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Cake day: August 22nd, 2023

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  • I’m so tired of “Ai big number” headlines. 100+ packages - but how many good ones? (Genuinely, I don’t know, as I don’t the recognize those listed.)

    We need projects that diligently keep the slop out. We don’t need every protect to do so, but it is critical that some do.

    We have Meta and Microsoft to provide quick-to-market low quality shit.

    The version of Linux that matters is the slow thoughtful good software.

    I’m not mad at slop slingers, aa long as they don’t secretly inject slop risk where not welcome.

    Edit: Thanks for responses.

    For anyone who has read this far, I encourage seeing responses below for informative and nuanced thoughts on this.












  • Linux, MacOS and Windows have been completely rewritten multiple times across their existence.

    So it’s not like the original decisions are all still in play.

    Windows and Mac OS were rewritten under some particular weird duresses of closed source profit driven engineering; so it has been a mix of improvements and seemingly illogical setbacks.

    Linux developers still need to eat, so some of the same (corporate) influences are there.

    But the average engineer who sets out to create a brand new completely modern operating system - usually decides to reuse any components of Linux that they don’t have any concerns with…

    Which usually gives us yet another cool new Linux variant.

    But sometimes we get…

    something very different

    A cool new FreeBSD variant!






  • Thanks. I think every data point helps people.

    I can’t say that particularly shocks me. I imagine that Chat GPT has probably swallowed dozens of Physics textbooks.

    The bigger part of the problem is knowing where we are on the human knowledge novelty / training data available curve, or rather, when we need to get off of the ride.

    Edit: Any chance your chosen AI has access to Wolfram Alpha as an MCP Tool? Because that would be very different, too.

    If it’s successful at math, there’s likely much more than a learning model in play.

    Which brings us back to the opacity problem. :(

    If the answer machine has been cleverly rigged (and weighted) to pass off math questions to more capable software, things can be great.

    But a person who just hears that AI can do math now can have a very bad time if they go blindly use a brute force pleasant answer machine on a math problem. :(


  • For simple math that could work, and as long as the question is close enough to an exact match with plenty of published examples to copy.

    A good rule of thumb is that the script it will come up with is about as likely to be correct as blindly taking the highest voted answer to the most similar question on Stack Overflow.

    If the question is simple and common, the odds are quite good. If the question is nuanced or rare, the odds of a correct result drop off aggressively.

    Edit: Your mileage may vary - providing an API to these LLMs that can do math correctly for them is pretty easy. Getting the LLM to consistently detect when to use that API is more challenging.