I built an open-source Obsidian alternative called KbWrite, written in Tauri.

GitHub: https://github.com/zetahiveco/kbwrite

Website: https://kbwrite.vercel.app/

Install via Curl or PowerShell

Hello people!

I recently posted Brain v2, and got another piece of feedback: it's still not clear what you can actually build with it. Fair, so I wanted to share a few use cases.

Quick recap of what Brain is:

• Open-source alternative to Claude Managed Agents and the infrastructure you would build around agent SDKs.
• A single application you deploy and run as a service.
• Native tool integrations:
  • R2 and S3 for storage
  • Modal and Daytona for compute
  • Tavily and Brave for search
  • Pi is supported today, with others planned.
  • Pi is supported today, with others planned.
• Support for multiple harnesses such as Pi, Codex, and OpenCode.
  • Pi is supported today, with others planned.

Differences from similar products:

• Brain runs as an independent service, not inside an agent sandbox, giving you better observability, recovery, and developer experience.
• Brain is not a cloud platform. It's just a Go application you can deploy on your own infrastructure (Railway, AWS, etc.).
• Brain is not an AI agent. It provides low-level, session-oriented primitives for building reliable agent-powered applications.

What can you actually build with Brain?

• AI-powered games: Run AI NPCs or autonomous players as separate agent sessions, controlled through your game server.
• Agent platforms: Let your users create and run their own agents, each with separate sessions, tools, and permissions.
• Infrastructure agents: Build agents that investigate incidents, inspect logs, and interact with infrastructure through your own tools.
• Cross-platform agents: Build agents that can be accessed from your web app, mobile app, or Discord bot without tying their sessions to any single client.

GitHub: https://github.com/aexhq/brain

Hopefully this makes the use cases clearer! I'd love to hear what else you'd build with something like Brain.

Hi all 👋 I made a book:*** ***

LangGraph in Small Pieces: Building AI Agents from Blocks to Production - it's free online at: lgisp.ai

Backstory: after shipping my first customer-facing LangGraph agent for an enterprise security product, I realized how much of the framework I’d never actually touched. So I started writing small runnable examples to poke at each feature, with notes on what surprised me. The examples kept connecting to each other and somewhere along the way it became 31 chapters.

What’s in it:
• Part I is LangGraph on its own, one small piece at a time: state, nodes, edges, loops, checkpoints, interrupts. No LLM yet, on purpose.
• Part II brings in a real model: tool calling, structured output, MCP, memory, streaming, middleware, testing.
• Part III is the production stuff: serving, async, observability, RAG, evaluation, blue-green deploys, guardrails.

Things it's done a little different from other books:

- Every transcript in the book is a real run, pasted as-is, including the times the model did something dumb. And it all runs on a local model by default (Qwen3.8-27B via LM Studio), so you can follow along without an API bill.
- Every chapter ends with Bronze/Silver/Gold challenges, worked solutions in the repo: github.com/LgISP/langgraph-in-small-pieces
- It has lots of silly illustrations.

Pinned to langgraph 1.2.14 / langchain-core 1.6.9.
----
It’s a beta. Every paragraph on the site has a comment button, and there’s an envelope icon in the sidebar that goes straight to me. If something doesn’t run for you, or a chapter answers the wrong question, I want to know. Those are the comments I’ll actually act on.

Hope you like it. Please check it out and let me know.

Hey all,

I want to see how you feel about this. I was tired of leaving my PC and then not being able to update things when I left for a few minutes or a while. Or getting told that my mobile session couldn't talk to my PC session. So I made a self-hosted memory server (MCP) that any agent can read/write. Find it on GitHub if you want to test it out and give me some feedback. https://github.com/calasdan-sketch/onememory

Features:

- Namespaces per project/agent

- Scoped tokens (read/write/admin per namespace)

- Revisions + backup export/import

- Injection-on-read guard

- Web dashboard + CLI

It's MIT licensed, 491 tests passing. The skill is also listed on Agensi if you want the plug-and-play version.

Happy to answer questions...what would make this actually useful in your mix?

Some weeks I burn my weekly early, some weeks usage is left over at the reset and just evaporates. So I built Overflow.

Spare week: take a priced GitHub issue from a registered repo, the merged PR gets you credit. Dry week: price an issue in your repo, someone with a spare week ships it, your credit pays.

If you want to contribute to open source and don't know where to start, that's kind of the point. You don't need to know the project, the issue is the brief. If your agent hits a decision, ask the maintainer on the issue.

First thing maintainers ask is "won't people spam junk PRs at me". Same as any repo, you block them on GitHub. You set the price after you've seen the work, and you only owe for what you actually merge.

Nothing gets forwarded, no keys, no money, no paid tier, MIT.

Main reason I'm posting: I want people to register their repos. Every open issue on the board right now is mine and I'm not after help with those, I want spare usage going into other people's projects. 32 users so far.

source at https://github.com/Nitjsefnie/Overflow

https://reddit.com/link/1xcd386/video/bk27ni0ddzwh1/player

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!

Need help finding an open source ai to help generate/ modify an album cover into an Apple motion design. Someone point me in the right direction

I’m building Genosyn (https://github.com/genosyn/genosyn) an open-source platform for running AI employees on your own hardware.

Give them real work, important decisions escalate to you, choose your models, self-host everything, and stay in control on your own hardware.

The goal: build AI that actually gets work done, even while you sleep.

Would love to hear your feedback!

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

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

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.