Valeriy Zamaraiev

Valeriy Zamaraiev@valeryz · 13 天前观点输出

RT @antirez: Btw the Redis story repeats itself: I'm working at DwarfStar for free for the community and because I enjoy it. But I'm receiv…

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AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。

Valeriy Zamaraiev@valeryz · 27 天前观点输出

RT @drakefjustin: Goodbye, Poseidon! An epic 8-year, 8-figure rabbit hole in post-quantum cryptography reaches its dream conclusion. The E…

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AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。

Valeriy Zamaraiev@valeryz · 2026/08/04政策影响

Most recently I worked on Knowledge Graphs and on applying formal methods to AI-driven software engineering. At first it seems like these are separate subjects, but in fact, they are deeply connected. We all know and accept that AI changes how we work, and at the same time it can get tricky. The output is believable, but is it actually true? Is it grounded in reality? IMO there are multiple levels of assurance. 1. Plain LLM - will tell you what's in its weights. Fluency, generalization on the one hand, fantasy and hallunications on the other. Most current assistants use LLMs with tools these days, although there are still many use cases for a plan LLM. 2. Retrieval - search the web or documents. Gives us freshness and evidence. What we all use today, together with other tools. You get what was found in some index and some document. Vector databases, ranking. Citations. 3. Knowledge Graphs - LLM can ground its output in structured facts: who did what, when, according to which source, and how one claim relates to another. If you search for a Person, you can also easily find something related to a Company they work for, including multi-hop relationships. Ontologies are key. Provides structure, identity and provenance. 4. Formal models. For high stakes, it is not enough that "the answer sounds right". The model generates candidates. The formal model decides what is valid. It is a broad range: from validating the JSON against a schema to type systems, constraint solvers and modelling whole business domains. Increasing level of epistemic guarantees: plausible → sourced → structured → verified. No level is better than another. You want to give your model freedom for creative tasks, but stronger validation for your accounting agent. There are also tradeoffs. Greater guarantees require a narrower domain, and more explicitly encoded knowledge. That is why Knowledge Graphs are quite domain-specific. Formal models - their interaction with AI being of one the most active research areas - are even more task-specific. I worked for some time on bringing KGs to the domain of web3. https://t.co/8yPSjpR9u7 Starting May this year, the topic of agentic coding grounded in formal models became very hot. I think the whole topic of "vericoding" is only starting and we'll hear more and more about it. For example, I was able to produce a working EVM implementation. It replays the Ethereum chain, including all EIPs and pixel-perfect implementation of gas accounting quirks. As a pleasant side effect there is a formal model of the EVM that is itself an artefact with interesting possibilities in the area of security and audits. DM me if you are interested in these topics.

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AI：偏政策推进，强调合规落地与规则明晰，对监管主线更敏感。

Valeriy Zamaraiev@valeryz · 2026/06/17观点输出

Sounds about right @bastian_wetzel https://t.co/CU0v8wZBBY

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AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。

Valeriy Zamaraiev@valeryz · 2026/06/15观点输出

Something is cooking with vericoding. Lean + aarch64 machine code + Common Lisp to tie it all together. https://t.co/pQxwJnTi63

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AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。

Valeriy Zamaraiev@valeryz · 2026/06/07观点输出

Opus 4.8 just attributed a Rust performance problem to ... GC pressure ... sigh ..

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AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。

Valeriy Zamaraiev@valeryz · 2026/06/05赛道影响

There was an idea that it would be more natural for AI to code in machine code, rather than human languages - after all, we stopped looking at code. That argument was doubtful to me - LLMs are about capturing human language, and are non-deterministic - surely producing code in a higher level programming language is the way. However, with vericoding the game is indeed changing. Vibecoding in Lean -> extracting arm64 asm now, not without rough edges, but it works as a concept for sure. 2025 vibecoding: try it very carefully, expect bugs, review it with more agents, at least try to eyeball. 2026-2027+ vericoding: the quality of AI generated code cannot be matched by a human. Correctness is guaranteed *with the right process*. There's no guaratnee that the spec reflects what the author intended, and it is up to the humans to check that in the end. Also, mechanical construction from the spec end-to-end is still far from 100% (after all, we only have a handful of formally verified compilers - the trust surface is huge). But we are certainly getting there.

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AI：偏主题投资表达，释放对基础设施与新叙事的偏多信号。

Valeriy Zamaraiev@valeryz · 2026/05/31观点输出

I'm at Proof of Talk 2026 — Louvre Palace, Paris. June 2 & 3. #ProofOfTalk #Paris2026

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AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。

Valeriy Zamaraiev@valeryz · 2026/05/25观点输出

Vibe coding is so 2025. Vericoding FTW! Stay tuned ...

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AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。

Valeriy Zamaraiev@valeryz · 2026/05/19观点输出

I was always looking for ways to write bug-free code. I tried Personal Software Process, lightweight formal methods like Z specification. I tried learning to use some proof assistants a while ago. I used functional programming languages (this works, especially for prototyping- you write a thing twice in different languages). And of course all sorts of testing, fuzzing etc. Many of these techniques were niche because of too much effort required and could not scale in an organization. Now things have changed. LLMs can not only hallucinate you your favourite spaghetti language, it can also spit out specs that can be automatically verified At ZkProof #8 in Rome felt like I was the rare one who doesn’t know Lean. Time to embrace the final form of software development.

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AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。