I had the same trouble but I also had some success. I did a little write up on what I got to work.
replyhttps://joeldare.com/a-local-open-weight-model-builds-its-fi...
I think it can be made to work if you’re willing to tweak the prompts.
But, to be honest, I find my time to be worth more than the $20 I spend on a light weight frontier plan that can do this without the hand holding.
I want local to succeed, however, because of things like Anthropic stopping games based on other games.
One RTX4090 with 24G gives me about 200k KV cache with Q4_K_M quant for typica coding tasks, via opencode. I use Q6_K with 60k cache if the work doesn't require scanning too many files. Q6_K is really good.
replyLocal models requires investment of about $2500 into hardware, so that you can run models at decent tok/sec. You then need to basically write custom harnesses. All of my stuff runs on a llama.cpp backend on a dedicated inference machine, and a client runs custom code built with OpenAPI library to make inference calls to accomplish things.
reply> need to basically write custom harnesses
replyCurious, why/how does that help? Or do you have some niche needs?
The latent space of models, which basically steers the token generation into a particular direction is much coarser with the local models compared to big models. Small models known fundamentals well, but do not have the breadth to realize the particularities of combination of fundamental task given certain criteria.
replyAs such, if you want to replicate big model behaviour, you need to basically do context augumentation. How you do that is up to you for your own needs.
But my question remains: isn't something like pi.dev good enough for this? Or are you using custom harness to describe the scripts/skills/etc that you create yourself?
replyI am cheerfully using Opencode and pi.dev on a local model without thinking about any of that stuff, just running it straight out of the box and it's working great. I have access to an RTX6000 on a remote machine so using it for 'free' and it's replaced Claude for my personal projects. It gets stuck occasionally and gets things wrong from time to time, but so does everything.
replyNo. All agents out there basically operate on skills, mempry, and tools. There are almost none that do context injection with every prompt. You basically need an external system that modifies every submitted prompt with additional context
reply> You then need to basically write custom harnesses.
replyWhy? Most existing harnesses let you point them at whatever API you want. I'm currently have a great time with Juggler.
I find myself having to write a lot more skills for Qwen3.8-27b to effectively use tools it hasn’t seen before, versus virtually no skill-writing needed for Claude anymore. It seems to understand novel tools and when to use them pretty intuitively.
replyFor basic tasks, yea. For more advanced tasks, where the agents running on big llms will do more actions autonomously, the smaller models will sometimes stop execution or go off in wrong directions.
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