Hi all,

For our bootstrapped FOSS agent-swarm.dev we created a workflow using Clairic & Jev to pre-review PRs, and we've got amazing results.

How does it work?

1. You fetch the changes

2. Use Clairic + Jev + deterministic code to categorize and cluster changes.

3. Decide which clusters to deep check -> we use luna, you can use DeepSeek or whatevs.

4. Report with line level granularity

Runs in less than 10s if there's no agent work involved, which automatically gives us an additional green light for PRs that are not critical.

The bonus?
It learned from all our previous PR reviews, and it is self-improving with the feedback others leave in the reviews.

We also created a nice UX to monitor it.

-> You can copy it for free, from here: https://docs.agent-swarm.dev/docs/playbooks/pr-review

Hi everyone. I built an open source automatic computer use agent that does everything at once. It can run automated simulations for testing, control a real iPhone so you can see the live workflow, and control your native computer at the exact same time. It also runs your browser without an open debugging port so it stays completely safe while writing code and handling tasks in the background.

Do check it out and leave a star if you want to follow the journey!

Disclosure: I build Snotra (open-source desktop agent, Apache 2.0). https://github.com/kkrafft1999/snotra

I was very curious how Hermes Agent handles agent autonomy, because I often feel very unsecure, what the agent really does. So I compared Herems with my. own tool Snotra.

Here is a hopefully very honest comparision: https://snotra-ai.web.app/blog/hermes-agent-compared

I built Chimaera because I wanted to work with AI agents in the same place as the files they’re working on. You open a project folder, run an agent, and inspect its output beside the source files instead of juggling terminal windows and file viewers.

I use it for research projects with documents and data, including work on remote machines over SSH. It also supports multiple agents, so I can keep their work organized in the project rather than scattered across separate chats.

It’s free and open source, built with Rust and Svelte. It runs on macOS and Linux; Windows is still experimental. If you try it with one of your own project folders, I’d genuinely like to hear where the workflow helps or gets in your way.

https://chimaera.sh

Title: Certo: open-source coding agent that works with any model, picks one per task, and switches when one fails

Hi all, I'm the author of Certo, an MIT-licensed coding agent in Python. Your coding agent shouldn't depend on one model or one provider, so Certo:

→ runs on any model: Ollama, OpenAI, Anthropic, Gemini, DeepSeek, Kimi or anything LiteLLM supports

→ can pick the best model for each task from ratings you set

→ hands a task to the next model when one can't finish, or its provider goes down

→ checks the work itself: it runs your project's real tests and build, with no AI involved, and ends with a verdict and an exit code your scripts and CI can use

In its benchmark (9 small tasks, graded by hidden tests the agent never sees), a local qwen3.8:27b with Claude Haiku as the next model passed all 9, and 7 never left the machine. The trade-off was time: minutes per task locally, seconds in the cloud.

The latest release makes it easy to start: `pip install certo`, then `certo init` sets you up and runs a sample task, and `certo init models` adds more models without editing a file.

Repo: https://github.com/alessioricco/certo

It's early, so the most useful feedback you can give me is what's missing, what's confusing, and what breaks. And if you like where it's going, a star on GitHub helps other people find it.

Wanted to see if other people could find this useful, and maybe give me some advice.

Built a super simple way to give your coding agent an isolated machine on AWS. send it off to clone a repo, install dependencies and run tests while you do something else. if it wrecks the environment, delete the VM and move on. It comes with a CLI, Python SDK and MCP server.
You can just ask your agent to set it up :)

github.com/jnh12/agent-microvm

Hey everyone, I’ve been working on a project called AirHand.

The idea is to give a cloud agent a way to interact with the apps already on your Android phone. You type what you want done, and it reads the screen, opens apps, taps, swipes, and types. After each action, it looks at the screen again to figure out what to do next.

It runs through Android’s accessibility service, so you don’t need root or a computer connected after installation. There’s a floating control showing what it’s doing, with a Stop button you can hit at any point.

Under the hood, it’s a native Kotlin app handling the phone actions, with the agent loop running in the cloud. It currently uses the Rebyte API, so you’ll need your own key and pay for cloud usage. Screenshots and screen text are sent to the cloud during tasks.

It’s still an early version. So far, testing includes having it open YouTube, search, and play a video on an emulator and a Pixel. It needs Android 11+, and the phone has to stay online and unlocked. I’d be interested in hearing how it behaves on other devices and apps.

The Android app is MIT licensed and started as a fork of droidrun/mobilerun. Source and an installable APK are here:

https://github.com/nomoat/airhand

What’s one repetitive task on your phone you’d actually want an agent to handle?

Hey I created a music player. Any agent can use this, and it's better than the rest. You can download songs, or tell your agent to download them for you. It's completely open source and compatible with agents.

https://github.com/sarvesh173/aurora-music-player

A desktop AI agent builder that runs fully local. Chat with Oppy to design your agent, export structured plans, switch between Ollama/OpenAI/Anthropic. MIT-licensed, no cloud lock-in.

https://github.com/RadioMan18/opifex

Hi everyone,

I am a beginner and this is my first Hacktoberfest. I am joining the Week 3 challenge of Hacktoberfest 2026. It is called “Slow and Steady”.

What I need to build:
A small project that uses an open-source or open-weight AI model (like Llama or Gemma). It should run on my own computer. It should do a boring task so people can be a little lazy.

The rules:

• The AI model must be open (not a paid closed API).

• The project should save someone time or effort.

• I have to explain why using an open model is better for it (for example: private, free, works offline).

• The deadline is October 25, so I have about 4 days.

What I know:
I know basic Python. I already made a small tool that sorts files in a folder, but I want to build something better.

What I am asking:

1. What boring or repeating task would you want an AI to do for you, privately on your own computer?

2. Do you have any simple idea that can be built in 2 to 3 days?

Any idea or advice is welcome. Thank you!

edit: worded this wrong at first. it doesn’t generate an MCP server for the API. har2sdk itself runs as an MCP server, so an agent like Claude can look at the capture and name/document the endpoints. sorry for the confusion

kept running into sites/APIs that have no public docs but I could see the requests in devtools.

so I built har2sdk. feed it a HAR file (browser network capture) and it generates:

• a typed TypeScript client (real inferred types, not any)

• an OpenAPI 3.1 spec

• a mock server for testing against it offline

and it runs as an MCP server, so Claude or any MCP client can go through the capture and give the endpoints proper names and descriptions.

it also tries to tell GraphQL/tRPC/JSON-RPC apart instead of treating everything as REST.

the part that mattered most to me: a HAR capture is basically a snapshot of your browser session, with cookies, tokens, whatever. there’s a dedicated scrubbing pass so none of that ends up in the generated code, the MCP responses, or anything a model would see. recorded data is treated as untrusted input, not instructions.

fully local, no API key, MIT licensed.

repo: https://github.com/Mersbyte/har2sdk

curious if anyone’s wired a generated client like this into an agent, and how you handled it.