Which I guess makes what semgrep sells obsolete. Unless they have built a pareto-optimal point in terms of capabilities and token usage maybe?

The other alternatives with LLMs becoming more expensive in an Uber-like move may not work due to a lot of competition. I also don't think usage will increase 10x. I don't always have coding tasks for an LLM despite it being good.

My reasons to believe so are outside of what interests HN community and I am neither endorsing this behavior, nor I think it is that simple. But US also has a huge debt that it must service. Wouldn't it be convenient if it was suddenly halved in actual value?

One colleague describes monolith vs microservices as "the grass is greener of the other side".

In the end, having microservices is that that the release process becomes much harder. Every feature spans 3 services at least, with possible incompatibility between some of their versions. Precisely the work you cannot easily automate with LLMs.

https://developers.openai.com/api/reference/resources/respon...

I don't know their internal eval, but I think I have heard it does not hurt or improve performance. But at least this parameter may affect how many comments are in the code.

I guess they can be a contributor there.

* Likely they will seek regulation that would ban some models. Not sure this can work, but they will certainly try.

* Likely they will not release some of their next models in the API.

Anthropic seems the best in this. Everything is in the API on day one. OpenAI tend to want to ask you for subscription, but the API gets there a week or a few later. Now, Gemini 3 is not for production use and this is already the previous iteration. So, does Google even intent to release this model?

I am interesting if I can run it on a 24GB RTX 4090.

Also, would vllm be a good option?

What you need to do is to match the distribution of how the models were RL-ed. So you are right to say that "do X in 200 lines" is a very small part of the job to be done.

However, going above 75%, it is likely about the same. The remaining instances are likely underspecified despite the effort of the authors that made the benchmark "verified". From what I have seen, these are often cases where the problem statement says implement X for Y, but the agent has to simply guess whether to implement the same for other case Y' - which leads to losing or winning an instance.

I would have expected good providers like Together, Fireworks, etc support it, but I can't find it, except if I run vllm myself on self-hosted instances.

Somehow if I take the best models and agents, most hard coding benchmarks are at below 50% and even swe bench verified is like at 75 maybe 80%. Not 95. Assuming agents just solve most problems is incorrect, despite it being really good at first prototypes.

Also in my experience agents are great to a point and then fall off a cliff. Not gradually. Just the type of errors you get past one point is so diverse, one cannot even explain it.

For some of these I see something like 15k followers on X, but then no LinkedIn page for example. Website is always a company you cannot contact and they do everything.

I ended up disabling copilot. The reason is that the completions do not always integrate with the rest of the code, in particular with non-matching brackets. Often it just repeats some other part of the code. I had much fewer cases of this with Cody. But, arguably, the difference is not huge. But then add on top of this choice of models.

I wonder if US monopoly regulation actually starts to work well, which I see some signs of happening, will all this license revert back to fully open source?