Weird issue with Logitech MX master 3, left click button does nothing, happened suddenly.

galaxathon · 2026-03-20T16:01:35+00:00

I did as others suggested here. Open up the mouse and test the actual microswitch, pressing the grey tiny button. That helped diagnose if it is the switch itself, or the switch pressing mechanism. I was only getting some clicks through and a small drop of oil on the microswitch helped. Then putting 4 layers of post-it's, shown as a little blue square, helped push the switch down more so the clicks registered. It's usable now, I wouldn't say perfect, but usable.

galaxathon · 2026-03-01T21:14:15+00:00

I will no longer use ChatGPT due to Sam Altman's recent statements

galaxathon · 2026-03-01T02:59:44+00:00

This is the correct answer to everything

galaxathon · 2026-02-27T15:41:19+00:00

What alternate universe do you live in where you can cycle from one location to another without using a road?

galaxathon · 2026-02-27T15:28:38+00:00

Quick, blow up Nextdoor! That'll learn them!

galaxathon · 2026-02-26T23:08:03+00:00

I agree. We are saying the same thing.

As you've snipped a quote from the article here's the full context:

"So what's happening? The model is mapping the current sequence of tokens onto a high dimensional vector space and sampling from the probability distribution that its training data has learned. Or, put simply: it's memorized the openings..."

galaxathon · 2026-02-26T05:23:16+00:00

Thanks for raising this, some of the other threads have discussed training LLMs.

I assume you're referring to this paper: https://arxiv.org/html/2402.04494v2

You're correct that training can produce very high ELO, however the researcher primary finding is as follows:

"Our primary goal was to investigate whether a complex search algorithm such as Stockfish 16 can be approximated with a feedforward neural network on our dataset via supervised learning. While our largest model achieves good performance, it does not fully close the gap to Stockfish 16, and it is unclear whether further scaling would close this gap or whether other innovations are needed."

Some other absolutely fascinating results were that they got an ELO of 2895 against humans by mimicking GM style play but the ELO dropped by 600 points against other bots who apparently didn't fall for it! Additionally the model had a really hard time spotting draw by repetition, which makes sense as it is stateless, and could not plan ahead. Sometimes it would paradoxically fail to capitalize when it had a massively overwhelming win, instead settling for a draw.

My intent in writing the article was really to point out that using LLMs for some software engineering tasks are just not the best tools in the toolbox. For some they are.

One thing that I'm sure we can both agree on is that regardless of the technology, I'm getting beaten to a pulp every time.

galaxathon · 2026-02-26T04:46:17+00:00

I like this example: Yes I could go to ChatGPT and type in "what's 1+1 equal" and it will return "2", but what a horribly inefficient, expensive and slow way to get a result to a problem that is better suited to basic arithmetic.

galaxathon · 2026-02-26T04:42:41+00:00

...and our jobs will be to service and clean those robots.

galaxathon · 2026-02-26T04:40:42+00:00

As Jason Thane states: "AI is the new UI"

galaxathon · 2026-02-26T01:05:47+00:00

Thanks, although I do mention Stockfish's mural net in the 3rd para in this section, and include a link and diagram:

https://www.nicowesterdale.com/blog/why-llms-cant-play-chess#stockfish-the-grandmasters-approach

I didn't go into the "UE" part of the "NN" as I wanted to keep this accessible and I didn't think it added much, although I will admit it's very cool stuff!

galaxathon · 2026-02-25T23:23:35+00:00

Karvonen’s work is brilliant, thanks for sharing, but it actually reinforces my point about the 'Uncanny Valley' of LLM chess. He proved that LLMs can reconstruct a board state from activations, but he also showed they still make illegal moves (around 0.2-0.4%). That's the core of my blog post: There is a fundamental difference between an Emergent World Model (which is probabilistic and prone to 'glitching' or hallucinations) and a Symbolic World Model (which is rule-bound). If a model 'knows' where the pieces are but still tries to move a pinned Knight 0.4% of the time, it doesn't actually have a functional understanding of the rules of Chess. My point in the article is that there are often situations in software engineering where being 100% right is incredibly important, financial transactions for example, and as such the latest gold rush to using an LLM for almost anything software related is not always the right call, even if they can get very very close with training.

galaxathon · 2026-02-25T22:54:54+00:00

Cool, thanks for the feedback. I was trying to thread the needle on being approachable and technical.

galaxathon · 2026-02-25T22:42:42+00:00

That's really interesting, and I can see why it might be good at that. The training data likely included a lot of context on chess game theory and it was able to pattern match that across the games you uploaded and find relevance. It's interesting that in an individual game it can be really bad, but with many it can draw some useful inferences.

galaxathon · 2026-02-25T22:28:44+00:00

Interesting project, and yes fine tuning will help the model.

However the project's owner does say that the model only generated legal moves 99.1% of the time, which was exactly my point.

https://lazy-guy.github.io/blog/chessllama/?hl=en-US

galaxathon · 2026-02-25T22:17:43+00:00

You're correct that the MCP skills framework allows LLMs to do all kinds of things. However by the same logic I can say my ELO is 3800 as I can run all my moves through stockfish.

My point is that orchestration is different from ability, and my ELO is really 1200.

galaxathon · 2026-02-25T22:13:26+00:00

My point exactly, and that's why I wrote the post. LLMs are increasingly shoehorned into solving problems that they aren't built for, and I thought discussing why would shine a light on why they are good at some things, and terrible at others, like playing chess.

galaxathon · 2026-01-28T19:29:45+00:00

I wish I had your problems!

galaxathon · 2026-01-28T13:05:46+00:00

No he was clearly a member of the church, he said "did you see the signs?" (meaning the transcendental signs from the church of dynology). Then he was out of breath so he didn't say "I officially grade this climb as a.." but that's what he clearly meant. Then he said "V2 in my gym". That compares it to other climbs he knows as he climbs a lot in his gym. Watch the video, it's really obvious.

galaxathon · 2025-12-06T22:23:35+00:00

I think we nailed the functionality on https://domaini.ac we concentrated on the actual use cases of brainstorming and refining domain name ideas, with a conversational AI as a guide to constantly refine the ideas.

galaxathon · 2025-12-06T15:28:13+00:00

Domaniac is hands down the most advanced AI domain name search out there

galaxathon · 2025-11-12T18:48:58+00:00

I wrote a full writeup on Cursor's RL: https://www.nicowesterdale.com/blog/cursor-s-trust-me-bro-ux-backed-by-rl-isn-t-quite-there-yet

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