wbolt

Fantastic news! … for AI companies. These projects will continuously produce high quality, novel training data for free! Amazing deal.

I guess AI companies are actually super happy with major open source projects having such policies. It guarantees that their source code repositories will remain amazing assets for the training dataset construction. Open source projects don't buy massive licenses anyway - the paying customer is in the enterprise not in open source. So it's fine for open source to go without AI and by doing this contributing to AI models becoming better and better.

So all in all, a good news to everyone :) Both the "pro-AI" and "anti-AI" crowds.

This is a nice project but looks like is either AI written or AI assisted and I haven’t seen mention of that in any of the docs.

AI coding agents can do that pretty nicely already and it will only (slowly) improve over time.

This is so crazy. Companies benefit from OSS so they need to pay? Come on. Companies benefit from OSS because the core idea of most of these licenses is exactly this - everyone can benefit even without contributing back. Don’t like it? Think this is not fair? Don’t do OSS or pick a more restrictive license.

If a company pays for your work time not work products (many contracts work like this) they have the full right to expect that during this work time you do the work explicitly ordered by them. It’s not only the law - it’s common sense.

Are there any „real world users” of this? During all my years in academia I haven’t met any. Most just use plain LaTeX. Some do MS Word. Rarely something else. Never Texmacs. This is my experience at least.

With stuff like Overleaf and plugins for modern IDEs, honestly I can’t say LaTeX is a bad experience. It does what it should.

No. It is actually λProlog which seems to be an extension of Prolog.

At this moment there are 8 security issues open on GitHub (2 of them marked as High) and more than 190 issues in general. So it does look like a work-in-progress thing to me.

The application seems very cute and handy and it still might be very useful in a lot of specific use cases. Just keep in mind that it might not be production ready for you.

Actually most of CA simulations are done on torus which is referred to as periodic boundary conditions in the literature. Alternatively you can also have null (or fixed) boundaries or reflective ones. If the initial configuration has compact support (finite number of non-null states) and the CA keeps null-neighborhoods as null in the next step, you can simulate infinite grids… but not many people bother to do it. Many many papers use finite grids on torus.

This is interesting and may reveal additional properties of certain class of CAs.

Yet, as some comments already stated what you do is basically study a subclass of multi-state 2D CAs where specific states from the finite state set have a specific meaning associated.

In general a CA is defined as a dynamical system governed by a local rule operating on the neighborhood configuration and yielding a new state. State set is typically finite. But the actual structure of the states can be anything you like. A valid state can be a tuple of a form (visible state, number of neighbors, sum of neighbors degrees, …). As the maximum neighborhood size is finite and the visible cell states are finite - there is a finite number of such tuples which constitute the state set on which a CA can operate.

Summing up - you are studying CAs in which your multi-state setup has some implied meaning. Still cool and interesting.

Repl I get it. Possibly valid point. Yet I guess same issue are valid to node.js which seems much faster in many cases and still has valid dev experience.

C compatibility / extension compatibility - nope. First, it is an issue of limited resources. Add more devs to pypy team and compatibility bugs gets fixed. Second, aren’t people writing C extensions due to python being slow? Make python fast - as pypy - and for some cases native code won’t be that crucial.

So I don’t see a real issue with pypy that could not be solved by simply moving all the dev efforts from CPython.

So are there political, personal or business issues?

Yes it is.

But this is exactly my point. The resources pypy has are much smaller. And still for years they managed to follow up being just 2-3 versions behind with features and high on performance.

So why not move all the resources from CPython to close the gap with features faster and replace CPython entirely?

Since this is not happening I expect there to be serious reasons, but I fail to see them. This is what I ask for.

More than 300 comments here and still no convincing answer. Why the community wastes time on trying to make CPython faster when there is pypy which is already much faster? I understand pypy lacks libraries and feature parity with up to date CPython. But… can’t everyone refocus the efforts and just move to pypy to add all the missing bits and then just continue with pypy as the “official python”? Are there any serious technical reasons not to do it?

None

Exactly! The way in which the LLM is used here is very, very basic and outdated. This experiment should be redone in a proper „agentic” setup where there is a feedback loop between the model and the runtime plus access to documentation / internet. The goal now is not to encapsulate all the knowledge inside single LLM - this is too problematic and costly. LLM is a language model not knowledge database. It allows to interpret and interact with knowledge and text data from multiple sources.