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Cake day: March 3rd, 2025

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  • When I’m using AI for coding, I find myself constantly making little risk assessments about whether to trust the AI, how much to trust it, and how much work I need to put into the verification of the results. And the more experience I get with using AI, the more honed and intuitive these assessments become.

    For a system that has such high cost (to the environment, to the vendor, to the end user in the form of subscription), that’s a damningly low level of reliability.

    If my traditional code editor’s code completion feature is even 0.001% unreliable – say it emits a name that just isn’t in my code base – that feature is broken and needs to be fixed. If I have to start doubting whether the feature works every time I use it, that’s not an acceptable tool to rely on.

    Why would we accept far worse reliability in a tool that consumes gargantuan amounts of power, water, political effort, and comes with a high subscription fee?




  • Revenue going up, hiring going down, layoffs every quarter and a big push for everyone to use AI. But at the same time basically no real success story from all this increased AI usage. Probably just me, but I just don’t get it.

    No, you’ve got it: Revenue increases, short term, when personnel costs are cut, through layoffs and hiring freezes.

    The story told (“workers must return to the office to sit on teleconference all day” prompting more of them to quit, or “your job can be done by robots”, or whatever) only needs to make enough sense that the stock holders are satisfied the executives have a sane explanation for sudden loss of workers. Otherwise it might look like the executives are panicking!






  • Now we are beginning to see agents: systems that aspire to greater autonomy and can work in “teams” or use tools to accomplish complex tasks.

    Given that an “agent” can be assigned work and carry it out autonomously: no, we are not yet seeing any agents. Every one of these bots requires close attention by a human to weed out the huge quantity of mistakes it generates. That’s not an “agent” by any useful definition:

    Both Anthropic and OpenAI, for example, prescribe active human supervision to minimise errors and risks.

    Right. So, it’s a bot which even the vendor recommends you don’t leave it to work autonomously. Not an agent.

    In other news: “self driving” that requires continuous human monitoring and intervention, by multiple humans per vehicle, is not self driving.

    Just because the hype marketing of tech corporations bleats a term into the media, does not mean they’ve got anything that actually does what they say it does.




  • No kidding there’s a bubble. When it pops - the tech’s not going anywhere. […] No need for a wall of text, over and over and over.

    He’s making the point that the entire tech economy is dominated by this bubble, and gargantuan amounts of money is tied up in this with no hope of getting any useful or profitable business.

    Yet the mainstream press continues to coddle the egos of Musk and Altman and Zuckerberg and Nadella and Bezos and Pichai, as though their business use of this technology is worth the trillions of investment value they have attracted. It’s a fantasy. While that continues, the message is not getting through and the bubble continues to inflate.

    For as long as the bubble goes on inflating, yes there is urgent need to keep repeating that message until the mainstream tech and financial press starts accepting it as reality (because that’s what investors read), so that people stop hooking our economies into that bubble.




  • Despite their great value to society, open source projects are frequently understaffed and underresourced. That’s why GitHub has been advocating for a stronger focus on supporting, rather than regulating, open source projects.

    What nice sentiments. Perhaps you, GitHub, could start by insisting that Microsoft cease the un-attributed, non-consensual shovelling of open-source software into their LLM training maw. And turn off the LLM that they’re attempting to unilaterally sell based on all that uncompensated labour.

    Or is your platitude of “supporting open source projects” fall short of actually respecting what we want and need?





  • Following up after your clarification (thank you):

    it is not okay to do, but the severity of how much of an issue it is depends on the context? Either that or it’s completely avoidable in the first place if I just use “automated testing” or “loggers”.

    It’s important here to distinguish the code you’re currently working on, in your local development environment only; versus the code you commit to VCS (or otherwise record for progress / deployment / sharing with others / etc.).

    In your local development environment, frequently you are making exploratory changes, and you don’t yet know what exactly is the behaviour you need to implement. In this mode, it’s normal to pepper the area of interest with console.log("data_record is:", data_record) calls, and other chatty diagnostic messages. They are quick and easy to write, and give immediate result for your exploratory needs.

    In the code you commit (or deploy, or share, or otherwise record as “this is what I think the code should be, for now”) you do not want that chatty, exploratory, effectively just noise diagnostics. Remove them as part of cleaning up the code, which you will do before leaving your workstation because you now understand what the diagnostic messages were there to tell you.

    If you find that you haven’t yet understood the code, can’t yet clean it up, but you need to leave your workstation? Then you’ve made a mistake of estimation: your exploration took too long and you didn’t achieve a result. Clean up anyway, leave the code in a working state, come back to it another day with a fresh mind. Your will have a better understanding because of the exploration you did anyway.


  • bignose@programming.devtoGodot@programming.devWhat version control software do you use?
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    7 months ago

    Magit, which is the best Git porcelain around. Git, because it has an unparalleled free-software ecosystem of developer tools that work with it.

    Why is Git’s free-software ecosystem so much better than all the other VCSen?

    Largely because of marketing (the maker of Linux made this! hey look, GitHub!), but also because it has a solid internal data model that quickly proved to experts that it is fast and flexible and reliable.

    Git’s command-line interface is atrocious compared to contemporary DVCSen. This was seen originally as no problem because Git developers intentionally released it as the “plumbing” for a VCS, intending that other motivated projects would create various VCS “porcelain” for various user audiences. https://git-scm.com/book/en/v2/Git-Internals-Plumbing-and-Porcelain The interface with sensible operations and coherent interface language, resides in that “porcelain”, which the Git developers explicitly said they were not focussed on creating.

    But, of course, the “plumbing” command line interface itself immediately became the primary way people were told to use Git, and the “porcelain” applications had much slower development and nowhere near the universal recognition of Git. So either people didn’t learn Git (learning only a couple of operations in a web app, for example), or to learn Git they were required to use the dreadful user-hostile default “plumbing” commands. It became cemented as the primary way to learn Git for many years.

    I was a holdout with Bazaar VCS for quite a while, because its command-line interface dealt in coherent user-facing language and consistent commands and options. It was deliberately designed to first have a good command-line UI, and make a solid DVCS under that. Which it did, quite well; but it was no match for the market forces behind Git.

    Well, eventually I found that Magit is the best porcelain for Git, and now I have my favourite VCS.