Krzysztof Warechowicz

The magic happens before the roadmap,before the plans, the backlog, or the pitch deck.

There’s intuition, sparks,and raw ideas,
version 0.0.1

[Krzysztof Warechowicz @warechow]

School, college, work, die. Play safe . Ehh

[Chris Lakin @chrislakin]

what your risk-aversion costs you

[Krzysztof Warechowicz @warechow]

Ludzie nie rozbestwiają się od dobroci. Rozbestwiają się od braku granic. To jest zupełnie inna zmienna.

People don’t get spoiled by kindness. They get spoiled by a lack of boundaries.

[Krzysztof Warechowicz @warechow]

Nie da się zoptymalizować procesu, którego jeszcze nie ma.

It is impossible to optimize a process that does not exist yet.

[Krzysztof Warechowicz @warechow]

As usual ... doers.

[justine @justinemach_]

an idiot in motion goes further than a genius at rest

[justine @justinemach_]

an idiot in motion goes further than a genius at rest

[Krzysztof Warechowicz @warechow]

Incredibly simple in its simplicity, yet complex and profound. Read this at least twice.

[wanye @xwanyex]

Adam Carolla has an old line about how if you want to destroy somebody’s life, just give them free rent in a shitty little house. You’ll come back 30 years later and find them still living in that shitty little house, because it was just too good of a deal for them to go out and do all the things that they would’ve otherwise done to improve their lives.

[Krzysztof Warechowicz @warechow]

Not other way.

[Simon Sarris @simonsarris]

you must cross the river by feeling the stones

[Krzysztof Warechowicz @warechow]

"Entelechy is the complete realization of potential; when a possibility becomes a reality. ". Coming from Arystoteles.

[Krzysztof Warechowicz @warechow]

Too good to be public available. Just forget nmap -sS. https://t.co/7sSdN4Zw8N

[Krzysztof Warechowicz @warechow]

Those who do, achieve.

[Cormac @cormachayden_]

the only cure to uncertainty is taking an unreasonable amount of action until the answer reveals itself

[Krzysztof Warechowicz @warechow]

Fully agree. We need intelligence for solving old problems.

[Markus J. Buehler @ProfBuehlerMIT]

Scientific discovery is reaching the limits of human capacity: too much data, too many disconnected fields, and too few ways to connect ideas fast enough to matter. The next breakthroughs in materials, medicine, energy, and beyond will not come from scaling today’s AI paradigm alone or from relying on serendipity alone. They will require a new kind of AI for knowledge discovery that not only models the world but shapes what it could become. At Unreasonable Labs, we are building superintelligence for knowledge discovery: systems that reason across disciplines, generate novel hypotheses, test them through simulation and experimentation, and help guide real-world discovery. Our AI engine is not confined to what it has seen in training. It creates new data, builds new tools, and maintains a persistent world model that grows more powerful as it reasons.

Why now?

Even today's most powerful AI models face a core limitation: they are trained on what we already know. True discovery begins when a system encounters something its current model cannot explain. This is why you cannot train your way to a discovery - a system has to reason through new problems, update its beliefs, and revise its understanding of the world as it thinks.

Another critical insight is that rich knowledge already exists, but is not yet applied to solve pressing problems. It sits in millions of papers, patents, and datasets, trapped in isolated silos, often in legacy data vaults. What's missing is a way to connect it, scale it, unlock the potential, and synthesize genuine novel predictions.

The time is now to build a system that enables practitioners to design, explore, and direct discovery, whether through human guidance or full automation, while capturing the tacit insight that domain experts bring.

Steerable reasoning

That is why we built an operating system for scientific discovery - one that replaces chance with steerable reasoning.

Rather than retrieving static facts, our AI builds and continuously updates a living world model - a representation of knowledge the system can actively reason over, question, and revise.

A concrete example: say you want to create "smart concrete" that can flex - a concept that doesn't exist yet. Our AI maps relationships across domains, finds a path from morphable smart materials to concrete, and identifies the most efficient way to bridge those concepts. It then autonomously writes simulations, tests the hypothesis, and refines the idea. Then it interacts with hardware to produce a physical artifact, and the loop expands into the real-world, where the machine becomes world-shaping.

Our AI gives users full visibility into how the system arrived at a conclusion. It delineates which existing patents and papers it drew upon versus what is genuinely new - protecting IP and competitive concerns from the start, and offering deep compositional insights into technology advances.

It takes unreasonable people to make progress

Our team reflects the interdisciplinary expertise required to build this next breakthrough - my co-founder Yuan Cao @caoyuan33 (formerly DeepMind) and Andrew Lew, @HaiqianYang, Matt Insler, Jennifer Kang and Julia McLaughlin.

We are backed by $13.5M in seed funding led by @PlaygroundVC with participation from @aixventures, @e14fund, and MS&AD. We are guided by advisors including Robert Langer (1,000+ patents), Kostya Novoselov (Nobel Prize in Physics), and @Thom_Wolf (Co-founder of Hugging Face).

We already have multiple pilot programs underway with leading industrial partners in materials science and engineering, with additional engagements developing across energy, logistics, bioengineering, and other strategic domains.

The biggest challenges of our time - fusion energy, sustainable materials, new medicines - demand exponentially more innovation than humans alone can produce. We are not replacing scientists, and instead are making every scientist capable of leading their own team of AI-powered researchers. Abundant innovation leads to abundant prosperity.

Watch our launch video below to see what we're building @unreasonable_ai 👇

[Krzysztof Warechowicz @warechow]

Computer train computers and then again computers train computers , loop.

[Andrej Karpathy @karpathy]

Three days ago I left autoresearch tuning nanochat for ~2 days on depth=12 model. It found ~20 changes that improved the validation loss. I tested these changes yesterday and all of them were additive and transferred to larger (depth=24) models. Stacking up all of these changes, today I measured that the leaderboard's "Time to GPT-2" drops from 2.02 hours to 1.80 hours (~11% improvement), this will be the new leaderboard entry. So yes, these are real improvements and they make an actual difference. I am mildly surprised that my very first naive attempt already worked this well on top of what I thought was already a fairly manually well-tuned project.

This is a first for me because I am very used to doing the iterative optimization of neural network training manually. You come up with ideas, you implement them, you check if they work (better validation loss), you come up with new ideas based on that, you read some papers for inspiration, etc etc. This is the bread and butter of what I do daily for 2 decades. Seeing the agent do this entire workflow end-to-end and all by itself as it worked through approx. 700 changes autonomously is wild. It really looked at the sequence of results of experiments and used that to plan the next ones. It's not novel, ground-breaking "research" (yet), but all the adjustments are "real", I didn't find them manually previously, and they stack up and actually improved nanochat. Among the bigger things e.g.:

- It noticed an oversight that my parameterless QKnorm didn't have a scaler multiplier attached, so my attention was too diffuse. The agent found multipliers to sharpen it, pointing to future work.
- It found that the Value Embeddings really like regularization and I wasn't applying any (oops).
- It found that my banded attention was too conservative (i forgot to tune it).
- It found that AdamW betas were all messed up.
- It tuned the weight decay schedule.
- It tuned the network initialization.

This is on top of all the tuning I've already done over a good amount of time. The exact commit is here, from this "round 1" of autoresearch. I am going to kick off "round 2", and in parallel I am looking at how multiple agents can collaborate to unlock parallelism.
https://t.co/WAz8aIztKT

All LLM frontier labs will do this. It's the final boss battle. It's a lot more complex at scale of course - you don't just have a single train. py file to tune. But doing it is "just engineering" and it's going to work. You spin up a swarm of agents, you have them collaborate to tune smaller models, you promote the most promising ideas to increasingly larger scales, and humans (optionally) contribute on the edges.

And more generally, *any* metric you care about that is reasonably efficient to evaluate (or that has more efficient proxy metrics such as training a smaller network) can be autoresearched by an agent swarm. It's worth thinking about whether your problem falls into this bucket too.

[Krzysztof Warechowicz @warechow]

No excuse for jogging now ;)

[Claude @claudeai]

New in Claude Code: Remote Control.

Kick off a task in your terminal and pick it up from your phone while you take a walk or join a meeting.

Claude keeps running on your machine, and you can control the session from the Claude app or https://t.co/er6Blrr63e

[Ava @noampomsky]

I know a surprising number of guys who spent all of their 20s making money and all of their 30s trying to learn how to be happy

[Krzysztof Warechowicz @warechow]

" You see they didn't find, they created it. " Overheard.

[Krzysztof Warechowicz @warechow]

Haha this post is just incredible:D @steipete in Poland we say " ale ziomek pomzmiatał temat ". Handled it brilliantly.

[Peter Steinberger 🦞 @steipete]

Alright, back to coding. The Claw doesn't ship itself...

yet.

[Krzysztof Warechowicz @warechow]

@naval "Get busy living, or get busy dying" The Shawshank Redemption. So simple.

[Krzysztof Warechowicz @warechow]

Next.js -
code splitting per route
lazy loading
cache per route
streaming per route
frontend+ backend+ supabase+ stripe.. ohh yeah

[Krzysztof Warechowicz @warechow]

Pure good !

[Prompter @PromptLLM]

life is so much easier when you realise the first hour of the day is self hypnosis

[Krzysztof Warechowicz @warechow]

You won't read anything better today

[James Clear @JamesClear]

Savor the little victories as much as you criticize the little mistakes.