Jad Ghalayini

mad scientist @hiloopai (YC26)

A compiler-compiler, now made flesh

[Jad Ghalayini @technicallyrain]

The pit of success is, in fact, still a pit

[Jad Ghalayini @technicallyrain]

The more things change, the more things stay the same

[Jad Ghalayini @technicallyrain]

@p_droabreu0 we’re gonna find out…

[Jad Ghalayini @technicallyrain]

@deepmatmul @_vatsadev all my proofs are load-bearing

[Jad Ghalayini @technicallyrain]

@ThielenKalani @p_droabreu0 What is light without darkness? Darkness without light?

[Jad Ghalayini @technicallyrain]

@deepmatmul repeat after me: S3 is not a filesystem. And yet

[Jad Ghalayini @technicallyrain]

@calvincongelado How about telling Claude to improve it in a _piloop_?

[Jad Ghalayini @technicallyrain]

we're at the "find out" stage of the scientific method

[Karan Brar @deepmatmul]

Once men turned their thinking over to machines in the hope that this would set them free. But that only permitted other men with machines to enslave them.

[Karan Brar @deepmatmul]

Once men turned their thinking over to machines in the hope that this would set them free. But that only permitted other men with machines to enslave them.

[Jad Ghalayini @technicallyrain]

writing is good for your soul -- transformers aren't turing complete without chain-of-thought

[Jad Ghalayini @technicallyrain]

@thomasboser slop don't stop

[Jad Ghalayini @technicallyrain]

It's really interesting to see the vast disparity in takes by different academics, even within the same discipline. I'm also consistently surprised by how unimpressed some PL people were, at least relatively recently, given that "do large induction" is probably the easiest type of proof to accelerate with an LLM -- it's an amazing experience to sketch the typing rules for a language and watch semantics and soundness materialize out of thin air.

[Andrej Karpathy @karpathy]

Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.

- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.

The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.

So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.

[Jad Ghalayini @technicallyrain]

@deepmatmul says I need to start posting -- sorry in advance guys

[Jad Ghalayini @technicallyrain]

@_jhunsaker @quant_arb I mean, I've seen some truly horrible embedded code, yet much C for high-level tasks is indeed fast. But the article is targeted at beginners, who may think that merely writing the code in C will make it fast; it might, but Rust would probably be faster.

[Jad Ghalayini @technicallyrain]

@_jhunsaker @quant_arb Author here: that's a very good point! Sorting is a toy problem here; we're trying to compare the perf of the solution ChatGPT would give to a problem, since 90% of code will have roughly that level of quality, esp. if moving fast. The slogan is "Rust makes sloppy code fast."