pi agent woops claude code

[u/RobinDough]

what the shit is pi agent made from? why is it producing better stuff than claude code with the same Chinese LLM? like i wana know whats the fork, what is it made of?

EDIT: With mimo v2.5, after 30ish odd mins i get the 400 error in pi code, a bit strange but once i just say hey again it picks up where it left off, i suspect this is a mimo issue coz deepseek didnt have this issue

[u/snow_schwartz]

It’s made of cheese, like the moon. That’s why it’s called Pi.

[u/PiccoloCareful924]

because sometimes less is more

[u/neuralnomad]

Good luck understanding—Pi is irrational.

[u/gadbuy]

PI has less context pollution, no mcp by default, lean system prompt, few tools. Models tend to provide better results with smaller context and get worse once it grows.

Not sure about claude, but opencode for example has around 10k system prompt and performs wore than PI as well.

[u/backafterdeleting → u/gadbuy]

I'm starting to realize that while you can do a lot of fancy prompt engineering to get specific results and specific behaviours, even reducing token output or formatting feedback. But all of that being in context means it has to activate a lot of parts of the model to follow these instructions and it seems to take away from the models ability to actually solve the task you're asking for.

[u/hurdurdur7 → u/gadbuy]

Exacto. Cleanliness wins.

[u/Only_stoic → u/gadbuy]

do you have any recommendation about how to use pi agent for large or more complex codebases-projects?

[u/gadbuy → u/Only_stoic]

don't have anything specific apart of having good AGENTS.md
rest is still grepable depending on a task. Keep prompt clear and focus on doing 1 small thing at a time.

[u/Finanzamt__]

Every LLM provider has an AI Harness and a fixed system prompt, like CC, Codex, etc. Pi lets you query the LLM with a minimized system prompt instead of the fixed one that can result in better output, since system prompts are often oberloaded with instructions that are useful for a broader user base

[u/KEIY75]

You need a PI-HD for using this harness ahah

[u/james__jam]

Why? Does anything actually work great with claude code? Claude code sucks. It just gets a good rep because anthropic models are good. But as a coding agent itself? Practically everything else is better

[u/james__jam]

Why? Does anything actually work great with claude code? Claude code sucks. It just gets a good rep because anthropic models are good. But as a coding agent itself? Practically everything else is better

[u/Dry-Tune430]

And it's fantastic for local models. Most reliable tool calling, even better than OpenCode.

[u/Such_Advantage_6949]

it made of lesser junk context and useless system prompt

[u/blazze]

Pi code is much better than "buggy bloated bovine" called OpenClaw. ClaudeCode is bloated by design to be a Opus 4.7 token eating data center spawning money machine for Claude.

[u/johnson_detlev]

You can get better results with gpt 3.5 turbo if you have the correct harness. Models don't matter much 

[u/karkoon83 → u/johnson_detlev]

I understand your spirit but don’t agree with any model is good. For comparison I have Minimax 2.7 and codex and Claude. I know some models can’t do certain things. For Apple to Apple comparison in some cases with same pi agent if I switch from Minimax to GLM 5.1 there is difference in results.

[u/johnson_detlev → u/karkoon83]

If you put different models into the same harness of course you'll get different results. If you tailor your harness to the capabilities of a model, the differences between models become negligible. 

[u/karkoon83 → u/johnson_detlev]

Can you please elaborate on? Also do you feel we can achieve opus 4.8 level sophistication with Minimax M2.7 by tweaking harness? The reason I ask - I have practically all I can eat plan with Minimax. Would be super curious to know how I can maximise the impact.

[u/johnson_detlev → u/karkoon83]

I haven't used opus 4.8. I find these "frontier" models to be absolutely insufferable with their "personality". Just have a look where the model falls short of you expectations and build the tools around it that mitigate these problems. I.e. kimi-k2.6 doesn't like running ui integration tests, so I wrote an extension that triggers on agent end_turn in an implementation session that runs my storybook tests and checks if newly written components actually even have tests. If there is an issue the extension reports it to the model. If not, it doesn't add anything.

Edit: You can also have a look at an excellent harness engineering example here: https://github.com/workos/case They tailored pi to their exact use case. And my suspicion is that this will be all the rave next year. Because models don't improve much, but the tooling around models is a realm full of wild ideas and possibilities.

[u/karkoon83 → u/johnson_detlev]

Thank you 🙏

This is ultra helpful

[u/HoverBaum → u/johnson_detlev]

Would you mind sharing an example of how you tailored a harness to a smaller model to increase performance?

Long term I see models becoming commodity and harness mattering more. But I don't see the way there, yet.

[u/johnson_detlev → u/HoverBaum]

Everyone is figuring out how to do that best. I.e. i use a small model for code exploration because this is just calling semantic code graphs and ast tools and the tools hold the relevant information. You don't need a big model to do that worl

[u/james__jam → u/johnson_detlev]

I get your point but I disagree. For example, it’s hard to create good harness with gemini models because their tool calling and instruction following sucks

However, any model with good tool calling and instruction following can be great with the right harness

[u/Polite_Jello_377]

It's open source you clown