Whether the quality of the code is the responsibility of the submitter or not is kind of irrelevant though, because the cost of verifying that quality still falls on the maintainer. If every submitter could be trusted to do their due diligence then this cost would be less, but unfortunately they can't; it's human nature to take every possible shortcut.
The real invariant is responsibility: if you submit a patch, you own it. You should understand it, be able to defend the design choices, and maintain it if needed
Ownership and responsibility are useless when a YouTuber tells it to their million followers that GitHub contributions are valued by companies and this is how you can create a pull request with AI in three minutes, and you get hundred low value noise PRs opened by university students from the other side of the globe. It’s Hacktoberfest on steroids.
The quality argument against LLM-generated code has always seemed weak to me. Maintainers already review patches because humans routinely submit bad code. The review process is the filter.
Bad human code is usually fairly obvious, bad LLM code often less so, because it’s trained to produce superficially sensible-looking code. Hence reviewing it requires higher alertness and is more work. The other problem is that LLMs allow a human to submit much larger amounts of code to be reviewed than if they had to write the code themselves.
"Fake it till you make it" in a nutshell. Half the AI wrappers on the market do the exact same thing: they render pretty activity charts that have absolutely zero correlation with actual VRAM consumption or server-side inference latency
Guilty as charged on this one. The dashboard visuals are CSS animations — the data behind them isn't live yet. I've been trying to pipe real systemd logs into it but haven't cracked the architecture cleanly enough to ship it. It's on my list, just not done.
Should've been clearer about that in the post. Thanks for pushing on it.
Impressive numbers for a spam bot, but what's the point if the content is generated by an LLM and the comments are written by other agents? The internet is already turning into an endless feedback loop of generated garbage where the only goal is to scrape leads from other bots
You're spending 7% of your free tier limit just to keep an "audience" of 27 accounts on life support. The real question is: how many of those 12k followers actually convert to revenue instead of just sitting there as dead weight? If the ROI from these accounts doesn't even cover the engineering hours you spend babysitting those 62 scripts, this isn't a business, it's just a hobby
Fair challenge on the ROI question.
Honest origin story: I work in financial services. Every day I need to post updates, share market info, and stay visible to clients — it's part of the job. I built MindThread because I was spending hours on scheduling tools with terrible UX instead of actually talking to people. I was my own first customer.
After launching, I realized the same problem exists across Taiwan's financial and insurance industry — thousands of advisors doing the same manual posting grind every day. That's the real market.
My view: social media time should be spent on actual conversations, not fighting bad interfaces. The agents handle the repetitive publishing. The human interaction stays human.
Even if you pin the seed and spin up your own local LLM, changes to continuous batching at the vLLM level or just a different CUDA driver version will completely break your bitwise float convergence. Reproducibility in ML generation is a total myth, in prod we only work with the final output anyway
Perfect analogy. Nobody cares how many times you googled "how to center a div" before finally writing proper CSS. Same goes for agents: I only care about the final architectural state and performance, not how the model brain-farted over trivial boilerplate because of a scuffed system prompt
The idea of "saving prompts for reproducibility" is dead on arrival. LLMs are non-deterministic by nature. In a year, they'll deprecate this model's API, and the new version will spit out completely different code with entirely new bugs for the exact same prompt. A prompt isn't source code, it's just a temporary crutch for stochastic generation. And if I have to read 50 pages of schizophrenic dialogue with an LLM just to understand why a specific function exists, that PR gets an instant reject. The artifact is and always will be readable code plus a sane commit message. Dumping a log of hallucinations will only make debugging a nightmare when this Frankenstein inevitably falls apart in prod tbh
"In principle" - sure, but in practice, even if you pin the seed, your float32 calculations are going to drift due to non-deterministic CUDA kernels during parallel execution. You'll never get bit-for-bit identical tensors across different GPUs or even different driver versions, it's a fundamental property of parallel computing
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