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?
I think the point is less "how can we throw shade on the OP" and more "a harness can enable a lot of models to do very serious cybersec, glm 5.2 is one of them"
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?
unlikely scenario as the main mandate of the federal reserve is to keep inflation in check. inflation reaching such levels would also cause interest rates to rise astronomically, and this would make the debt harder to service
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 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 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.
This is the right call. llama.cpp has dozens of hand-tuned CUDA kernels across Q4_K_M, Q5_K_S, Q8_0 and other quant formats, each targeting different hardware profiles. An autoresearch approach that could optimize these per-GPU would be huge — right now performance varies wildly between, say, an RTX 3090 and a 5070 Ti on the same quant format because the kernels are tuned for specific architectures. The hardware diversity in the llama.cpp user base is exactly where automated kernel search has the most to gain.
If I'd like to benchmark a new language / compile backend for LLM inference, what would be some good projects to try? If I'd start from tinygpt, what would make sense as the next step?
Damn but I guess I can still understand it but can there be a blanket ban of Chinese model themselves period or the idea being that you can't use Chinese models for american citizens or similar under the US constitution.
This is a big deal of itself and thanks for sharing but I feel like that the above idea of blanket banning chinese models is defintiely something that American AI companies want and with their lobbying efforts and many other things, I think that its up to genuine debate if this would be reality or not.
Good that the US pissed so much softpower and goodwill away around the World that they won't convince other countries to ban these models. And only banning them domestically would hurt them on their own too much
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?
Which I guess makes what semgrep sells obsolete. Unless they have built a pareto-optimal point in terms of capabilities and token usage maybe?