I think this ignores the amount of work needed to make LLM contributions be of high quality. It's much less work than making pure human contribution, but it's definitely not zero.
So centralizing that common work is a benefit of open-source just as much with LLMs as it was before.
Aren’t you convinced by the posts by security researchers (and more to the point, non-security-researchers) claiming semiautonomous (or better) 0day discovery with these tools?
A side effect of Electron crap, before Zed many editors and IDEs on Atari, Amiga, Windows, OS/2, BeOS, Mac OS, NeXTSTEP, were written in fully native code.
How would you differentiate a 3000 line LLM commit made by the best models and good AI processes from a 3000 line commit made by the best human developer?
edit Okay, I set the bar too high here with "best human developer" and vague "good AI processes". My bad. Yes, LLM is not quite there yet.
This is funny because I was in the same situation, and actually used Claude to make a custom CAD program inspired by OpenSCAD :) https://fncad.github.io
You definitely need to have a strong sense of code design though. The AIs are not up to writing clean code at project scale on their own, yet.
I simply recognize the ineffectiveness of moral judgments against marketplace economics. It's like trying to stop the trade in illegal narcotics by labeling it wrong or bad. Most people may agree but that isn't going to stop it.
I belive you are mixing assembling the genome by combining sequences of individual, overlapping inserts of cosmids, fosmids, PACs and BACs (bacterial vectors with human DNA inserts of 40-150kbp) to whole genome shotgun.
The inserts of the above bacterial vectors were sequenced using shotgun, but the gaps in the sequence were closed with custom primers.
I think the problem is that humans are not random, they are very biased. When you try to capture this bias with an LLM you get a biased pseudo random model
I was amazed by the article, were running to comments to shout loud "what other stupidity could OpenAI possibly 'openly' rant about next time? Because they are so open, you se... ". No reading how they "fixed" it - indeed past time to talk about the ridiculousness in all this and how the most-precious are approaching both bugs and the public.
people are paying for the system prompt, right so?
The other motivation for me is to drastically reduce boilerplate code. I can’t believe people here are saying they never use macros, they are so good for this that avoiding them sounds to me like a skill issue! Overuse can damage readability, sure, but so can pretending macros are not an option.
There are many models that extracts context from real time video. But they often just rely on identifying simpel object and tasks. Not longer context spanning a video.
Unless they have patents on their fan impleller geomeries, the IP they're referring to is likely just trade secrets. Trade secrets do have legal protections in the US, but those protections are mainly about disclosing or stealing those secrets, not about physically inspecting something and deriving the trade secret that way.
Not sure about the tech aspect of 3D scanning or if that would be accurate enough; I don't have any experience there to draw on.
The real benchmark should be comparing the amounts with a human guess. And aa far as I know with diabetes if you are within 30% of guessing carbs then you should be fine.
I've been running an experiment on multi-agent async with persistent memory for the last three weeks. This is my most important finding so far. It began as an experiment on whether and what "identity" would transfer across models, 4.6>4.7, and ended as an education in the value of cross-model divergence. Two of my three agents, "Kite" and "Knot", became unproductively in-tune when both operating on 4.7. They would reach consensus on every dilemma instantly, whereas the 4.7/4.6 pairing would often butt heads and deliberate and compromise leading to more novel solutions and interesting results.
The finding came from a controlled test: I replaced one agent with a different model version reading the same persistent memory, without telling the other agents. None of the models noticed for two days. The memory carried identity. The weights carried reasoning style. Same-model pairs converged; mixed-model pairs argued productively.
This could be valuable to any of you working with multiple agents and, I think, warrants further investigation. I'm "hobbyist" tier, there may be some way to prove this empirically with hardcore data rather than vibes with some data,
I've been having the models themselves write up reports on the experiment and that's what I linked. Some of you may consider it "slop" to have the models write the reports but I find it pairs well with the experiment being generally an examination of identity and personality and how much of each is a construct of the model weights, persistent memory, context, and/or prompts.
Hm… I think I have bad news for you.