[1] https://github.com/ljtn/epiq

Small off topic thing: I recommend removing the bit in the URL from ?si=... and forward, unless you want Google to track every user who clicks this link to your share.

I do this to completely skip online uploading, and am keeping track of stats using a mashup of bash scripts and cli tools.

It does capture a provenance graph for any ad-hoc shell commands executed in the shell mode, or if prepended by `sci run`, by tracing all new files created from commands, and creating an accompanying json file for every output with metadata, which can later be assembled into a graph specific to any output file, using the `sci tohtml` or `sci toshell` commands (producing an HTML report with an SVG graph, or a reproducing shell script, respecively).

I'm quite bullish of the possibilities with this approach.

And, this was in fact also born out of the thinking to "rip apart" workflow tools, and build them up again using small, well-defined tools that do one thing well.

[1] https://github.com/samuell/scicommander

Comments URL: https://news.ycombinator.com/item?id=49106875

Points: 2

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Perhaps it is because I was really curious as a kid about how things work, peeked into things, really tried to understand how it works, and figured it was far from easy.

I also tried to draw bikes, and figured you really have to think it through quite hard the first times, lol.

In my experience sparks are generally generated when quite small metal surfaces get in contact at high speeds, meaning they can generate a lot of friction in a very small point, and thus not have enough braking effect despite the local friction in one small point (think rail wheels squeeking in curves).

Brakes OTOH are designed to spread out the friction on a somewhat larger area, which allows more material to take up the friction and the heat, and spread it around.

I know brakes can get extremely hot too though, and after a really hard braking you might have to worry about deformed brakes sometimes.

Race cars tend to have really large breaks (and brake pads) for this reason.

And I argue that the notion of AI models having overtaken the human mind in practically all areas, is vastly inaccurate.

I could back it up with a number of references of course, but I think this is already quite well known and accepted by most people with some insight into the field.

Regarding the idea of distributed models communicating with each other, I have also been thinking (and writing [1]) along those lines, where I see that the data amounts needed to fully digitalize ourselves and our society requires far too much storage if just serialized (limited by bandwidth if nothing else), while smart, updateable models are actually a much better storage medium for such information, as it can communicate only the important bits (any new information) on a higher level, with each other.

The other observation here that rings bells for me is how I think lessons from trying to develop intelligent systems should upvalue the human mind rather than devalue it, as we start to treat it less like an ad-hoc thing, and more like the finely tuned machine it is, which also benefits greatly from optimizing what data we feed it with, the architecture of solution strategies etc. All of which is an area where humans and machines can do wonders together [2].

[1] https://livingsystems.substack.com/p/the-future-of-data-less...

[2] https://livingsystems.substack.com/p/ai-progress-should-upgr...

While this may be slightly overstated, my take on this is that AI progress should have us upgrade our view of the human brain rather than the opposite.

Wrote about it the other day:

https://livingsystems.substack.com/p/ai-progress-should-upgr...

Comments URL: https://news.ycombinator.com/item?id=48860443

Points: 2

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https://spf13.com/p/go-the-agentic-language/