Torino, 20 febbraio: Guerra civile in Messico.
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Torino, 20 febbraio: Guerra civile in Messico. Sulla cosiddetta “guerra al narcotraffico”
@anarchia
Riceviamo e diffondiamo: Il primo di una serie di incontri internazionalisti Venerdì 20 febbraio, ore 18, Radio Blackout, via Cecchi 21/A Guerra civile in Messico. La cosiddetta «guerra al narcotraffico», le operazioni di polizia interna e internazionale:
Gli ultimi otto messaggi ricevuti dalla Federazione
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#Quicksy is getting an unusually high amount of registrations from Russia and the US. I wonder if these two countries have something in common.
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@matt I didn't say it was often. It's just more often than finding real serious bugs. It's mostly things like, "it assumed I wanted to keep backward compatibility with an old API endpoint, even though there are no consumers of the old API and killing old code is actually a benefit".
I'm not convinced it's writing more bugs than I would have written in the same amount of code or that my bugs are more likely to be caught in review. I don't write 100% test coverage for any of my projects...
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@swelljoe If the LLM misunderstands that often when doing code review, then it could also misunderstand in the direction of letting legitimate bugs through, right?
Sounds to me like we're lowering the bar on quality because business people, in response to what AI boosters are selling, are demanding that we pump out more and more, faster and faster.
I mean, do what you have to do to hold onto your job, but I think I'll keep resisting as long as I can.
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@matt there's a theory that very strict languages (e.g. Rust) are a great fit for AI, because the AI doesn't mind fighting the borrow checker, and the strictness of the language protects against many classes of bug.
I think lack of verification (as in "trust but verify") is why a lot of folks have bad experiences with it (I mean, even after the crossover point where the models and agents got really good). If you give a model clear success criteria, it'll hammer on it until success is achieved.
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@matt that's the other thing I feel uneasy about. You can't realistically review it all. At least not in the sense we think of reviewing code, if you want to obtain the velocity of using AI.
You can insist on extensive static analysis and 100% unit test coverage. It never complains about busy work. You can let another AI review the code. I've been doing Copilot code review when checking in code, which is also a cost to velocity, and it rarely detects real bugs, more often misunderstandings.
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Seriously debating putting on my crampons to take my bins out to the curb tonight.
Bah, I'm Canadian. I'll just walk like a penguin.
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@swelljoe What scares me is the thought of having to *review* all that code (not yours specifically, just in general as usage of coding agents ramps up). Given that LLMs can write code faster than we can, they can certainly write it way faster than I can read it.
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@matt "quantity has a quality all its own". Maybe I can write better code, given sufficient time. I can certainly write more concise code (especially in Perl).
But, the models write code an order of magnitude faster than I can, and they can write code 24/7. And, honestly, it's pretty good code, most of the time.
It's still true that the hardest part is deciding what to make rather than making it, but it's drastically easier to write software now with the AI than doing it myself.
