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If you saw the Claude Code leak, you’d know the harness is anything but simple. It’s a sprawling, labyrinthine mess, but it’s required to make LLMs somewhat deterministic and useful as tools.
Hypothesis: it's a sprawling, labyrinthine mess because it was grown at high speed using Claude Code.
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Claude Code with Opus 4.6 regularly uses sed for multi-line edits, in my experience. On top of it, Pi is famously only exposing 4 tools, which is not just Bash, but far more constrained than CCs 57 or so tools. So, yes, it can work.
I think the problem/limitation would be as much due to context management as tools. Obviously bash plus a few utilities is sufficient to explore/edit the code base, but I can't imagine this working reliably without the models being specifically trained to use specific tools, and recognize/adapt to different versions of them etc. Context management, both within and across sessions, seems the bigger issue. Without the…
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I thought CC only supports it's find/replace edit tool (implemented by CC itself, using Node.js for file access), and is platform agnostic. Are you saying that on linux CC offers "sed" as a tool too? I can't imagine it offers "bash" since that's way too dangerous.
Yes, Claude Code has a Bash tool, and Claude in some cases uses the CLI sed utility (via the Bash tool) for file changes (although it has built-in file update), at least on my Linux machine.
I just asked Claude, and apparently CC makes it's bash tool available on all platforms it runs on (Linux, macOS, Windows WSL, Git for Windows), and doesn't do platform-specifc filtering of bash commands, which would seem to make for some interesting incompatibilities - GNU utils (sed, grep, find) on Linux and Windows, but BSD variants on macOS.
> This is speculative, but I suspect that if we dropped one of the latest, most capable open-weight LLMs, such as GLM-5, into a similar harness, it could likely perform on par with GPT-5.4 in Codex or Claude Opus 4.6 in Claude Code. Unless I'm misunderstanding what's being described here, running Claude Code with different backend models is pretty common. https://docs.z.ai/scenario-example/develop-tools/claude It doe…
I also find OpenCode to be drastically better than Claude Code, to the extent that I'm buying OpenRouter API credits rather than Claude Max because Claude Code just isn't good enough.
I'm frankly amazed at what OpenCode can do with a few custom commands (just for common things like doing a quality review, etc.), and maybe an extra "agent" definition or two. For many projects even most of this isn't necessary. Often I just ask it to write an AGENTS.md that encapsulates a good development workflow, git branch/commit policy, testing and quality standards, and ROADMAP.md plus per milestone markdown files with phases and task tracking, and this is enough.
I'm somewhat interested in these more involved harnesses that automated or enforce more, but I don't know that they'd give me much that I don't have and I think they'd be tough to keep up with the state of the art compared to something less specific.
> long contexts are still expensive and can also introduce additional noise (if there is a lot of irrelevant info) I think spec-driven generation is the antithesis of chat-style coding for this reason. With tools like Claude Code, you are the one tracking what was already built, what interfaces exist, and why something was generated a certain way. I built Ossature[1] around the opposite model. You write specs describ…
Hey, you seem to have similar view on this. I know ideas are cheap but hear me out: You talk with agent A it only modifies this spec, you still chat and can say "make it prettier" but that agent only modifies the spec, the spec could also separate "explicit" from "inferred". And of course agent B which builds only sees the spec. User actually can care about diffs generated by agent A again, because nobody wants to ve…
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> It doesn't perform on par with Anthropic's models in my experience. Why do you think that is the case? Is Anthropic's models just better or do they train the models to somehow work better with the harness?
It is more common now to improve models in agentic systems "in the loop" with reinforcement learning. Anthropic is [very likely] doing this in the backend to systematically improve the performance of their models specifically with their tools. I've done this with Goose at Block with more classic post-training approaches because it was before RL really hit the mainstream as an approach for this. If you want to look at…
Isn't there a better word than harness? I understand the metaphor of leading and constraining a raw power - but I don't like it.
> long contexts are still expensive and can also introduce additional noise (if there is a lot of irrelevant info) I think spec-driven generation is the antithesis of chat-style coding for this reason. With tools like Claude Code, you are the one tracking what was already built, what interfaces exist, and why something was generated a certain way. I built Ossature[1] around the opposite model. You write specs describ…
Waterfall!
Well, for the first problem, if an AI can generate the code in a day or a week, the world hasn't moved very much in that time. (In the future, if everything is moving at the speed of AI, that may no longer be true. For now it is.)
The second problem... if Ossature (or equivalent) warns you of gaps rather than just making stuff up, you could wind up with iterative development of the spec, with the backend code generation being the equivalent of a compiler pass. But at that point, I'm not sure it's fair to call it "waterfall". It's iterative development of the spec, but the spec is all there is - it's the "source code".
I still find it incredible at the power that was unleashed by surrounding an LLM with a simple state machine, and giving it access to bash
If you saw the Claude Code leak, you’d know the harness is anything but simple. It’s a sprawling, labyrinthine mess, but it’s required to make LLMs somewhat deterministic and useful as tools.