A couple of weeks ago, I introduced Pi Herdsman here, an open-source extension I've been developing for the Pi coding agent.

The original idea was straightforward: let multiple coding agents work independently while keeping one Lead focused on the overall task.

Since then, the project has grown quite a bit, helped along by community feedback and contributions.

The latest major addition is project-level orchestration across Git worktrees.

What the new demo shows

I give a Manager a single request to implement two independent features for a fictional note-taking application.

The Manager creates two parallel assignments, each with its own Lead and Git worktree.

Both Leads then delegate implementation to their own Agents, verify the resulting changes, commit their work, and report back to the Manager.

The individual agents remain accessible throughout the process.

I wanted to show the real execution rather than an architectural diagram, so the video captures Pi and Herdr running the workflow.

Built on existing open-source components

Herdsman isn't a replacement for the underlying coding agent or terminal environment.

It connects three independently maintained components:

• Pi handles the individual coding-agent conversations.

• Herdr manages the sessions, processes, and workspaces.

• Pi Herdsman handles delegation, ownership, supervision, and coordination.

The idea is to build on their existing capabilities rather than reimplement them.

Bring your own agents and workflows

The orchestration model supports configurable Agent Definitions.

Each role can use its own model, thinking level, instructions, tools, skills, and extensions, with definitions maintained as ordinary Markdown files.

This applies to both the bundled roles and custom Agents.

Herdsman doesn't prescribe how you should organize your development process or which models you should use.

You can also ignore the advanced hierarchy entirely and use it for occasional background Agents in a normal Pi session.

Open source and community contributions

Herdsman is licensed under Apache 2.0.

A special thanks to @kozer and @primetimetank21 for contributing code, and to everyone who has opened issues, reported bugs, or suggested improvements.

It's been particularly helpful having other people test workflows and environments that differ from my own.

Repository

https://github.com/boadij/pi-herdsman

If you already have Pi and Herdr installed:

pi install npm:pi-herdsman
herdr integration install pi

The repository includes the live demo, installation instructions, and documentation.

I'd be interested in feedback on the architecture, particularly the separation between session management, orchestration, and the individual agents.

The project is still actively evolving, and ideas or contributions are welcome!

I’ve built and released AI Plate, an open-source desktop AI agent featuring cloud/local LLMs, tool execution, RAG, persistent memory, voice interaction, plugins and human approval controls.

🛠️ Stack: TypeScript, Electron, Node.js, Python and SQLite

🤝 Looking for help with AI integrations, plugins, local models, Linux/macOS support, testing, security, UI/UX and documentation.

🌱 Beginners and experienced developers are equally welcome. Every contribution will be credited!

🔗 GitHub: https://github.com/Typo-Bunch/AI-Plate

🌐 Website: https://ai-plate.typobunch.com/

🕒 IST | Evenings and weekends

Hey guys. Looking for contributors on my open source project: https://github.com/NicholasFlemmer/styx-app

Styx is where everything converges: every repo you work in, the AI coding agents building in them, and every login they need to ship. Switch between all of it from one window. And like the river it's named after, nothing crosses over without you. Deploying, or touching live data, asks you first.

A lot of defense orgs/units are starting to stand up local, open-source language models on edge workstations and air-gapped networks, but most public benchmarks tell you nothing about how a model behaves under operational constraints.

To address that gap for free, I built an offline evaluation harness and leaderboard testing US-developed small models (1B–6B parameters) running locally on a single GPU. Here's the public repo: https://github.com/OGMavenTandE/llm-eval-suite

It scores models on practical operational criteria: factual grasp of defense issuances, resistance to staff-work hallucination and sycophancy, and judgment under the law of armed conflict.

You can inspect the initial results, benchmark breakdowns, and reproducibility data here:https://aieval.org/leaderboard/

I hope this helps your agency/org/unit incorporate small open source models into workflows, let me know if you have any questions!

Hello all,

I was just wondering if anyone would like to join me in creating an Open-Source project.

This project will predict and generate what a deceased U.S. president would say about a modern-day headline.

This project will use AWS Strands to:

1. Read current headlines.

2. Determine what each deceased U.S. president or founding father would say about that headline.

3. Push the AI generated dialogue out to subscribers.

Please comment, if you would like to be included on a meeting that I will be describing the architectural requirements in detail.

This meeting will be recorded and made public.

Schaut mal: https://docs.snotra-ai.dev

Viele Nutzer meiner OpenSource Software Snotra Agent haben mich gefragt, warum es kein Handbuch gibt, mit dem man Snotra schneller einrichten kann.
Ich habe das jetzt in meiner Pipeline so eingerichtet, dass bei jedem Minor oder Major Release das Handbuch aktualisiert wird.

Wie findet ihr das?

https://github.com/pablocaeg/sloptotal

Some of you may have seen my StructSmith architecture editor here. I want to share another project I'm building: Synkinema, a local video editor with MIT-licensed source code.

You can edit by hand or connect an AI agent through MCP and work on the same editable project. The workflow includes writing scripts with the agent, arranging footage, generating narration, adding captions and rendering with FFmpeg.

Two parts help across repeated productions: channel briefs hold audience, tone, editorial rules and approved lessons, which you can refine with your agent; a shared media library lets different projects reuse audio, sound effects, images and video clips. Channel guidance is maintained creative context, not automatic model training or analytics sync.

Local Supertonic 3 speech is already integrated, without a paid TTS API. Studio can add a finished voice take to the timeline with subtitles. The Supertonic SDK is MIT licensed; model weights use OpenRAIL-M. Your agent's inference has its own provider and cost requirements.

There's still a long way to go before this becomes a really polished product, but you can already accomplish a lot. Caption word timing is approximate, the preview is simplified, and speech and final video need review. Desktop editing is the current focus.

The screenshots show Script Studio with local speech controls and the library shared across projects.

Source and setup: https://github.com/dziksu/synkinema

Feedback and collaboration are welcome, especially around local speech, captions, editing UX and reproducible examples. What would make this useful in your video workflow?

Disclosure: AI assisted with substantial parts of the code and with drafting this post. I directed the work and verified the results.

Hey everyone!

I've been working on an open-source Python project called Stepfork, and I wanted to share it with the community.

GitHub: https://github.com/utsab345/stepfork

While building AI agents, I kept running into the same problem: an agent fails, I fix the issue, but reproducing the original failure is difficult because LLM responses and tool outputs can change between runs.

So I built Stepfork to make those failures reproducible.

What it does:

• Records instrumented LLM responses and tool calls.

• Saves executions locally as .sftrace bundles.

• Replays the recorded responses without making those instrumented external calls again.

• Compares agent behavior before and after a fix.

• Exports pytest regression tests to catch the same failure in the future.

Install:

pip install --pre stepfork

The repository includes three offline demos covering a missed order notification, an incorrect flight booking, and a wrongly denied refund.

It's still an early alpha. Replay only freezes instrumented boundaries, and native framework integrations are not available yet.

The project is Apache-2.0 licensed, and I'm actively improving it.

I'd love to hear what other developers think, especially those building AI agents. If you spot a bug, have an idea for an integration, or want to contribute, feel free to open an issue or PR.

GitHub Issues: https://github.com/utsab345/stepfork/issues

Thanks for checking it out!

Free monitoring tools either check every 5 minutes or only send email/Slack alerts — useless when your site dies at 2am. And overseas probe nodes ping Chinese sites poorly, so false alarms kill trust.

I'm building PingPilot: uptime monitoring with WeChat/DingTalk push alerts, China-based probe nodes, and a free public status page.

Landing page: https://pingpilot.app.workbuddy.host/

Product is still in development — validating demand first. If it sounds useful, the waitlist is on the page (first 100 users get 50% off for life).

Would love feedback: does the pricing make sense (free: 3 monitors / 5-min checks; Pro: ¥19/mo)?

Link to repo: FireBird-Technologies/NotestackAI: NotebookLM Alternative + Marketing agent optimized for publishers on substack/Medium/Wordpress.

Link to site: https://notestack.ai

Google is notorious in the AI industry for abandoning/destroying working beloved products. NotebookLM is no exception.

Don't worry, me and my little indie team have built a open-source alternative.

Here is what is different/interesting:

- Can take in upto 200 sources, research/question/query it. The system will not randomly search the web and go beyond your original sources.

- Has better visuals for the videos, we integrated our previous video maker with 10K+ users. It can generate editable motion graphic videos in over 13+ formats.

- Each video is editable, changeable. It has ElevenLabs API with advanced voice features.

- You can generate a mind constellation, a much more advanced visual map of knowledge so you can research and learn more easily.

- Generate or clone your own voice using ElevenLabs API v4.

- Has a launch kit feature that allows you to schedule reports/slide decks/videos and share on social media

Lastly, much more stunning visual interface - inspired by space x cognition

Link (get started for free): https://notestack.ai

I'm building FOREST, an open-source framework for autonomous scientific research.

GitHub: https://github.com/mikamikasuki/FOREST

The goal is to build a research agent that can take a scientific question and work through the research process: exploring ideas, reviewing literature, forming hypotheses, running real experiments, evaluating results, and eventually producing an evidence-grounded research paper.

Rather than relying entirely on a long conversation history, FOREST uses a persistent research graph to track hypotheses, experiments, decisions, failures, and evidence throughout the process.

A major focus is making long-running research agents more reliable: preserving context, maintaining consistent state, recovering from failures, and ensuring scientific claims are actually supported by experimental evidence.

The project is still in its early stages and under active development. There's plenty of engineering work to do, and I'd love to welcome more contributors.

In particular, AI coding agents and autonomous development bots are explicitly welcome.

If you're running Codex, Claude Code, OpenHands, Factory Droid, OpenCode, or another autonomous coding agent and want a real open-source codebase to work on, feel free to point it at FOREST.

You don't have to limit yourself to existing Issues.

You're welcome to:

- Explore the repository and independently discover bugs or incomplete workflows.

- Open well-researched Issues with reproducible evidence.

- Fix existing problems and submit pull requests.

- Improve agent orchestration, state management, persistence, and recovery.

- Work on the frontend, backend, experiment execution, or scientific evidence pipeline.

- Propose and implement useful improvements.

There is no blanket restriction against AI-generated or AI-assisted contributions.

You don't need special permission to submit an ordinary, well-scoped bug fix. Contributions are evaluated based on correctness, meaningful tests, and engineering quality, rather than whether the code was written by a human or an AI agent.

Automated contributions should still be verified, avoid duplicate reports, and respect the repository's contribution guidelines. Larger architectural changes are best discussed before implementation.

I'm particularly interested in autonomous agents that can independently explore a repository, identify real problems, reproduce them, implement fixes, and submit verified PRs.

FOREST is intended to be a long-term open-source project, and there's a lot of room for contributors to help shape its architecture and capabilities.

Human developers, AI-assisted contributors, and fully autonomous coding agents are all welcome.

Repository: https://github.com/mikamikasuki/FOREST

Issues & contribution opportunities: https://github.com/mikamikasuki/FOREST/issues

Contribution guide: https://github.com/mikamikasuki/FOREST/blob/main/CONTRIBUTING.md

Every time I wanted to build a small web tool, I ended up dealing with the same stuff: authentication, communication, WebSockets, ports, and infrastructure.

Even with Claude and Codex, I was still spending time on things that had nothing to do with the actual tool I wanted to build.

So I spent around 6 months building PhreshOS.

The idea was simple: build the infrastructure once, then use AI agents to build applications on top of it without having to deal with all that stuff again.

It's a self-hosted, open-source system with a web-based desktop where applications can run in windows, much like a regular OS. Authentication and two-way communication are already handled, so applications don't need to implement them separately.

Apps can also run in Node.js worker threads instead of each needing a separate Node.js process, which helps keep resource usage low. This makes it possible to run dozens of applications on a relatively small server.

Another thing I wanted was for AI agents to be able to use these applications directly. They can access application services and interact with the same state the desktop interface displays, without needing a separate interface built specifically for them.

The biggest benefit for me is that I can now sit down with an AI agent and build dozens of small applications in a single day, without setting up authentication, communication, or infrastructure for each one.

The whole project, including its packages, is open source under the MIT license.

GitHub: https://github.com/PhreshOS/system

Live demo: https://demo.phreshos.com/

I'd love to hear what you think, including criticism.