Skip Navigation

InitialsDiceBearhttps://github.com/dicebear/dicebearhttps://creativecommons.org/publicdomain/zero/1.0/„Initials” (https://github.com/dicebear/dicebear) by „DiceBear”, licensed under „CC0 1.0” (https://creativecommons.org/publicdomain/zero/1.0/)A

antihumanitarian

@ antihumanitarian @lemmy.world

Posts
0
Comments
56
Joined
3 yr. ago

  • AI-generated code contains more bugs and errors than human output

    Jump

  • A later commenter mentioned an AI version of TDD, and I lean heavy into that. I structure the process so it's explicit what observable outcomes need to work before it returns, and it needs to actually test to validate they work. Cause otherwise yeah I've had them fail so hard they report total success when the program can't even compile.

    The setup I use that's helped a lot of shortcomings is thorough design, development, and technical docs, Claude Code with Claude 4.5 Sonnet them Opus, with search and other web tools. Brownfield designs and off the shelf components help a lot, keeping in mind quality is dependent on tasks being in distribution.

  • AI-generated code contains more bugs and errors than human output

    Jump

  • The vast majority of my experience was Claude Code with Sonnet 4.5 now Opus 4.5. I usually have detailed design documents going in, have it follow TDD, and use very brownfield designs and/or off the shelf components. Some of em I call glue apps since they mostly connect very well covered patterns. Giving them access to search engines, webpage to markdown, in general the ability to do everything within their docker sandbox is also critical, especially with newer libraries.

    So on further reflection, I've tuned the process to avoid what they're bad at and lean into what they're good at.

  • AI-generated code contains more bugs and errors than human output

    Jump

  • So this article is basically a puff piece for Code Rabbit, a company that sells AI code review tooling/services. They studied 470 merge/pull requests, 320 AI and 150 human control. They don't specify what projects, which model, or when, at least without signing up to get their full "white paper". For all that's said this could be GPT 4 from 2024.

    I'm a professional developer, and currently by volume I'm confident latest models, Claude 4.5 Opus, GPT 5.2, Gemini 3 Pro, are able to write better, cleaner code than me. They still need high level and architectural guidance, and sometimes overt intervention, but on average they can do it better, faster, and cheaper than me.

    A lot of articles and forums posts like this feel like cope. I'm not happy about it, but pretending it's not happening isn't gonna keep me employed.

    Source of the article: https://www.coderabbit.ai/blog/state-of-ai-vs-human-code-generation-report

  • Baldur's Gate 3 now has a native Linux build

    Jump

  • Did some testing on this on Linux, Arch + KDE 6 (wayland) + Nvidia GPU. It looks like DLSS works on Nvidia hardware. I didn't do formal benchmarks, but I didnt notice performance improvement. On my laptop it actually used roughly 10% more watts on the GPU while capped at 60fps. I don't think I could tell the difference on a blind test. So I'll use it anyway to do my part for hardware surveys.

    Although this was Act 1, perhaps Act 3 will tell a different story.

  • ChatGPT 5 power consumption could be as much as eight times higher than GPT 4 — research institute estimates medium-sized GPT-5 response can consume up to 40 watt-hours of electricity

    Jump

  • The last 6 to 12 months of open models has pretty clearly shown you can substantially better results with the same model size or the same results with smaller model size. Eg Llama 3. 1 405B being basically equal to Llama 3.3 70B or R1-0528 being substantially better than R1. The little information available about GPT 5 suggests it uses mixture of experts and dynamic routing to different models, both of which can reduce computation cost dramatically. Additionally, simplifying the model catalogue from 9ish(?) to 3, when combined with their enormous traffic, will mean higher utilization of batch runs. Fuller batches run more efficiently on a per query basis.

    Basically they can't know for sure.

  • Why LLMs can't really build software

    Jump

  • LLMs have made it really clear when previous concepts actually grouped things that were distinct. Not so long ago, Chess was thought to be uniquely human, until it wasn't, and language was thought to imply intelligence behind it, until it wasn't.

    So let's separate out some concerns and ask what exactly we mean by engineering. To me, engineering means solving a problem. For someone, for myself, for theory, whatever. Why do we want to solve the problem, what we want to do to solve the problem, and how we do that often blurred together. Now, AI can supply the how in abundance. Too much abundance, even. So humans should move up the stack, focus on what problem to solve and why we want to solve it. Then, go into detail to describe what that solution looks like. So for example, making a UI in Figma or writing a few sentences on how a user would actually do the thing. Then, hand that off to the AI once you think it's sufficiently defined.

    The author misses a step in the engineering loop that's important though. Plans almost always involve hidden assumptions and undefined or underdefined behavior that implementation will uncover. Even more so with AI, you can't just throw a plan and expect good results, the humans need to come back, figure out what was underdefined or not actually what they wanted, and update the plan. People can 'imagine' rotating an apple in their head, but most of them will fail utterly if asked to draw it; they're holding the idea of rotating an apple, not actually rotating the apple, and application forces realization of the difference.

  • GitHub CEO delivers stark message to developers: Embrace AI or get out.

    Jump

  • I've used it most extensively doing Ruby on Rails greenfield apps, and also some JS front ends, some Python mid sized apps, and some Rust and Nix utilities. You're absolutely right about it struggling with code base scale, I had to rework the design process around this. Essentially, design documentation telling the story, workflow documentation describing in detail every possible functionality, and an iteration schedule. So the why, what, and how formalized and in detail, in that order. It can generate the bulk of those documents given high level explanations, but require humans to edit them before making them the 'golden' references. Test driven development is beyond critical, telling it everywhere to use it extensively with writing failing tests first seems to work best.

    So to actually have it do a thing I load those documents into context, give it a set unit of work from the iteration schedule, and work on something else.

    It does go down some seriously wrong paths sometimes, like writing hacky work arounds if it incorrectly diagnosing some obscure problem. I've had a few near misses where it tried to sneak in stuff that would bury future work in technical debt. Most problematic is it's just subtle enough that a junior dev might miss it; they'd probably get sent down a rabbit hole with several layers of spaghetti obscuring the problem.

  • GitHub CEO delivers stark message to developers: Embrace AI or get out.

    Jump

  • I'm a professional developer and have tested AI tools extensively over the last few years as they develop. The economic implications of the advancements made over the last few months are simply impossible to ignore. The tools aren't perfect, and you certainly need to structure their use around their strengths and weaknesses, but assigned to the right tasks they can be 10% or less of the cost with better results. I've yet to have a project where I've used them and they didn't need an experienced engineer to jump in and research an obscure or complex bug, have a dumb architectural choice rejected, or verify if stuff actually works (they like reporting success when they shouldn't), but again the economics; the dev can be doing other stuff 90% of the time.

    Don't get me wrong, on the current trajectory this tech would probably lead to deeply terrible socioeconomic outcomes, probably techno neofeudalism, but for an individual developer putting food on the table I don't see it as much of a choice. It's like the industrial revolution again, but for cognitive work.

  • How do you reconcile staying sane while keeping yourself up-to-date with the news?

    Jump

  • I find old Stoic philosophy helpful. If I can't do anything about it, I stay informed but try to be mindful of my limitations. If I can do anything about it, even if not much, if I'm worried about the thing I use that to do what I can.

  • Deleted

    Permanently Deleted

    Jump

  • Most if not all leading models use synthetic data extensively to do exactly this. However, the synthetic data needs to be well defined and essentially programmed by the data scientists. If you don't define the data very carefully, ideally math or programs you can verify as correct automatically, it's worse than useless. The scope is usually very narrow, no hitchhikers guide to the galaxy rewrite.

    But in any case he's probably just parroting whatever his engineers pitched him to look smart and in charge.

  • Syncthing alternatives

    Jump

  • I had some similar and obscure corruption issues that wound up being a symptom of failing ram in a main server node. After that, only issues have been conflicts. So I'd suggest checking hardware health in addition to the ideas about backups vs sync.

  • the beautiful code

    Jump

  • I've used it extensively, almost $100 in credits, and generally it could one shot everything I threw at it. However: I gave it architectural instructions and told it to use test driven development and what test suite to use. Without the tests yeah it wouldn't work, and a decent amount of the time is cleaning up mistakes the tests caught. The same can be said for humans, though.

  • Black Mirror AI

    Jump

  • Some details. One of the major players doing the tar pit strategy is Cloudflare. They're a giant in networking and infrastructure, and they use AI (more traditional, nit LLMs) ubiquitously to detect bots. So it is an arms race, but one where both sides have massive incentives.

    Making nonsense is indeed detectable, but that misunderstands the purpose: economics. Scraping bots are used because they're a cheap way to get training data. If you make a non zero portion of training data poisonous you'd have to spend increasingly many resources to filter it out. The better the nonsense, the harder to detect. Cloudflare is known it use small LLMs to generate the nonsense, hence requiring systems at least that complex to differentiate it.

    So in short the tar pit with garbage data actually decreases the average value of scraped data for bots that ignore do not scrape instructions.

  • Official page on the new EPA.gov, alleging that Biden gave $2 billion in "gold bars" to Stacey Abrams's group, linking to press coverage from some hilariously chosen press

    Jump

  • Yeah the title is pretty accurate, no blame there. Comments were just amusing.

  • Official page on the new EPA.gov, alleging that Biden gave $2 billion in "gold bars" to Stacey Abrams's group, linking to press coverage from some hilariously chosen press

    Jump

  • Ironic that a forum about disinfo has some thinking the gold bars, thrown off the Titanic, here are literal.

  • Deleted

    Are there any uncensored LLM available publicly?

    Jump

  • Was about to post a Hugging Face link til I finished reading. For what it's worth, once you have Ollama installed it's a single command to download, install, and immediately drop into a chat with a model, either from Ollama's library or Hugging Face, or anyone else. On Arch the entire process to get it working with gpu acceleration was installing 2 packages then start ollama.

  • Is it possible to fix one's eyesight? What are working methods? What is to be cautious about?

    Jump

  • Orthokeratology lenses reshape your cornea overnight. Been using them for years, heartily recommend.

  • ‘Would you survive 72 hours?’ Germany and the Nordic countries prepare citizens for possible war

    Jump

  • Important context: Sweden and especially Finland have long had a defense model based around literally everyone contributing to defending against an occupation. The real change is they don't consider that enough of a deterrent anymore, hence joining NATO, after seeing Russia bloody itself against Ukraine for several years.

  • Special counsel Jack Smith drops election subversion case against Donald Trump

    Jump

  • Key detail: they're not dropping it because they're giving up, the judge dismissed it without prejudice, which means that in 4 years they can pick the case back up. Under a Trump DoJ the case would likely have ended with prejudice, closing it permanently.