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I like the Pi approach, but I think I didn't "hold it correctly" so to say. I would like to migrate away from Claude Code and use Pi as my "peimary" harness. I

I like the Pi approach, but I think I didn't "hold it correctly" so to say. I would like to migrate away from Claude Code and use Pi as my "peimary" harness. I really like in particular how it manages conversation trees and branches. But I think I didn't do a good job in customizing it for my work. While nothing dramatic, I think the LLM I was using did a better job on Claude Code than on Pi a couple of time when I tried giving it the same work. I was not sure how to improve on it though.

mdgld 3mo ago
That’s why I like things like oh-my-pi and lazypi, nothing’s stopping you from modifying it after you install; I find that when I’m working on my own sometimes I brick myself into a corner and it’s nice to see someone else’s starting point for reference.
- monkmartinez 3mo ago
  This is so easy to do because we are unable to contemplate every edge case at the time of inception right? At least that is what I am telling myself as I brick myself on a "manifest driven system" that surely will detect drift as I bolt more and more on, right? no... bricked as fah. At least I am learning to build modular so I can reuse parts like image gen, audio gen (STT/TTS), knowledge management. I have probably built 4 of these systems in the last year, each one gets better and lasts longer until I brick the crap out of it. Super fun.
sdesol 3mo ago
What was lacking? This is self promotional but I am working on https://github.com/gitsense/pi-brains https://github.com/gitsense/pi-brains which is designed around the Pi philosophy of less is better by focusing on ondemand context/guidance. I won't bloat the context unless the LLM needs to do something I know it will need better guidance with. I have a demo repo for this at https://github.com/gitsense/gsc-rules-demos https://github.com/gitsense/gsc-rules-demos One of the examples is, if I know the agent is reading a specific file, I will inject additional context. So if the agent never needs to do something in a certain file or directory, I don't need to pollute the context with "what it may need to know".
- surgical_fire 3mo ago
  It's difficult to be very specific, because this was not a formal experiment. I was using LLM collaboratively to help me setting up and document a home server. I was using DeepSeek for that matter. I tried some tasks on Claude Code and some on Pi. Subjectively, I felt that it was marginally "smarter" on Claude Code. It would figure things out better, that sort of thing. I am still using Pi btw. My current set up is using MiMo on Pi as a planner, ans DS in Claude Code to validate/execute the plan. I may try moving it all to Pi, but I wonder if I should learn how to better configure the things there.
  - sdesol 3mo ago
    > I may try moving it all to Pi, but I wonder if I should learn how to better configure the things there. Honestly if the difference was marginal, I would move to Pi. I just tried layzpi and I had the agent write/compile a hello world in c and it required 20k in tokens. My minimal setup required 5.3k in tokens. People will say the cost is minimal since this is cached but 15k is a lot since that needs to be reasoned by the LLM. I haven't looked at Claude Code but I read somewhere the system prompt is like 20k so I can see how Claude Code might have seem smarter as what you are working was probably addressed in the system prompt. For me the bloat is not worth it since I am more interested in the LLM being able to reason better.

mdgld 3mo ago

That’s why I like things like oh-my-pi and lazypi, nothing’s stopping you from modifying it after you install; I find that when I’m working on my own sometimes I brick myself into a corner and it’s nice to see someone else’s starting point for reference.

monkmartinez 3mo ago
This is so easy to do because we are unable to contemplate every edge case at the time of inception right? At least that is what I am telling myself as I brick myself on a "manifest driven system" that surely will detect drift as I bolt more and more on, right? no... bricked as fah. At least I am learning to build modular so I can reuse parts like image gen, audio gen (STT/TTS), knowledge management. I have probably built 4 of these systems in the last year, each one gets better and lasts longer until I brick the crap out of it. Super fun.

sdesol 3mo ago

What was lacking? This is self promotional but I am working on https://github.com/gitsense/pi-brains https://github.com/gitsense/pi-brains which is designed around the Pi philosophy of less is better by focusing on ondemand context/guidance. I won't bloat the context unless the LLM needs to do something I know it will need better guidance with. I have a demo repo for this at https://github.com/gitsense/gsc-rules-demos https://github.com/gitsense/gsc-rules-demos One of the examples is, if I know the agent is reading a specific file, I will inject additional context. So if the agent never needs to do something in a certain file or directory, I don't need to pollute the context with "what it may need to know".

surgical_fire 3mo ago
It's difficult to be very specific, because this was not a formal experiment. I was using LLM collaboratively to help me setting up and document a home server. I was using DeepSeek for that matter. I tried some tasks on Claude Code and some on Pi. Subjectively, I felt that it was marginally "smarter" on Claude Code. It would figure things out better, that sort of thing. I am still using Pi btw. My current set up is using MiMo on Pi as a planner, ans DS in Claude Code to validate/execute the plan. I may try moving it all to Pi, but I wonder if I should learn how to better configure the things there.
- sdesol 3mo ago
  > I may try moving it all to Pi, but I wonder if I should learn how to better configure the things there. Honestly if the difference was marginal, I would move to Pi. I just tried layzpi and I had the agent write/compile a hello world in c and it required 20k in tokens. My minimal setup required 5.3k in tokens. People will say the cost is minimal since this is cached but 15k is a lot since that needs to be reasoned by the LLM. I haven't looked at Claude Code but I read somewhere the system prompt is like 20k so I can see how Claude Code might have seem smarter as what you are working was probably addressed in the system prompt. For me the bloat is not worth it since I am more interested in the LLM being able to reason better.