ActivePattern

You would be surprised how much of a difference the model makes for certain niche tasks.

For my use case, `gemini-3.1-flash-lite` is ~20% higher accuracy than the next best model of comparable cost (considering both proprietary and open-weight alternatives)

A lot of people in this thread think they're being clever by pointing out that a bookstore can't sell "banned" books. But it's common for bookstores and libraries to feature titles that have been banned in some jurisdictions. It's a small way to push back against censorship and promote freedom of information and critical thinking.

It took a few minutes to get the hang of, but it does work very nicely! Being able to adjust length without any re-tying is a great feature

Seeing this makes me wonder if Grok uses Claude conversations for training.

It's otherwise kind of surprising that they both converge on very similar phrases (e.g. "API integration is kicking my ass") that aren't anywhere in the prompt.

Automatic coding systems have way too much economic value to be considered a "fad". I don't think you need to be Nostradamus to predict that we're never going back to manual coding. Sure, the systems will evolve and improve, but they're certainly not going anywhere.

You're describing "modularity" or "loose coupling" in code. But it rarely implies you can just delete files or directory. It usually just means that a change in one component requires minimal changes to other components -- i.e. the diff is kept small.

He's most definitely talking about a white homeland [1][2]

[1] https://x.com/elonmusk/status/1962406618886492245 [2] https://en.wikipedia.org/wiki/Remigration

Reinforcement Learning by Sutton & Barto is an excellent introduction by two of the founders of the field.

Read here: http://incompleteideas.net/book/the-book-2nd.html

The win is in how many weights you process per instruction and how much data you load.

So it's not that individual ops are faster — it's that the packed representation lets each instruction do more useful work, and you're moving far less data from memory to do it.

I still don't understand is why they don't even make an attempt to apply overlayers, when (as the author notes) there is ample secondary evidence that it would be present. It's not like there isn't already some element of inference and "filling in the blanks" when reconstructing how something was painted from the scant traces of paint that survived.

I assume you didn't read the article, since that's their exact point...

"Since underlayers are generally the only element of which traces survive, such doctrines lead to all-underlayer reconstructions, with the overlayers that were obviously originally present excluded for lack of evidence."

I can only say being against this is either it’s self-interest or not able to grasp it.

So we're just waving away the carbon cost, centralization of power, privacy fallout, fraud amplification, and the erosion of trust in information? These are enormous society-level effects (and there are many more to list).

Dismissing AI criticism as simply ignorance says more about your own.

I've never heard the caveat that it can't be attributable to misinformation in the pre-training corpus. For frontier models, we don't even have access to the enormous training corpus, so we would have no way of verifying whether or not it is regurgitating some misinformation that it had seen there or whether it is inventing something out of whole cloth.

Yes, as is implied by the word "improvements"

You started this by objecting to my wording ("among the most") when I said fish/chicken are the most sustainable meat options. They are, by a wide margin. Beef’s footprint is roughly 10× higher, so swapping a beef meal for chicken or fish cuts ~90% of those emissions. That’s not a "slightly less bad choice".

Calling harm reduction "silly" because tofu exists just shifts the target. We can hold two thoughts at once: (1) plant-heavy diets are best, and (2) for the vast majority who aren’t going vegan tomorrow, steering from beef to chicken/fish dramatically reduces damage right now. Dismissing that because it’s not maximal purity guarantees we leave real cuts on the table.

I agree it’s worth comparing beef sources! That was my point about within-category differences and harm reduction. Saying "tofu is cleaner" doesn’t make beef comparisons pointless - just like the existence of bicycles doesn’t make car fuel economy comparisons pointless. We should compare across categories and within them, so people who aren’t switching today still choose the lower-impact option.

I don't think it's "unserious" to recognize that >85% of the world's population eats meat.

If you're quibbling about wording, all I meant was: farmed fish and chicken are among the most sustainable meat sources.

I'm not making a statement that people should eat meat, but many people do eat meat, so it's worth comparing which meat sources are better than others. I think it would be great if more people knew that beef produces 10x the greenhouse gases than chicken/fish do.

That take’s outdated. In the US/EU, routine antibiotics in fish farming are banned [1]. Growth hormones aren’t used in edible fish. Farmed salmon’s feed changed (more plant oils), but it still delivers high omega-3s and usually less mercury than wild [2].

[1] FDA “Approved Drugs for Use in Aquaculture” — https://www.fda.gov/media/80297/download

[2] Jensen et al., Nutrients 2020 — https://doi.org/10.3390/nu12123665

On the contrary, farmed fish is among the most sustainable protein sources for those not willing to go full vegetarian [1]

[1] https://ourworldindata.org/grapher/ghg-per-protein-poore

Hah, why don't you try implementing your 3 little functions and see how smart your "AGI" turns out.

not a particularly capable AGI

Maybe the word AGI doesn't mean what you think it means...

I don't think you've understood the paper.

- There are no experts. The outputs are approximating random samples from the distribution.

- There is no latent diffusion going on. It's using convolutions similar to a GAN.

- At inference time, you select ahead-of-time the sample index, so you don't discard any computations.

It doesn't play nice with a lot of popular Python libraries. In particular, many popular Python libraries (NumPy, Pandas, TensorFlow, etc.) rely on CPython’s C API which can cause issues.

Smalltalk JITs make p x * 2 fast by speculating on types and inserting guards, not by skipping semantics. Python JITs do the same (e.g. PyPy), but Python’s dynamic features (like __getattribute__, unbounded ints, C-API hooks) make that harder and costlier to optimize away.

You get real speed in Python by narrowing the semantics (e.g. via NumPy, Numba, or Cython) not by hoping the compiler outsmarts the language.

A “sufficiently smart compiler” can’t legally skip Python’s semantics.

In Python, p.x * 2 means dynamic lookup, possible descriptors, big-int overflow checks, etc. A compiler can drop that only if it proves they don’t matter or speculates and adds guards—which is still overhead. That’s why Python is slower on scalar hot loops: not because it’s interpreted, but because its dynamic contract must be honored.

Ironically, this comment reads like it was generated from a Transformer (ChatGPT to be specific)

It's a OpenAI researcher that's worked on some of their most successful projects, and I think the criticism in his X thread is very clear.

Systems that can learn to play Atari efficiently are exploiting the fact that the solutions to each game are simple to encode (compared to real world problems). Furthermore, you can nudge them towards those solutions using tricks that don't generalize to the real world.

I am quite confident that an LLM will never beat a top chess engine like Stockfish. An LLM is a generalist -- it contains a lot of world knowledge, and nearly all of it is completely irrelevant to chess. Stockfish is a specialist tuned specifically to chess, and hence able to spend its FLOPs much more efficiently towards finding the best move.

The most promising approach would be tune a reasoning LLM on chess via reinforcement learning, but fundamentally, the way an LLM reasons (i.e. outputting a stream of language tokens) is so much more inefficient than the way a chess engine reasons (direct search of the game tree).

I don't think that bidding system really works. If one side is strongly favored in the opening, the optimal bid would be essentially 0.4999999999... so that you can pick the color and win the game by a slim margin. Players then increase the bid with tiny steps ad infinitum.

The other idea works but is essentially just discarding all of the lopsided starting positions, in which case they might as well not be in the game.

Wouldn't the extra stamina have been rewarded, assuming creatine allowed you to perform extra repetitions? All exercises were done to repetition maximum.

The study seems to have controlled for training intensity -- all exercises were done to repetition maximum.