solarwindy

Heh, one vestigial bit of code, and they all are. Mind you, it's quite a creaky codebase, so it's forgivable to keep finding these appendices and calling them out as such. Useful, even.

The possibility to continue to sound like yourself after permanently losing your voice (e.g. from motor neurone syndrome) is one. Perhaps almost the only one.

Uh, no, most crypto wallet addresses have either a checksum or some other means of typo detection / prevention.

100 billion a quarter is Alphabet, right? Given how much click fraud there is, and that every org and business under the sun is held to ransom to feature on the SERP for their own name even — it’s tempting to say Google’s become a private tax on everything.

Outstanding video, thank you. No wonder this took months’ worth of research and animation to make.

Well, yes. Rather than that being a takedown, isn’t this just a part of maturing collectively in our use of this technology? Learning what it is and is not good at, and adapting as such. Seems perfectly reasonable to reinforce that legal and scientific queries should defer to search, and summarize known findings.

FWIW, Claude Sonnet 4.5 and ChatGPT 5 Instant both search the web when asked about this case, and both tell the cautionary tale.

Of course, that does not contradict a finding that the base models believe the case to be real (I can’t currently evaluate that).

I’m finding that whether this process works well is a measure (and a function) of how well-factored and disciplined a codebase is in the first place. Funnily enough, LLMs do seem to have a better time extending systems that are well-engineered for extensibility.

That’s the part which gives me optimism, and even more enjoyment of the craft — that quality pays back so immediately, makes it that much easier to justify the extra effort, and having these tools at our disposal reduces the ‘activation energy’ for necessary re-work that may before have just seemed too monumental.

If a codebase is in a good shape for people to produce high-quality work, then so too can the machines. Clear, up-to-date, close-to-the-code, low redundancy documentation; self-documenting code and tests, that prioritizes expression of intent over cleverness; consistent patterns of abstraction that don’t necessitate jarring context switches from one area to the next; etc.

All this stuff is so much easier to lay down with an agent loaded up on the relevant context too.

Edit: oh, I see you said as much in the article :)

VLLMs are incredibly good at decoding math from screenshots, if you’re working from a PDF textbook. ChatGPT especially, and since it’s conversant in LaTeX, it can respond directly in the notation you don’t recognize to break it down for you. It even manages with photos of my handwritten scrawl (mostly).

Isn’t GP’s point, that it’s already enough for those two to have solved it? Not every country with a civil nuclear program needs its own waste containment, it’s just such a small absolute quantity.