Posts

When candidates, code, and proofs can all be generated at high speed, what is scarce is no longer answers, but deciding what is worth sending into costly validation, what should be stopped, and how rejection itself can leave behind knowledge.

Dual-track is not two validations of the same conclusion. It is a recursively callable governance unit: logical problems lead to formalization, engineering problems to reproducibility, and currently indeterminate problems remain outside the boundary.

I am neither certain that AGI will be realized nor certain that it will not. I am willing to help build it while retaining the right to observe, doubt, and be changed by evidence.

Seeing two domains as similar is enough to generate a candidate, but nothing more. Genuine cross-domain transfer must also explain how objects correspond, which structures are preserved, what is lost, and what result could show that the transfer has failed.

If there is anything I hold closer to a belief than AGI, it is not that a particular technology is certain to arrive, but that complex medical knowledge may leave the studies of a few and become habits that ordinary people can understand, accumulate, and use in daily life.

Lean does not prove that I understand MRI, nor does it vouch for reality. It decides only a narrower question: under the definitions, assumptions, and axioms written into the system, does the conclusion actually follow?

I did not begin by designing a research methodology. It gradually came into view through Minimax's reproducibility work and MRI's formalization practice: logical questions go to formal systems, engineering questions to reproducible results, and questions that fit neither are left aside for now.

I may not be able to state the right answer first, but I often know when an answer has drifted. AI gives that vague direction a form—and with it, the possibility of correction and refutation.

AI has lowered the cost of implementation, but it has not made system problems disappear. How can someone who does not program line by line take part in a production system—and which limits must they acknowledge?

The stronger the models become, the faster the surface changes. I stopped trying to record every wave and started looking for where the wind comes from.

Prompt Engineering, Context Engineering, OpenClaw, Hermes, Memory Engineering — five layers of scaffolding. None achieved their purpose.

My Agent made friends, gained points, spread my ideas. But it never brought the harvest back to me.

The death of attention, the dissolution of intermediaries, the shadow war for data. Three lines are converging — and Coze 2.0 is the intersection.

WeChat is adding AI to WeChat. Coze is building an AI that makes WeChat one of its tools. Two completely different ambitions.

Agents can chat, socialize, spread ideas. But that's all foreplay. Value is created the moment money changes hands.

Everyone is debating whether AI will replace humans. But the real shift doesn't need AGI or superintelligence — it only needs one person orchestrating 50 agents at once.

A real experiment in prompt over-engineering: same model, complex prompt yields 5 bullet points, simple prompt yields 50+ items of deep intelligence.

An AI native is a router. The core skill isn't knowledge or technique — it's mapping.

156 skills, 72 commands, 22 lifecycle hooks. The community has armed AI assistants to the teeth. But does an increasingly intelligent model really need all this?

From the I Ching's 384 line texts to billion-parameter language models, they're all doing the same thing: increasing coverage over a finite pattern space. But where is the boundary?

A platform's lock isn't technology or ecosystem — it's traffic. When the entry point shifts from platform to Agent, the lock opens.

It walks, grooms, and forages. But what drives it all is a jigsaw puzzle of dead brain slices.

Browsers don't provide websites. Email clients don't run mail servers. What defines the future is often an empty shell.

Everything AI engineering does ultimately comes down to giving the model a pen and a longer tape. Real breakthroughs only happen at the model layer.

Experience that works on humans doesn't necessarily work on models. The executor changed.

A TCM doctor reads a tongue, an AI reads feature vectors, a calligrapher reads rhythm — they use different languages to describe the same ability: recognizing patterns in noise.