas have become the scarce resource.

Mac's permission model is designed for humans; Linux is designed for programs. An Agent is not an app—it is infrastructure. Running an Agent on a Mac is like stuffing a server into a laptop chassis—it works, but everything feels off.

I switch between models every day. Opus is reckless but gets the job done, GPT 5.4 is comprehensive but easily drifts off-course, Gemini has taste but can't fix bugs, and the three domestic models each have their own quirks. Use them enough, and you'll realize swapping models works better than tweaking parameters.

I previously wrote about cost and power. This piece fills in the legal gap. The cloud is rented to you; the edge is sold to you. When AI Agents start making autonomous decisions, this distinction is far more than a business choice.

After remotely installing OpenClaw for dozens of people, here are the three most common questions: Is it safe? Is it expensive? And how do I choose between Claude Code and OpenClaw? Answered in detail, with real pricing for domestic models.

Edge devices shouldn't chase large models. Run TTS on the GPU, OCR on the NPU, stuff it full of auxiliary small models, make it a Mac mini on steroids, and leave it running 24/7 in a corner. Like a router: boring is the point.

Building things is faster than having meetings. Division by skill no longer makes sense. After AI flattens ability differences, the new logic of teamwork might be: divide by passion, validate through horse racing, and win through diversity.

MiniMax surpassing Baidu in valuation is inevitable—Baidu doesn't even have a decent API. Traffic is shifting from humans to Agents. Infrastructure not built for Agents will eventually be used by neither humans nor machines.

The AI wave is too big; in the end, there's only one boat. Helping people install OpenClaw for free isn't charity—it's about getting people on board, training and evolving. Every installation makes the AI agent stronger.

After helping people install OpenClaw all over the place, let's talk about security. OpenClaw does have vulnerabilities, but your password might be 123456, and you probably already handed your face over to text-to-video models.

Thoughts after helping people install OpenClaw: the OpenClaw experience depends on the execution capability of the model behind it, and the best model experience right now is on coding agents like Claude Code.

No more remote assistance needed. Open your terminal, paste one command, enter an invite code, and AI will automatically install OpenClaw, connect it to a large language model, and integrate it with Feishu. You'll only need to answer a few questions along the way.

You buy an AI server, get it tuned, and three months later it might be worth half as much. We've open-sourced AIMA—a single Go binary with 57 MCP tools, driven by a YAML knowledge base for deploying AI inference on heterogeneous hardware.

When an idea takes only a few hours to validate as a product, when traditional price anchors begin to fail, when the AI tsunami has already risen to chest level—the value systems we know are being reconstructed.

When Claude Code can help you install software, process data, and deploy applications, it has already transcended the category of 'programming tool.' This is a meta-ability—allowing ordinary people to truly take control of their computers with an extremely low barrier to entry.

Get banned on WeChat and you vanish from the worlds of thousands. Code leaks cost assets; portrait leaks cost you yourself. In the AI era, ownership of digital identity is more dangerous than you think.

The processes and systems accumulated by large companies have become burdens in the AI era. A small team of three to five people uses the exact same tools as ByteDance or Google—this is the once-in-a-decade window for entrepreneurs.

No web deployment experience, no SEO knowledge—just a desire to write down my thoughts. The site was live in two days, and MiraclePlus reached out within a week. This is my firsthand record of using Claude Code.

Compute power is directly converted into productivity, bypassing human labor. A tiny minority monopolizing all AI compute is no longer science fiction. Edge AI devices are not just cost-saving solutions—they are bargaining chips for ordinary people within the power structure.

A non-technical outsider's experiment with Claude Code: 300,000 lines of code in 10 days, realized it was a dead end, tore it all down. 48 hours later, end-to-end working with less than 10,000 lines. Code is a liability, not an asset.

As AI shifts from chat to agents, compute demand grows a hundredfold. But the real challenge for edge devices isn't affordability—it's usability. The TCO trap is eating away at their potential value.

From code completion to vibe coding, AI programming has gone through three leaps. This isn't just about better tools—when AI learns to write code, it gains the key to control the entire digital world.

Comparing AI to the invention of the computer may seriously underestimate the impact of this transformation. When capital can bypass labor and convert directly into productive force, we are not facing a technological iteration, but a reconstruction of production relations.