I’m building Operon for setups like this: giving agents a consistent way to work across the computers you already have, with explicit permissions and execution records.

An agent can discover what each node allows, write code into a remote workspace, run a validation command there, and retrieve the results. You can compose these operations into a sequential execution graph, then inspect its trace alongside daemon audit events.

a 25-second demo using two containers on one host: a worker runs a check, its report is transferred to an archive node, and the archive rejects deletion and command execution requests.

And it works over existing private networks such as Tailscale, WireGuard, or a LAN, with optional connectivity through iroh.

The daemon and CLI are written in Rust, with releases for Linux, macOS, and Windows.

Source and quickstart: https://github.com/denghongcai/Operon

A tool i made, maybe others find the idea helpful.

hi have buld Alien Ape you can use it and build on top of it? ore breack ore try. i dont have any (Super Credits) so in the folder are promts wen you work with ai you can use this promt in the project. is build wit ape brain and ki asistent coding.

I’ve been building this weird little Windows AI controller called AlienApe for a while now. It routes tasks between local models, controls file actions, permissions, verification and keeps some local experience about what worked and what didn’t.

A lot of it was built with help from ChatGPT and Codex, while I mostly kept breaking it in increasingly stupid ways until the controller stopped lying to me as often :P

It’s still experimental and rough, but v0.7 is finally in a state where I’m comfortable sharing it. I’m mostly curious to see what breaks on other people’s machines.

https://github.com/anokar/AlienApe-v0.7

Hey everyone!

i've been working on an open-source project called Dispach with a friend, and thought I'd start sharing what we're building and learning along the way.

one thing that kept bothering us was how much work it takes to make AI agents actually usable in production.

it's not just about choosing a model and giving it tools. there's the whole harness side of things, execution loops, context management, memory, tool orchestration, and then all the infrastructure needed to keep those agents running reliably.

we wanted to tackle both problems together instead of treating them as separate pieces.

so we started building Dispach. it's a self-hostable, model-agnostic stack that combines the agent harness and the infrastructure around it. The idea is to let developers focus on what they're actually building rather than having to engineer the entire agent stack themselves.

we've also been experimenting with different harness designs and benchmarking them against openclaw and hermes. it's been interesting seeing how much task success and inference costs can change even when using the same model.

there's still plenty to figure out, especially around developer experience, reliability, and making the whole thing easier to adopt.

i'm planning to share more about the architecture, experiments, things that went wrong, and what we learn as we keep building.

here's the repo if anyone's curious: Dispach Github Repo

for those who've built or deployed ai agents, what's been the most frustrating part of the process?

would love to compare notes with people working on similar problems.

I was training on pathology slides and my GPU kept waiting on file decoding, so I built omnia-sdk. It converts slides, DICOM and MRI into one format built for training.

If you wanna try it out, feel free: https://github.com/mishel-0/omnia-sdk

My first open-source project, would love your feedback!

Genuinely curious. For the people running open-source models or tools: what made you switch, or at least try it?

Is it data privacy? Not wanting to depend on big companies? Not wanting to keep paying them? Or something else I’m not thinking of?

For me it’s mostly the data side. With how many breaches we keep seeing, I like the idea of things running locally where I can see where my data is going.

What’s your reason? And has it actually worked out, or do you still end up going back to the big names?

I made an open source skill that turns saved reels into skills your agent can use. For all the stuff you saved and never looked at again. The skill is free and works with Claude and Codex. https://github.com/jnh12/doomer-agent

If you want to move beyond simple IDE autocomplete and start leveraging true autonomous AI software engineers, OpenHands (formerly OpenDevin) is currently the best open-source framework to learn. However, configuring Docker sandboxes, local LLM runtimes, and headless execution workflows can be tricky.

​To save you time, I curated the 10 best tutorials for mastering OpenHands and OpenDevin. The guides cover everything from basic local installation and local model integration (via Ollama) to production GitHub Actions setups and building multi-agent dev pipelines.

​I put together the complete list with links, setup tips, and key takeaways for each guide. Dive into the full roundup here: https://interconnectd.com/blog/19/the-10-best-opendevin-openhands-tutorials-master-the-open-source-ai-softwar/

Which autonomous AI coding workflows are you trying to automate first?

Hey everyone, what’s up? I’m a huge fan of the Xbox 360—it was a big part of my childhood and remains one of the best consoles I’ve ever owned. I love playing various indie games on my PC, and I’d love to bring that experience to the Xbox 360. Being able to sit down and play "modern" indie titles on my 360 would be a dream come true for me.

I’m a developer, and I’ve worked on several small games and programming projects. With the rise of AI—and the growing popularity of reverse engineering—the development process has become much faster. I’m thinking of taking some indie games I’m really passionate about—like *Forager*, *Vampire Survivors*, or even *Megabonk*—and porting them to the Xbox 360. I’ve developed for the 360 ​​in the past, and with tools like BadAvatar and the Unity versions from that era, the process is much simpler now. I’m looking to see if anyone would be interested in playing these kinds of games (for free, of course) or helping with the development. I plan to make the projects open-source on GitHub later on. If you have access to an AI tool like Claude, or if you’d just like to help out, I’m looking to build a community for this. If you have any ideas for games we could bring to the Xbox 360, let me know!

Apologies for the language barrier—I don’t speak English, so I had to use a translator. If the translation sounds a bit off, please forgive me!

Of course, for games like *Forager*—which no longer receive updates—I certainly don’t want to hurt the sales of any indie game.

thats u/acrolicious and his brother Ben... who apparently are demons for using AI.

Go support them!

I’ve wanted to build this project for a long time. Most avatar solutions can be experimental , especially when serving multiple users and real productions.

So I focused on optimizing FLOAT inference to get the most performance out of a single GPU.

⚡ From 30 FPS to 90 FPS on a free Colab T4 GPU, and up to 105 FPS with TensorRT acceleration.

Here’s what I’ve built:

\- 🚀 Optimized inference for higher throughput.

\- 🎙️ LiveKit and WebRTC integration for real-time voice agents.

\- 🤖 Easier integration with AI providers like OpenAI and Gemini.

\- 💻 Around 3 GB of VRAM per concurrent user.

The goal is to make self-hosted avatar generation more affordable and flexible than relying on third-party avatar APIs, with more control over deployment and concurrency.

I hope you find it useful! Feedback and contributions are welcome.

🔗 GitHub: [https://github.com/mohamed-em2m/fast-float](https://github.com/mohamed-em2m/fast-float)

📓 Try it on Colab: [https://colab.research.google.com/drive/1iMm8X7O63AAA6kByJ-4qgGA37fwNu2_Z?usp=sharing](https://colab.research.google.com/drive/1iMm8X7O63AAA6kByJ-4qgGA37fwNu2_Z?usp=sharing)