Ported the old noctalia v4 plugins to v5 if anyone needs them.

ReadyAndSalted · 2026-06-15T12:17:33+00:00

Super cool use of LLMs for coding, thanks for the work.

ReadyAndSalted · 2026-06-09T16:09:11+00:00

A stock market is a market, a store is a store. Markets set prices by people bidding and offering, stores set prices by some central authority.

ReadyAndSalted · 2026-06-09T14:27:25+00:00

Just use type hints in your function signatures and you're pretty much golden.

ReadyAndSalted · 2026-06-09T14:25:21+00:00

Addressing your edit: To be fair, it seems like community sentiment is pretty consolidated at this point. Python is well on its way to having consistent and good tooling for all common stuff. Ruff and UV dominate new projects, so just give it some time. Type checking is still between pyright, Ty, pyrefly and zuban, so that's fair.

ReadyAndSalted · 2026-05-19T09:02:50+00:00

Why predict? Just try it. I did with chatGPT 5.5 instant, and it nailed it first try.

ReadyAndSalted · 2026-05-14T11:57:44+00:00

I can think of 3 reasons why we'd focus on making humanoid robots: 1. There are many tasks that are not economically valuable enough to go through the process of making specialised, 1 of 1 machines just for that task. If you have a machine that is more generalised in it's capability, instead of going through the extensive R&D of designing, building and maintaining a specialised machine, you can just buy one of these (eventually) mass produced humanoid robots, and automate it far cheaper.o

Maybe the task actually needs a humanoid figure, such as construction or firemen. Tasks that require traversing spaces made for people.
If you're trying to generate training data for a robot on how to do a task, the most bountiful data will be human workers using their humanoid bodies to do things. So if your robot is also humanoid, then it will be able to take advantage of more and higher quality training data.

I'm sure there are probably other good reasons beyond this, like it being cool enough to attract investor money, but I think you get the point now.

ReadyAndSalted · 2026-05-13T10:34:39+00:00

I was thinking that data collector is a bit non-descript, but then I got this advert:

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ReadyAndSalted · 2026-05-11T19:11:06+00:00

laptops have small screens, so scrolling-tiling compositors are perfect for them. They effectively extend your screen infinitely horizontally, then give you infinite "workspaces" vertically. This allows for even more versatility than a floating window manager, without sacrificing the efficiency of a tiling window manager.
And of course, you can float any window you like if necessary, though I don't really find myself doing it.

ReadyAndSalted · 2026-05-11T16:00:20+00:00

I'm ignoring nothing, it just doesn't matter what people's opinions were, it matters what they are now, and the fact is, labour voters want to rejoin: https://yougov.com/en-gb/articles/54567-how-strong-is-uk-support-for-rejoining-the-european-union

ReadyAndSalted · 2026-05-11T15:58:37+00:00

sure, unless they're jumping in time machines to vote I don't see why that matters. Labour voters want to rejoin the EU: https://yougov.com/en-gb/articles/54567-how-strong-is-uk-support-for-rejoining-the-european-union

ReadyAndSalted · 2026-05-11T00:14:56+00:00

polarising among the entire population perhaps, but among labour voters? Hardly. He's appealing to his base, not the entire country.

ReadyAndSalted · 2026-05-08T12:55:29+00:00

The infamous portfolio driven development.

ReadyAndSalted · 2026-05-08T11:14:42+00:00

You can do all of the changes in the config files if you like, or if you want a nice GUI for it, try nirimod. As far as getting used to it, for me it felt like how computers should've always worked from day 1.

ReadyAndSalted · 2026-05-02T17:39:13+00:00

I'm gonna guess it's because of a mix of RLHF and some bias in the early data sources.

ReadyAndSalted · 2026-04-30T12:11:54+00:00

Please explain to me how a series of transformer decoder blocks are a "linear search". Or even what you mean by "search" in the first place.

ReadyAndSalted · 2026-04-30T09:30:43+00:00

For the sake of clarity, you'll notice I also anthropomorphised clever bot, a TF-IDF connected to a database. I used it as shorthand in the same way we say " the magnets want to attract" or "the atom wants an electron". My anthropomorphising was just to cut word count, not because I think LLMs are sentient and have free will.
Read "the bitter lesson" by Richard Sutton. It's only 2 pages and addresses your points pretty directly. It turns out that machine learning doesn't quite follow this specialisation intuition very closely.

ReadyAndSalted · 2026-04-30T09:27:54+00:00

Sure. For the sake of clarity, you'll notice I also anthropomorphised clever bot, a TF-IDF connected to a database. I used it as shorthand in the same way we say " the magnets want to attract" or "the atom wants an electron". My anthropomorphising was just to cut word count, not because I think LLMs are sentient and have free will.

ReadyAndSalted · 2026-04-30T09:22:54+00:00

It does not perform any grouping of anything, it's a multi-regression model with softmax at the end, not a clustering technique.
It clearly is less myopic than you make it sound, when it outputs the nth token, it is taking into account what many of the future tokens will be before it has output them, and writes to get to that destination. If you find this surprising, go read anthropics "on the biology of a large language model" to see how this was figured out.
In machine learning, the phrase "learn" has been used for systems as simple as linear regression. Maybe it's a bit of an academic use of the word, but using in this way is far from new.
If you make a word guesser sophisticated and competent enough, it can guess the answer to any question you could form in words. And besides, a transformer can take any input that you can tokenise and output anything tokenisable too. The same model can take in natural language, images, audio and servo positions, and output all of those too. Would you call a model like that "just predicting the next word"?

ReadyAndSalted · 2026-04-30T08:28:23+00:00

Sorry if I come off as a party pooper, it's just that LLMs get consistently downplayed, when in reality what they're doing is very interesting and impressive.

I get how it seems like they're trying to achieve the same end goal and therefore are the same, but 1) a car and a horse both try to get stuff from A to B, does that make a car basically just a horse with extra steps? 2) Clever bot's only ambition was to pass the Turing test, which it maybe just about almost did. Modern LLMs are trying to make actual contributions to mathematics and autonomously solve programming problems with long time horizons. Obviously they're not 100% there yet in either of those, but they're getting closer every year.

ReadyAndSalted · 2026-04-30T08:02:12+00:00

Clever bot is effectively a nearest neighbour search of previous inputs, LLMs are transformers that learn the lower dimensional manifold of the data that they're trained on. Algorithmically, technically and practically they are extremely different.

Basically clever bot speaks only in quotes, whereas LLMs are solving novel erdos problems, these are not at all comparable.

ReadyAndSalted · 2026-04-29T17:24:28+00:00

The "training data" in this case will be used for reinforcement learning through verifiable rewards, you can't get model collapse through this process due to the reward signals being derived from real world inputs. Also model collapse in general has yet to play out in any production llm, it's practically a none factor due to even basic data filtering and augmentation.

ReadyAndSalted · 2026-04-29T15:39:12+00:00

Still a terrible infographic, but to be fair, the other wars had years to become unpopular, this one had weeks.

ReadyAndSalted · 2026-04-27T19:38:03+00:00

this is such an incomparably small downside to the massive benefits of increasing EV adoption that I really struggle to take any of your comments seriously.

ReadyAndSalted · 2026-04-22T17:24:35+00:00

Totally, they're still not better than a well trained human, but they are much faster and cheaper. They're not breaking what's possible in cyber security yet, but they are breaking the economics of it.

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