Does local AI have a future? The models are getting ridiculously big and any storage hardware is hoarded by few companies for next 2 years and nvidia has stopped making consumer GPU for this year.
It seems to me there is no chance local ML is going to be anywhere out of the toy status comparing to closed source ones in short term
Mistral have small variants (3B, 8B, 14B, etc.), as do others like IBM Granite and Qwen. Then there are finetunes based on these models, depending on your workflow/requirements.
320 tok/s PP and 42 tok/s TG with 4bit quant and MLX. Llama.cpp was half for this model but afaik has improved a few days ago, I haven't yet tested though.
I have tried many tools locally and was never really happy with any. I tried finally Qwen Code CLI assuming that it would run well with a Qwen model and it does. YMMV, I mostly do javascript and Python. Most important setting was to set the max context size, it then auto compacts before reaching it. I run with 65536 but may raise this a bit.
Last not least OpenCode is VC funded, at some point they will have to make money while Gemini CLI / Qwen CLI are not the primary products of the companies but definitely dog-fooded.
Most of Gemini's users are Search converts doing extended-Search-like behaviors.
Agentic workflows are a VERY small percentage of all LLM usage at the moment. As that market becomes more important, Google will pour more resources into it.
> Agentic workflows are a VERY small percentage of all LLM usage at the moment. As that market becomes more important, Google will pour more resources into it.
I do wonder what percentage of revenue they are. I expect it's very outsized relative to usage (e.g. approximately nobody who is receiving them is paying for those summaries at the top of search results)
> Most agent actions on our public API are low-risk and reversible. Software engineering accounted for nearly 50% of agentic activity, but we saw emerging usage in healthcare, finance, and cybersecurity.
this doesn’t answer your question, but maybe Google is comfortable with driving traffic and dependency through their platform until they can do something like this
In mid-2024, Anthropic made the deliberate decision to stop chasing benchmarks and focus on practical value. There was a lot of skepticism at the time, but it's proven to be a prescient decision.
Benchmarks are basically straight up meaningless at this point in my experience. If they mattered and were the whole story, those Chinese open models would be stomping the competition right now. Instead they're merely decent when you use them in anger for real work.
I'll withhold judgement until I've tried to use it.
Ranking Codex 5.2 ahead of plain 5.2 doesn't make sense. Codex is expressly designed for coding tasks. Not systems design, not problem analysis, and definitely not banking, but actually solving specific programming tasks (and it's very, very good at this). GPT 5.2 (non-codex) is better in every other way.
Codex has been post-trained for coding, including agentic coding tasks.
It's certainly not impossible that the better long-horizon agentic performance in Codex overcomes any deficiencies in outright banking knowledge that Codex 5.2 has vs plain 5.2.
Let's give it a couple of days since no one believes anything from benchmarks, especially from the Gemini team (or Meta).
If we see on HN that people are willing switching their coding environment, we'll know "hot damn they cooked" otherwise this is another wiff by Google.
It's like anything Google - they do the cool part and then lose interest with the last 10%. Writing code is easy, building products that print money is hard.
That monopoly is worth less as time goes by and people more and more use LLMs or similar systems to search for info. In my case I've cut down a lot of Googling since more competent LLMs appeared.
Accomplish the task I give to it without fighting me with it.
I think this is classic precision/recall issue: the model needs to stay on task, but also infer what user might want but not explicitly stated. Gemini seems particularly bad that recall, where it goes out of bounds
Flash models are nowhere near Pro models in daily use. Much higher hallucinations, and easy to get into a death sprawl of failed tool uses and never come out
You should always take those claim that smaller models are as capable as larger models with a grain of salt.
Flash model n is generally a slightly better Pro model (n-1), in other words you get to use the previously premium model as a cheaper/faster version. That has value.
They do have value, because they are much much cheaper.
But no, 3.0 flash is not as good as 2.5 pro, I use both of them extensively, especially in translation. 3.0 flash will confidently mistranslate some certain things, while 2.5 pro will not.
Totally fair. Translation is one of those specific domains where model size correlates directly with quality, and no amount of architectural efficiency can fully replace parameter count.
I am fine with the founder joining OpenAI, he gets to get paid regardless.
I am not confident that the open source version will get the maintenance it deserves though, now the founder has already exited. There is no incentive for OpenAI to keep the open sourced version better than their future closed source alternative.
It's not even always a more efficient form of labour. I've experienced many scenarios with AI where prompting it to do the right thing takes longer and requires writing/reading more text compared to writing the code myself.
It seems to me there is no chance local ML is going to be anywhere out of the toy status comparing to closed source ones in short term
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