Milind

Data, AI, and Engineering - with side interests in philosophy, history, space, and contrarian thinking

[Milind @milindsoorya]

A whole suite of privacy tools that run entirely in your browser https://t.co/d3C8yhczw3

[Milind @milindsoorya]

AI can still guess where your photo was taken even after you strip the GPS. kind of unsettling.
built a free tool that shows what your photos quietly reveal — and scrubs it. all in your browser 👇 https://t.co/PPUS4RZ8TQ

[Milind @milindsoorya]

@steipete @CeeKAU More like unlimited tokens 😂

[Milind @milindsoorya]

Humanity and intelligence are different

[Milind @milindsoorya]

Intelligence is a commodity

[Milind @milindsoorya]

A great challenge in life is to separate an opportunity to be seized from a temptation to be resisted - Nathan Sacks

[Milind @milindsoorya]

This is a glimpse of the future.

[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.

[Milind @milindsoorya]

@0xPaulius Yes, you just have to give it the proper tools. Minimum requirements are - codex, linear/jira, GitHub

[Milind @milindsoorya]

Builders: what signal tells you a side project is worth continuing?

[Milind @milindsoorya]

AI is a force multiplier.
It multiplies clarity or chaos, depends on your process.

[Milind @milindsoorya]

@stats_feed I need to sleep on time

[Milind @milindsoorya]

@Prathkum Agreed, and its the most patient tutor in history personalised to every individual.

[Milind @milindsoorya]

@warikoo @BellaBaddie__ Calicut Milk Sharbat

[Milind @milindsoorya]

@SahilBloom Yeah, Trump and Hitler would probably beg to differ.

[Milind @milindsoorya]

@ItsKieranDrew Silence is everything

[Milind @milindsoorya]

@umzrs rage-farming

[Milind @milindsoorya]

@Venkydotdev Thank you

[Milind @milindsoorya]

@hamptonism A boring job

[Milind @milindsoorya]

@LewisHowes Its like knowledge, it grows when shared. Yeah I know its a bit counter intuitive but its true.

[Milind @milindsoorya]

@1Umairshaikh Making babies?