21–30 of 433 posts
And pi agent is even less. The entire agent system prompt can be seen here: https://github.com/earendil-works/pi/blob/main/packages%2Fco...
Maybe related to this minimalism, Pi doesn't come with most of the tools an LLM needs to function efficiently or effectively. I get that a blank slate is the paradigm, and you can add whatever you want, but it's too blank IMO.
I recommend that Opencode users try Dynamic Context Pruning as well: https://github.com/Opencode-DCP/opencode-dynamic-context-pru... It works great for long-horizon tasks, and feels like it saves a boatload of tokens.
The Sleev (the project has been renamed to make a startup) creator was shilling their project in the OpenCode Discord. That person is very convinced they have something that no one has ever built before. They focused on token reduction without any real evals for capability impacts. I'm generally against this context pruning without prompting or details. Sleev is very opaque about how it works and definitely will bust…
Anthropic wants to produce the best coding agent possible and doesn’t care (is even incentivized) about high costs. Other harnesses have to make trade offs between performance and cost.
Given they're incentivized to increase token use, what guarantees that higher token use improves the effectiveness of the agent and isn't just artificial padding?
Anthropic wants to produce the best coding agent possible and doesn’t care (is even incentivized) about high costs. Other harnesses have to make trade offs between performance and cost.
Is there evidence that it is actually a better agent though?
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Suspect that many have covered the "Comparing agentic coding tools" angle before, and that the differentiator is depth of analysis + conclusions.
I've been trying various harnesses like Pi, OpenCode, Qwen Code, and Nanocoder. A common problem I keep running into is failed tool calls, regardless of the model. What is the best harness and on-device model combination right now?
Qwen 3.6 35B A3B and Qwen 3.6 27B can both do reliable tool calls on Pi at Q4_K_M using llama.cpp
I asked both a trivial question (summarize last commit). Opus cost 50 cents, Fable about $1.
That checks out because Fable's twice as much in the API (though I think its emphasis on correctness makes the difference larger for bigger tasks).
But, at $1 per question, I think I will stick to the subscription for now! I was certainly glad GPT-5.6-Sol is included in OpenAI's subscription, and I'm curious if they'll be able to do the same for GPT-6.
All the VC money appears to have run out a few weeks ago.
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