Maxwell: A new kind of model that sits between code and hardware

There's a lot of claim and so little technical details on that page.

How was the formalization of Python accomplished? What formal proof framework do you use?

How do you reproduce the optimizations across several instances / runs of the program? (since the source code doesn't change... assuming you don't redo the same work over and over?)

What visibility, tracking and logging is there into the actual code running? Debugging is an extremely important part of building software, especially if there's a demon switching out binary code constantly underneath. Even if the recompiler is perfect (it won't be), user code never is.

Why is the superoptimization and visibility into the data not surfaced back to the level of the source code? This seems like a waste.

Let me say that I like the idea:

- verification

- superoptimization specialized to a particular piece of hardware

- patching binaries while they are running

all great

That being said, I'm getting some weird vibes from the prose on this site. Little phrases and claims that seem intended for the purposes of marketing, but conflate things with clear technical meaning in confusing ways.

For instance: I watched the video ("a code execution model has to be right every time") which implicitly communicates something like "the model cannot hallucinate" ... of course, the agentic LM is still probabilistic, but it is wrapped with verification tools (to reason about semantic equivalence), and is likely post-trained to be good at using those tools.

This strikes me as a conflation between the model and the harness which is intended to strike a marketing point (like: "it doesn't hallucinate") but which is clearly a ridiculous claim to make (falling down the slope: any harness which does error correction for improper tool calls might also be a "model which makes no mistakes")

Also: "self-improving code"

that's not really it, is it? Self-improving code would be code that runs itself and improves itself. This is code I run, which Maxwell watches and improves ... no?

Related: I dislike the usage of the word "model" in the above discussions on the site. Do we want to call everything a "model" now? Can anyone invent a new frontier "model" by post training an agentic LM with tools?

Also, let's say you are optimizing kernels on GPUs. This can be subtly fraught: for instance, it was well-known for awhile that one should only use certain pieces of software (vLLM, for instance) to reproduce papers (which used vLLM) there are non-deterministic sources of randomness (including, of course, floating point) that would prevent one from recovering the performance claimed in a paper ...

While the people behind this project seem to be real and apparently have credentials, this article is giving me serious whatever the textual version of uncanny valley is called. It hits so many of what reads like assorted buzzwords meant for marketing but guised in programmer-speak. One moment, they’re talking about the Curry-Howard correspondence and then next moment they’re describing what a data type is to people who are presumably more than casually familiar with LLMs. And the worst offense imo is linking to that “November Singularity” bs clearly written by AI to sell hype to people without much of a technical background (blog writer even admits some articles are written with AI “assistance”).

Sounds very interesting, I've occasionally wondered what it would be like to have a profiler "in the loop" which could direct improvements. Not everything can be known at compile-time, and even one run can differ greatly from another (ETL vs normal interactive use, or day-to-day load).

I read the entire manifesto thinking the discourse about a code execution model would lead to a revelation that this is a model that can reduce bugs in code in realtime and make sure the code runs to the original spec or blueprint. Instead it turned out to be about a model that keeps on optimising code that can go faster and faster over time. That's very exciting in its own right, don't get me wrong, but I was really starting to hope this would be about creating code that breaks less etc.

Maybe it means both but perhaps the emphasis is on speed.

This was a lot more exciting than I expected.

The homepage does not do a very good job of explaining what we're looking at:

It appears to be a combination of two things:

1) A formalization of the Python language

2) An LLM which hooks into running programs and hot-swaps code with verifiably-equivalent behavior but better runtime performance, found via superoptimization and then verified via the formalization rules

This is brilliant, and in hindsight one of those "I can't believe nobody had thought of this yet" sort of things.

How is the compute of the model weighed against the compute that it saves?

I like how the Manifesto is written, the Turing example at the top got me thinking more holistically about different impressive computer science achievements

Sounds cool, but isn't it weird that optimizations from my workload are just for me? What if they could help a another user?

I love it, any timeline for general availability?

Just compile for your specific hardware sort of like what Gentoo does. I don't want you continuously running a model - sort of defeats the purpose of performance gains.

Can I use Maxwell in my js web app? Or just Python stuff?

this is an israeli company, use at your own risk (to life, the planet and humanity). plus you dont want an israeli company in your stack swapping code with malware and trojans.

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Is it 100% deterministic?

The manifesto is so cool!

very cool