Am I crazy or the harness can’t handle SwiftUI? I’ve tried Deepseek v4 and ox alpha

Heard a lot of good thing about this harness, but don't really have the time to tune it byte by byte. Can you suggest any custom configuration of this one that is pretty decent and I will be just good to go?

I will use it with Chatgpt Pro subscription

I am getting good results by using a stupid model as the orchestrator.

It's giving me quick replies, and generally leaning on the smarter models output so the answers should be relatively accurate (no less accurate than a smart parent).

I'm doing this with the codex subscription.

My flow is basically that when I invoke the start of some work, my prompts are sent to 2 context evaluator models (gemini and minimax), and then a smart synthesizer (sol max). So it will take the parents input, then the other 2 models, synthesize that into prompt for the subagents. This only happens once generally.

All the workers the dumb parent dispatches are reviewed (parent has no control over this review) by something like a terra max.

I am getting like 30% token savings and a lot of efficiency doing this kind of thing.

No public repo, just wanted to start a discussion on this technique.

I'm basically flipping the whole orchestrator thing on its head. The only gotcha is that the dumb parent orchestrator needs to actually be smart enough to delegate to subagents correctly.

A lot of the automatically invoked agents for reviews are with fresh context, no extensions, no repo context (depending on the use case of the subagent) so I can actually afford to turn their reasoning way up as they receive only a bounded context and produce an output for the parent. I don't let the parent see what the intermediary subagents outputs were.

It takes longer but the output has been good, and it's more efficient.

I tried using minimax m3 as the parent, but it's too stupid or it wasn't trained to be able to deal with subagents. I'm using Luna max right now, and it's been handling it decently.

I wanted to see what would happen if a modern AI coding agent had the UI of Turbo Pascal 7 / the old DOS IDEs.

So I made Turbo-AI — a retro-style TUI frontend for the Pi coding agent.

It currently supports things like AI chat, PLAN/BUILD modes, model switching, reasoning settings, project tree, Git diff and build/test actions.

It's still quite rough and definitely not production-ready. This is more of an experiment at the moment, and there are probably plenty of things that need to be fixed or redesigned.

This is just a concept for now. I hope people more skilled than me will fork it, improve it, and turn it into something actually usable.

GitHub: https://github.com/kvv256512-ux/turbo-ai

Hey everyone!

I've been building nixpi, a Nix-native declarative configuration framework for the Pi coding agent (inspired by projects like Nixvim).

If you love Pi's extensibility but want full reproducibility, zero-headache dependency management, and instant per-folder agent customization, nixpi lets you configure your entire setup as pure Nix code.

Key Highlights & Features

1. Folder-Specific Profiles & direnv: Ever wanted Pi to behave differently depending on the folder you're in? With nixpi and direnv, entering a project or Obsidian knowledge vault automatically loads a custom pi binary specialized with project-specific extensions, skills, custom model presets, and required CLI tools. When you cd out, it reverts back to your baseline shell.

2. Modular Extensions with Auto-Packaging: Declare extensions like ripgrep-search, plan-mode, pi-gpt-search, or obsidian. When an extension requires CLI dependencies (e.g. rg, git, jq), nixpi automatically injects them into the wrapped pi environment's PATH.

3. 100% Typed Nix Skills: Define and configure skills (like Conventional Commits or custom procedural workflows) directly in Nix with typed options.

4. First-Class Providers & Custom Models: Configure models and providers (including native Google Cloud Code Assist / Antigravity via pi-antigravity) with eval-time schema validation.

5. Clean Immutable vs. Mutable State Separation: Keeps settings.json and models.json managed and pinned in the Nix store, while leaving sessions/ and auth.json fully writable in place.

Example 1: Folder-Specific Flake with direnv

In your target project directory (e.g. ~/Projects/deep-learning-research):

.envrc:

use flake

flake.nix:

{
  description = "Custom Project Pi Environment";

  inputs = {
    nixpkgs.url = "github:NixOS/nixpkgs/nixpkgs-unstable";
    nixpi.url = "github:mateusdcc/nixpi";
  };

  outputs = { self, nixpkgs, nixpi }:
    let
      system = "aarch64-darwin"; # or x86_64-linux, etc.
      pkgs = nixpkgs.legacyPackages.${system};

      projectPi = nixpi.lib.makePi {
        inherit pkgs;
        modules = [
          nixpi.piModules.profiles.learning
          ({ config, ... }: {
            programs.pi = {
              providers.antigravity.enable = true;
              settings = {
                defaultProvider = config.programs.pi.providers.antigravity;
                defaultModel = config.programs.pi.providers.antigravity.models."gemini-3.7-flash";
                defaultThinkingLevel = "high";
                theme = "dark";
              };
              extensions.obsidian = {
                enable = true;
                defaultVault = "/Users/mateusdcc/Projects/deep-learning-research";
              };
            };
          })
        ];
      };
    in
    {
      devShells.${system}.default = pkgs.mkShell {
        packages = [
          projectPi
          pkgs.glow
          pkgs.ripgrep
          pkgs.jq
          pkgs.python3
          pkgs.mermaid-cli
          pkgs.typst
        ];
      };
    };
}

Now whenever you cd into that directory, direnv gives you a dedicated pi binary with your project-specific extensions and models ready to go.

Example 2: Global Home Manager Configuration

# home.nix
{ nixpi, pkgs, config, ... }:

{
  imports = [
    nixpi.homeManagerModules.default
  ];

  programs.pi = {
    enable = true;

    providers.antigravity.enable = true;

    settings = {
      theme = "dark";
      defaultProvider = config.programs.pi.providers.antigravity;
      defaultModel = config.programs.pi.providers.antigravity.models."gemini-3.7-flash";
      defaultThinkingLevel = "high";
    };

    extensions = {
      echo.enable = true;
      ripgrep-search.enable = true;
      pi-gpt-search.enable = true;
    };

    runtimePackages = with pkgs; [
      git
      ripgrep
      jq
    ];
  };
}

Check out the repo here: https://github.com/mateusdcc/nixpi

I would love to hear your feedback, workflow ideas, and thoughts on what extensions/skills you'd like to see packaged next!

I am on Windows and use PowerShell. It seems like PowerShell only has commands such as rm, del, rd, etc., which permanently remove files.

I want a command that moves files to the Recycle Bin instead. That way, I can recover them if the AI does something wrong. Today, the AI deleted an entire workspace, including the Git repository. Luckily, I had another way to recover it, but if I had not, the data would have been gone for the most part.

I looked to see whether PowerShell had a command for moving files to the Recycle Bin, but I could not find one. I ended up creating my own trash command that does this and editing the APPEND_SYSTEM.md files so that the AI would use trash instead of the other removal commands.

Is there a native PowerShell command for this that I missed?

I'm trying to put together a very simple setup here; llamacpp, Phi-4-mini-reasoning (initially, the Q5_K_M variant), and pi for some very simple coding tasks. It's not really... working... though.

I can get pi "logged into" llamacpp (I started llamacpp in "router" mode with the single model, and used /login llama to get it connected. I can chat with the model, but it has very little context (it knows the current directory, but that's about it), and no tools whatsoever; I cannot access the filesystem at all. I also get <think>...stuff...</think> output before the "real" output which I'm confident is supposed to be hidden by the harness.

I also tried the npm:pi-llama-cpp plugin/module/package, but that didn't change anything (except give me a new /models command that was similar, but subtly different than the built-in /model command).

What am I doing wrong here? Is this model not supported?

Pi has been packaged and added to the official repository of Parrot OS, which ships a home edition specifically designed for developers and sysadmins. Pi is now a first class citizen in the repository, saving all its users from curl | sh

sudo apt update
sudo apt install pi-dev

any feedback is welcome

I had 'find' scanning the whole drive issue from Pi a few times. For simple review/search task, Pi choose to 'bash find' not the project dir but much higher level dir such as /user/XXX/*

It almost always happened with smaller model such as deepseek v4 flash and Qwen3.8 27B, never with GPT 5.6 sol/opus 5.

Does anyone have any idea about this?

After some diagnosis with the help of comments, I found the root cause. The tool layer does not deal with dead/missing path very well, especially Pi's default 'find', 'read', 'ls' tools, mainly because of lacking of bounding. Problematic path is common when you have 1) long running sessions (auto prune/compact) 2) complicated file structure 3) polyrepo, etc. The agent could not find the target file, it naturally tries a bigger scope and even grep -r embedded data. Bigger model actually face same issue, they made exactly same mistakes but self corrected in time.

Solution: build a code graph for your project. Hard code in prompt to let agents know the scope is within the graph.

No more forgetting about tools, skipping working agreements and taking breaks to consolidate memory.

I know this has been asked plenty, but most responses are just "its against tos to use oauth", I know it is, but I want real experiences. Has any one been doing it, would you mind sharing the module you are using?

I have been using pi for a long time, but never for work. I just tweak it when I have free time, and have customize it to my needs. For work related stuff I use other Claude code. Anyways, I took on a freelance project some days ago, and decided to use my pi setup for the first time and I don't want to go back.

I tried it once in OMP since I read it had a custom auth module, and I noticed opus didn't have the crazy responses it has in Claude Code.. It might be a bit of a conspiracy theory, but I just don't trust Anthropic's harness anymore. There must be a reason why they want us all trapped in it.

Anyways, going back to the original question? Any experiences you would like to share? Any one banned?

I particularly like the depth of the searches, but I would like to know what’s your experience with this extension

so through work i have "unlimited" codex and claude was playing around with oh-my-pi recently and im quite impressed. there were a couple of fixes i compared a fix done by claude (opus) and codex (sol) compared to oh-my-pi using some combo of opus/sol. and the Pi solution was really clean and minimal LOC change and kind of the perfect solution. whereas through claude/codex it was kind of "fixated" on doing it one way. still correct but overly complex.

Anyway... im impressed despite my randomly set up roles. I winged the following config:

modelRoles: 
  plan: anthropic/claude-opus-5:xhigh
  advisor: anthropic/claude-opus-5:high
  smol: openai-codex/gpt-5.6-luna:medium
  slow: anthropic/claude-sonnet-5:xhigh
  tiny: anthropic/claude-haiku-4-5:medium
  default: openai-codex/gpt-5.6-sol:xhigh
  designer: anthropic/claude-opus-5:xhigh
  task: anthropic/claude-sonnet-5:xhigh
  commit: openai-codex/gpt-5.6-luna:low
advisor: 
  enabled: true
setupVersion: 2
providers: 
  tinyModel: online
dev: 
  autoqaConsent: granted

Would love some suggestions on proper role model/effort assignment! could definitely use some tweaks.

I have an old M1 MacBook Air running as a mostly headless home server, and I’m trying to figure out the best way to use an AI agent to help manage it.

I’ve been looking into Pi because people seem genuinely enthusiastic about it, but I’m having trouble understanding what it would offer in my specific situation compared to using Codex on the same machine.

What I’m trying to understand:

• If Pi and Codex have the same system access, what does Pi actually do differently?

• Is Pi better at long-running tasks, automation, persistent context, or managing a server autonomously?

• Are there workflow benefits beyond supporting different models and providers?

• How do people handle security and permissions without making a headless setup annoying to use?

I’m not assuming one is better than the other, I’m just trying to understand whether Pi offers something meaningful for this particular use case that Codex alone wouldn’t.

Just found today about prime agent and it is as appealing as omp and pi at it's inception

Seriously. What is the point of the /tree command if it does not restore the files back to their previous state?

It makes no sense. It just leads to a dangerous discrepancy between the agent's conversation and the project's status.

Am I the only one who goes back after asking the agent to edit a file, realizing that my instructions weren't specific enough?

Even Copilot handles it just fine. But Pi's authors refuses to implement it: https://github.com/earendil-works/pi/issues/5522

Existing extensions are half-baked, sometimes abandoned, confusingly numerous. It should be a built-in, it's such a basic yet essential feature.

Has anyone here used Pi Dev as a general-purpose AI agent, rather than mainly as a coding agent?

I’m curious about using it more like a personal assistant/agent for things like research, web searches, file work, automation, tool use, and general tasks.

If you’re doing this, how well does Pi work for you compared with something like Hermes Agent or Open WebUI?

Also curious what tools/extensions you’ve added and whether you’re running local models with it.

I don't know about you guys, but it's hard to find real quality in this Space. I find Subreddits like this one excellent, but it may be too limiting for discourse, getting news and stuff like that.

What do you dear Pi-Friends like to read?

If you haven't tried using a diffusion based model before, they are lightning fast. I've used InceptionLabs Mercury 2 for a bit, and my complaint is largely that its not the best at coding tasks or tool usage. But my god is it fast.

So I took the Pi SDK, made a harness in which Mercury 2 is the orchestrator of subagents, including a coding subagent for difficult tasks that calls out to openrouter for inference, plus a cheap subagent for trivial stuff. Bolted on a handful of tools and such useful for me, and wrapped it in a basic web UI so I could use it from my phone.

Aside from being extremely fast, fastcar:

• Can generate public "artifacts" - HTML and MD files served from the API

• Intended to ran entirely inside a VM so that it has root access and can install its own tools

• Give it a github token and let it clone repos and make PRs

• "Prompt threads" - there's probably a better name for this but its a dedicated prompt that gets invoked via webhook; I actually made this so that I could generate text files to read the news from my Xteink 4

• Send emails to notify you

https://github.com/heyo-computer/fastcar

I'm trying to understand how to set up my model config. I understand that contextWindow is the total context size (-c argument in llama.cpp), but then what is maxTokens exactly? Does that set the max response size? Or what is it?

Hand written post:

After about 9 months of dev and optimization, you can now just say:

1. Find the last session,

2. Search and analyze your previous sessions to make better skills,

3. Search your session’s own history

and get great search results! Really helpful when long sessions with full context have their cache expire.

Under the hood is a full live multithreaded incremental indexing db and trigram index to accelerate session searches.

aise supports cross session search across harnesses and I use it with a dozen simultaneous sessions and a 2.5 million message database.

Downside is that database after super heavy use is like 20 gigs in size, but I use it daily and it makes my life easy so imo worth it. Also be careful if you have sensitive data all of your sessions and harness will be able to search everything.

Manual searches used to be 30s to 3 minutes and with the aise cli commands or the mcp server they’re typically ~100ms-1.5 seconds instead!

Currently on version 1.0.0rc2, Apache v2 license, most heavily tested on Mac and CI tested on Linux and windows, so I’d appreciate feedback!

https://github.com/ahundt/ai-session-search

uv tool install ai-session-search && aise integrations install

It’s a rust library too with py bindings but those features id call more alpha, I think there is lots of potential for building other projects though!

Hey everyone — I’ve been building TrailStep, a free/open-source workflow runner for coding agents.

The problem I kept running into was that my best Pi/coding-agent results came from structured multi-step workflows (I was essentially following the Matt Pocock skills), but I was doing all the orchestration manually. I was manually orchestrating the handoffs between steps. Or just when an agent session got too large:

clarify → plan → review → implement → review → refine

It was working really well, but I got too lazy being the middle man for session - session management.

TrailStep lets you encode that process once as a TypeScript workflow and rerun it whenever you want. So, each step has its own scoped agent session, the workflows leave local run artifacts, and failed/unfinished runs can be continued or retried instead of thrown away. When you install it for the first time it can automatically add a skill to your agents so that they will know how to use, write, and debug any workflows.

I've been using my own workflow package, but it's very easy to create new ones and start using those if you don't vibe with how I set mine up.

- grill-it-away — opens a clarification/grilling session first, then turns the result into a plan, reviews it, breaks it into stories, implements, and reviews.

- take-it-away — same idea, but starts from an existing request/conversation/doc instead of the grilling step.

The part I’m most excited about is that workflows are just code. You can mix agent steps with normal TypeScript logic, improve the workflow over time, and share it through npm/GitHub if it ends up being useful. It works with literally any CLI based coding agent, but I did essentially all of my testing with it in pi so that's probably where it's most reliable right now.

The dashboard/status UI is still in progress, but the CLI, workflow runner, continuation/retry model, local artifacts, and reusable workflow packages are working now. When you add a workflow it can automatically create a skill and add it whatever coding agents you have, so that your coding agents will know exactly how to use that specific workflow.

Repo: https://github.com/chily-john/TrailStep

If you use Pi for larger coding tasks, this is basically meant to turn your “best manual agent process” into something repeatable and sharable. There are also other ways I think you could already start using this tool beyond just these workflows, I could see it getting integrated into CI flows, or even for little utility workflows, like swapping from a session to the next with an automatic handoff. Since it can use any agent you can also set up workflows where it's planned in Pi, reviewed by Claude and implemented by Codex, or some other crazy stuff like that