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HappyHorse topped the video generation leaderboard overnight, yet no one knows who built it. Domains have been squatted, HuggingFace repos occupied, and origins debated. More interesting than the hype itself is the hardware and business logic behind the privatization demand for text-to-video.

How do model companies make money with open source? Looking at DeepSeek's numbers makes it clear: model companies and cloud companies are becoming two sides of the same coin, and open source is currently the most efficient customer acquisition method.

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

While researching text-to-video models, I discovered that Chinese models' dominance in the language model open source community hasn't extended to every domain. Looking back at 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. When Seedance 2.0's API opened, my social media feed exploded. Agent-to-agent efficiency is 100x that of meetings. Finally, you can play ball without talking business.

Zhang Xue bore the trial-and-error costs of his supply chain just to reduce tolerance from 0.05mm to 0.03mm. Misa spent ten years obsessing over AR glasses, surviving on smart speakers in between. These people share something in common—not intelligence, but passion. AI happens to amplify exactly that.

DeepSeek reached 100 million users with zero budget, Claude Code ships a version every day, and DingTalk, Feishu, and WeChat Work all shipped the same feature within three days of each other. The underlying logic of product competition has changed—money and buzz are no longer decisive weapons.

A tool I couldn't build in two days of tinkering, finished in half an hour with a different model. Locked out of my computer; an AI agent fixed it in half an hour. Later I built Aima Service, wanting every device to have an AI helper when things go wrong.

The sudden death of Zhang Xuefeng, the OpenClaw founder lost after selling his company, young people flocking to AI fortune-telling. Technology is racing ahead, yet we have almost no decent answers about how to live in the future.

AI has compressed software production costs to the realm of a few thousand dollars and a few days. The bidding and tendering system, which relied on information asymmetry and commoditized comparison, is losing its foundation. This is the same script the large model market followed last year.

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

A conversation overheard during lunch, where anxiety and excitement switched seamlessly. The real bottlenecks in AI entrepreneurship lie in passion, focus, processes, and physical stamina—not technology.

Zero-friction onboarding plus extremely strong AI intelligence—two pillars can't support a product. When users see agents 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 actually know how to make agents perform well in products.

Coding agents have compressed implementation costs to the magnitude of 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 design is for humans, Linux is for programs. An Agent isn't an app, it's infrastructure. Running an Agent on Mac is like cramming a server into a laptop chassis—it works, but everything feels wrong.

Switching between models daily. Opus is reckless but capable, GPT 5.4 is comprehensive but tends to drift, Gemini has aesthetic sense but can't fix bugs, and the three domestic models each have their own issues. Use them enough and you'll realize that switching models works better than tuning parameters.

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

Security issues are being patched quickly, the price might be less than a cup of milk tea, and choosing between Claude Code or OpenClaw depends on your circumstances.

Edge devices shouldn't try to compete on large models. Run TTS on the GPU, OCR on the NPU, stuff it full of auxiliary small models, make it a beefed-up Mac mini, toss it in a corner to run 24/7. Like a router—boring is exactly right.

AI enables everyone to produce at 10x speed, rendering traditional functional divisions obsolete. When building becomes faster than meeting, the logic of collaboration must change.

It's inevitable that MiniMax's valuation will surpass Baidu's. As traffic shifts from humans to Agents, infrastructure not built for Agents will eventually become obsolete.

Someone asked me what I gain from helping others install OpenClaw for free. Two reasons: first, recruiting people; second, training my troops.

After helping people install OpenClaw (the 'little crayfish'), let's talk security. OpenClaw does have vulnerabilities, but your password might be 123456, and you've already handed your face over to text-to-video models.

Thoughts after helping people install the 'crayfish' (OpenClaw): The OpenClaw experience depends on the execution capability of the underlying model, and the best model experience currently lies with coding agents like Claude Code.

No more remote assistance needed. Open your terminal, paste one command, enter the invite code, and AI will automatically install OpenClaw, connect it to LLMs, and integrate with Lark for you. You only need to answer a few questions throughout the process.

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

When validating an idea takes just hours, when traditional price anchors begin to fail, when the AI tsunami has already reached your chest—the value systems we know are being reconstructed.

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

Platforms can erase your digital existence at any time. Your code, your likeness, your social connections—who actually owns these rights?

The processes and systems accumulated by large companies have become burdens in the AI era. Everyone has been leveled—this is precisely the window of opportunity for entrepreneurs.

I didn't understand web deployment, didn't understand SEO—just had an idea to record my thoughts. Website live in two days, MiraclePlus reached out in a week. This is my firsthand experience with Claude Code.

When computing power can be directly converted into productivity, whoever controls computing power controls power. The significance of edge AI devices lies not merely in cost savings—they serve as counterbalances to future power structures.

A technical outsider's experiment with Claude Code: 10 days to stack up 300,000 lines of code, realizing it wouldn't work, then tearing it all down. 48 hours later, less than 10,000 lines, end-to-end working. Code is liability, not asset.

As AI shifts from chat to agents, compute demand has grown 100-fold. But the real challenge with edge devices isn't affordability—it's usability. The TCO trap is consuming their potential value.

From code completion to vibe coding, AI programming has undergone three major leaps. This isn't just tool improvement—when AI learns to write code, it gets the keys to manipulate the entire digital world.

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