Yannick Sun

Co-founder Misogi Labs | Building an AI biochemist 🧬💊 | e/acc | Foodie × Hiker × Hacker

[Xiangyi Li @xdotli]

sharing my personal library on evals 1/n

i put together the highest quality blogs, podcasts, papers, and projects on evals. additions are welcome!

[Xiangyi Li @xdotli]

must read for everyone who wants to reduce the entropy of their agentic systems

[Andrej Karpathy @karpathy]

Fireside chat at Sequoia Ascent 2026 from a ~week ago. Some highlights:

The first theme I tried to push on is that LLMs are about a lot more than just speeding up what existed before (e.g. coding). Three examples of new horizons:

1. menugen: an app that can be fully engulfed by LLMs, with no classical code needed: input an image, output an image and an LLM can natively do the thing.
2. install .md skills instead of install .sh scripts. Why create a complex Software 1.0 bash script for e.g. installing a piece of software if you can write the installation out in words and say "just show this to your LLM". The LLM is an advanced interpreter of English and can intelligently target installation to your setup, debug everything inline, etc.
3. LLM knowledge bases as an example of something that was *impossible* with classical code because it's computation over unstructured data (knowledge) from arbitrary sources and in arbitrary formats, including simply text articles etc.

I pushed on these because in every new paradigm change, the obvious things are always in the realm of speeding up or somehow improving what existed, but here we have examples of functionality that either suddenly perhaps shouldn't even exist (1,2), or was fundamentally not possible before (3).

The second (ongoing) theme is trying to explain the pattern of jaggedness in LLMs. How it can be true that a single artifact will simultaneously 1) coherently refactor a 100,000-line code base *and* 2) tell you to walk to the car wash to wash your car. I previously wrote about the source of this as having to do with verifiability of a domain, here I expand on this as having to also do with economics because revenue/TAM dictates what the frontier labs choose to package into training data distributions during RL. You're either in the data distribution (on the rails of the RL circuits) and flying or you're off-roading in the jungle with a machete, in relative terms. Still not 100% satisfied with this, but it's an ongoing struggle to build an accurate model of LLM capabilities if you wish to practically take advantage of their power while avoiding their pitfalls, which brings me to...

Last theme is the agent-native economy. The decomposition of products and services into sensors, actuators and logic (split up across all of 1.0/2.0/3.0 computing paradigms), how we can make information maximally legible to LLMs, some words on the quickly emerging agentic engineering and its skill set, related hiring practices, etc., possibly even hints/dreams of fully neural computing handling the vast majority of computation with some help from (classical) CPU coprocessors.

[Zain Shah @zan2434]

Imagine every pixel on your screen, streamed live directly from a model. No HTML, no layout engine, no code. Just exactly what you want to see.

@eddiejiao_obj, @drewocarr and I built a prototype to see how this could actually work, and set out to make it real. We're calling it Flipbook. (1/5)

[Yannick Sun @yannick_sun]

@nc_frey @Prof_Oak_ @nc_frey are you starting with a specific workflow wedge or going full-stack since day 1? We keep running into the same thing - preserving the biological narrative across handoffs is harder than automating the actual tasks. What's your approach?

[Jarrid Rector-Brooks @jarridrb]

What if AI could invent enzymes that nature hasn’t seen? 👩‍🔬🧑‍🔬

Introducing 🪩 DISCO: Diffusion for Sequence-structure CO-design

14 rounds of directed evolution and over a year of wet lab work. That's what it took to engineer an enzyme for selective C(sp³)–H insertion, one of the most challenging transformations in organic chemistry.

DISCO surpasses this with a single plate. No pre-specified catalytic residues, no template, no theozyme, no inverse folding, just joint diffusion over protein sequence and structure.

📝 Blog: https://t.co/j9Za0JigfO
📄 Paper: https://t.co/ficrYNBBrM
💻 Code: https://t.co/p81sSwoaPH

[Reid Wiseman @astro_reid]

There are no words.

[NASA Artemis @NASAArtemis]

Approaching the near side of the Moon.

The Artemis II astronauts have surpassed the record for the distance from Earth at 1:56 ET (1756 UTC). This record was previously set during the Apollo 13 mission when the astronauts traveled 248,655 miles from Earth. The Moon continues to grow larger and larger in the windows of the Orion spacecraft as the Artemis II mission gears up to observe the far side. The astronauts are predicted to make their closest approach of the Moon around 7:02pm ET (2302 UTC).

[le.hl @0xleegenz]

Walking alone through a foreign city at night and realizing how far you’ve come has to be a top 3 peak moment of all time

[sui ☄️ @birdabo]

SOMEONE MADE A DIGITAL WHIP TO MAKE CLAUDE WORK FASTER 💀

[Han @HanchungLee]

https://t.co/SAr9yIZT03

[Yannick Sun @yannick_sun]

Sharing is caring🫰

[Elizabeth Holmes @ElizabethHolmes]

I'll refrain from commenting.

But if the founders would like to talk...

I've seen this show before.

[himanshu @himanshustwts]

Based on everything explored in the source code, here's the full technical recipe behind Claude Code's memory architecture:

[shared by claude code]

Claude Code’s memory system is actually insanely well-designed. It isn't like “store everything” but constrained, structured and self-healing memory.

The architecture is doing a few very non-obvious things:

> Memory = index, not storage
+ MEMORY.md is always loaded, but it’s just pointers (~150 chars/line)
+ actual knowledge lives outside, fetched only when needed

> 3-layer design (bandwidth aware)
+ index (always)
+ topic files (on-demand)
+ transcripts (never read, only grep’d)

> Strict write discipline
+ write to file → then update index
+ never dump content into the index
+ prevents entropy / context pollution

> Background “memory rewriting” (autoDream)
+ merges, dedupes, removes contradictions
+ converts vague → absolute
+ aggressively prunes
+ memory is continuously edited, not appended

> Staleness is first-class
+ if memory ≠ reality → memory is wrong
+ code-derived facts are never stored
+ index is forcibly truncated

> Isolation matters
+ consolidation runs in a forked subagent
+ limited tools → prevents corruption of main context

> Retrieval is skeptical, not blind
+ memory is a hint, not truth
+ model must verify before using

> What they don’t store is the real insight
+ no debugging logs, no code structure, no PR history
+ if it’s derivable, don’t persist it

[euan ashley @euanashley]

New AI paper from us this week. When my student first showed me his initial findings, I really didn’t know what to make of them. I felt that this was an interesting but curious loophole phenomenon that would shortly be closed. I was very wrong.
https://t.co/H3YIyl01FR

[Sakana AI @SakanaAILabs]

The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature

Nature: https://t.co/nNfpSV5e5I
Blog: https://t.co/i6h8LVQOdl

When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle.

From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible.

Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process.

Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature!

This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement.

Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable.

Building upon our previous open-source releases (https://t.co/H1tBT14Yx8), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science.

This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team!

@_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune

[Yannick Sun @yannick_sun]

@RealJonMaynard @ProductHunt CREDITS

[Thariq @trq212]

https://t.co/nKTDfC7zMm

[Yannick Sun @yannick_sun]

@jisong_learning @jiaying_ai @julianweisser @cgilly2fast @abed_hamami @yuvanarvind Got lost at the start and somehow turned it into an extra warmup 😂 great run with everyone!

[Carlson @carlsoncheng_]

we just killed writer’s block.

introducing nimbus, now you can write at the speed of thought.

real time auto-research and compounding intelligence, no prompting required.

[Yannick Sun @yannick_sun]

@felixleezd Great guide and thanks for sharing! How would you approach building on top of an existing design system or component library?