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Ask HN: Is the next big thing locally running coding agents?

2 points by baigy ↗ HN
There's extreme price escalation on part of Anthropic, with token spend now approaching levels that have made many-an-enterprise scratch their heads.

At the same time, judging by opensource advances (E.g. Qwen 3.6 27B), hosting a smart enough local LLM on 16GB VRAM (or equivalent) is increasingly becoming a reality. Lastly, I see most coding to be of intermediate difficulty, not beyond.

Seems to me it's a matter of time that people shift to free Claude Code type experiences, powered by local LLMs.

What do you think?

9 comments

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damnitbuilds
I got Qwen 3.6 running locally on 12GB VRAM.

It went:

  AI: "I see you are building a Django project. How can I help?"

  Me: "When I click on the Reload button, it does not set the reload option correctly. Fix this"

     <10 minutes>

  AI: "I see you are building a Django project. How can I help?"
Needs more tweaking of the context window, I think.

Seriously, I agree that this is the future, when OpenAI et al have gone bust.

damnitbuilds
I tweaked it and now I get good, better-than-Copilot answers, on local hardware but a little bit slower than Copilot ( okay, ~10min vs ~1 minute ).

I can take that for the joy of running this locally !

jonahbenton
There are many markets. Qwen 3.6 27b at a high enough quant is good enough for many use cases. But enterprise-consumed tokens come with legal/data protection agreements. They have just gotten comfortable with BYOD- there is no BYOD equivalent set of practices and protections for local LLMs (BYOLLM). So some enterprises are getting back into prem GPU capacity.
giwook
This seems like an obvious progression imo though I think very much subject to change. Open weight models will become better, and memory prices will return to normal prices in a couple years (hopefully).

That being said I think an unpredictable variable here is how the companies building frontier models respond to what should be a noticeable inflection point in consumers turning towards locally hosted open weight models.

There is also a significant amount of compute that is being built out as we speak that should in theory reduce costs for providers of frontier models but that's a whole other can of worms.

Despite all of the very impressive open weight models that are available to us today, Anthropic and OpenAI continue to remain steps ahead of the competition. Most of the biggest and brightest minds in AI are working at frontier labs. It's not hard to foresee that these labs continue to maintain their edge given the amount of expertise and brainpower they've assembled.

Assuming frontier models continue to maintain their edge, even if it's on a subset of tasks (e.g. reasoning, judgment, planning), I see a convergence towards a hybrid workflow where both frontier and local models are used for specific tasks. e.g. Claude for reasoning, planning, judgment, with intelligent routing to cheap/free models tuned for certain tasks.

verdverm
Already there friend! I just posted a Show HN from using opencode + qwen36moe output to modernize my old PhD research, surreal experience
potsandpans
I agree. I run gemma4 31b int4 quant on my 5090 and find that's it's quite capable for self contained tasks. There are larger open weights models that are more capable, such as minimax and glm5.1.

I've toyed with the idea of buying two rtx 6000s and vlinking them. But the cost benefit value prop doesn't really pan out quite yet, still cheaper to use open router / some subscription plan for open weights.

I'm looking forward to continued optimization from the open weights labs / models. Qwen and gemma4 are quite capable.

Also I feel what's really under utilized is a suite of llm/ai tools that are completely open and runnable locally.

Hunyuan 3d 2.0, trellis2, unirig

Flux 2 dev, z image, qwen image edit

Ltx 2.3 / wan

Ace step 1.5

All great for creation pipelines. Couple those with other smaller things like sam2 and dino. It's very exciting to see these things producing high quality on local systems.