veselin

Here, it appears they compare a single prompt "find IDOR", against a multi-agent system. However, one can also start far more sophisticated skills that spin up subagents and mostly do the same in Claude Code, Codex, OpenCode, Pi, etc.

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 more I think on the problem, the more I believe this will be solved with US interventions. And the interventions will increase inflation by a lot, so prices will not go down.

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?

Claude code itself spins a lot of its subagents with Haiku. The model has low hallucination rate, so it is great for exploration tasks. I guess this is what the best purpose of this model here will be as well. Which is a lot of tokens - many tasks spin multiple exploration agents before the planning or fixing, that is then just a few tool calls.

I would argue that with AI, this becomes less of an issue. Connect N services, deploy to bare metal. Granted, AI is an additional cost now local or remote. But so is the MacBook people use to develop their software.

Exactly our experience too. Effectively we catch these and on these status codes, we send to OpenAI. Retrying the same query in Gemini has high chance to give kind-of the same status code.

I think this is a promise, probably also for spec driven development. You write the spec, the whole thing can be reimplemented in rust tomorrow. Make small modules or libraries.

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.

I guess we will have a lot more benefits if we can get this to work on something like llama.cpp - since it really has a lot of kernels for different quantizations, a lot of home users, high hardware diversity - so it is a likely place with highest bang for the buck.

I guess they can be a contributor there.

I think they put two things:

* 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.

I am actually going to complain about this: that neither of the Gemini models are not preview ones.

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?

What is the state of using quants? For chat models, a few errors or lost intelligence may matter a little. But what is happening to tool calling in coding agents? Does it fail catastrophically after a few steps in the agent?

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

Also, would vllm be a good option?

I am taking for SWE bench style problems where Todo doesn't help, except for more parallelism.

I run evals and the Todo tool doesn't help most of the time. Usually models on high thinking would maintain Todo/state in their thinking tokens. What Todo helps is for cases like Anthropic models to run more parallel tool calls. If there is a Todo list call, then some of the actions after are more efficient.

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.

I work a lot on testing also SWE bench verified. This benchmark in my opinion now is good to catch if you got some regression on the agent side.

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.

Anybody knows if one can find an inference provider that offers input token caching? It should be almost required for agentic use - first speed, but also almost all conversations start where the previous ended, so cost may end up quite higher with no caching.

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.