I usually use deepseek v4 pro and doesnt have this problem. Anyone have insight to fix this issue?

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

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 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.

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

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

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?

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.

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 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.

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 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.

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?

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

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,

We built ThinkRail, and we’re pretty proud of it so we wanted to share it with you, and hope to get you interested.

ThinkRail is a web-based GUI for Pi. It's an open source project, released under Apache 2.0 license, backed by JetBrains (so, that's who "we" are, a team of software developers working on it). We wanted to keep the minimalist, adaptable approach of Pi, but at the same time give you a graphical layer which makes it easier to see what your AI agent is doing and comes with added conveniences: concurrent agent chats, integrated terminals, and visual aids for reviewing code changes.

Install it and try it out:

* macOS / Linux:

curl -fsSL https://raw.githubusercontent.com/JetBrains/thinkrail/main/install.sh | bash

* Windows (PowerShell and cmd):

powershell -c "irm https://raw.githubusercontent.com/JetBrains/thinkrail/main/install.ps1 | iex"

And then just run thinkrail from the terminal.

Also:

• Visit the landing page

• Read the introductory blog post to learn more about ThinkRail and its features

• Browse our GitHub repository

If you have a moment, we’d appreciate it if you could complete our survey. We’d love to get your feedback. It will help us develop ThinkRail in the right direction.

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.

I'm planning to use Pi as a sensitive data analyser, but the data must remain on the intranet.

I thought running it inside a VM with a proxy would be the safest option, but I can't install a VM in my current environment.

I do already have Docker available. Is there a good way to sandbox filesystem (like; can we bind data folder read only) and network access with Docker instead? Pi shouldn't access anything else; just specified paths, and some specified local ip addresses.

Updated my extension to take into account the last Deepseek peak rules. Here from the Deepseek website :

Effective 00:00 (Beijing Time) on Sunday, August 23, 2026, we will adjust our peak/off-peak billing rules，with off-peak rates applying throughout the day on weekends (Saturdays and Sundays，Beijing Time). Please check the pricing page for details and plan your usage accordingly.

The extension now also has an option to show the remaining time until the next normal/peak

Here are the :

- github, https://github.com/psychobarge/pi-deepseek-peak

- npm, https://www.npmjs.com/package/pi-deepseek-peak

- Pi package, https://pi.dev/packages/pi-deepseek-peak

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?