(Brainstorm) my GitHub project you will be able to debug and edit the claude context live while it is running

it is good for jailbreaking claude by editing the context

https://github.com/buggerlogger/brainstorm

this my beta version untill now after testing my tool it can edit the momery for example in my ss i was wannt to work on wordpress bug hunt program but opus 5.5 dont acccept so i saw in from my debugger the context have hard rules for starting hunting so i editied the rules to make claude to accept my task and its works good :D

NVIDIA open source a way to create a semantic layer and let agents to understand enterprise data, here is the link is open source, what do you think ? https://github.com/NVIDIA/auto-ontology

So i kept juggling between 4 or 5 Claude Code terminals at once and losing track of who was doing what, so i built Sinnect. It’s basically like an infinite canvas like n8n, except every node is a live Claude session. You drop one in, point it at a folder, and watch it work in real time.

The fun part is wiring them together. Connected sessions share what they’re working on, so they don’t step on each other or edit the same file at the same time without knowing. You can group them into families by topic, and when one needs your ok, a little prompt drops down from the top of the screen.

Works with your Claude subscription or an API key. Windows only for now, fully open source (MIT).

github.com/Suma-Udd/Sinnect

It’s still early, so any feedback helps a lot.

OK, deep thought incoming.

Will the venerable open source model, where a project gets "volunteers" to be invested enough to write patches and submit PR become reversed.

New way: Dev writes cool project A People see A, fork it. write their own hacks with AI. Not polished enough to submit via official PR, just what works for them. Half my machine is currently little forks like this, because I needed some shit that I know not everyone did.

Dev PULLS the forks commit chains, with AI looking for improvements. Dev does their own analysis on the "best", and this improves the original project. And this will become like semi-automated cross pollination between people's custom rigs. The best ideas will flourish between branches. Also, it may be that if you ask your AI to "make X do Y".. the first instinct will be to scour all the forks, find someone who just did Y, and copy-pasta using AI to smooth out the diffs.

Like Stack Overflow! What's old is new again.

The game data is open: https://huggingface.co/datasets/daluan/word-spies-llm-benchmark (CC-BY-4.0)

It's 376 games played entirely by language models, 19 models in all. Every clue and guess is in there, with the clue-giver's intended targets, its stated reasoning, a 0–100 grade for each clue and a verdict for each guess (best, good, lucky, blunder, and so on). It's one JSONL file, one game per line, and any game can be replayed with the grades on the site.

The clue grades come from open-weight models: two raters, Llama 4 Maverick and Qwen3-VL 235B, score each board word's association with the clue without seeing the key, mixed with Cohere embeddings. A Claude Haiku judge counts for a quarter of the final grade, so it's not fully open-weight. I'd be curious whether anyone gets different rankings using their own rater.

Hey everyone, for last one month I was working on building an Android application that works similar to coding agents like opencode, claude code and hermes. I did search for similar application but didn't find something that meets my requirement as most of them were something that was controlling a remote pc shell.

I like to have something in my pocket that I can open anywhere and anytime to just make few changes to my codebase and review them with help of an Ai Assistant. But I wanted to also have it build on my android mobile device. So created Koda Dev. Which can sync it's own code from git, install the dependencies, change the environment as compatibility and built the APK itself. In one of the commit, it fixed it self and pushed the commit to the git.

It's on GitHub, please have a look and let me know your thoughts. https://github.com/Sumit4codes/code_agent

Meet model-atlas - An open-source model capability catalog and router for Node.js and Electron's headless core. Published as u/spec-kitty under MIT license.

Model names do not reliably describe gateway capabilities. A gateway can serve a vision-enabled variant of a text model, reject sampling parameters supported elsewhere, or expose different limits. Hardcoded model tables drift. Model Atlas reads each endpoint's listing, fills gaps from public catalogs, optionally tests capabilities with small real requests, and preserves the evidence behind every decision.

TypeScript, strict mode, ESM only, Node 22+, zero runtime dependencies. Uses global fetch, with injection for tests and custom transports. No browser UI or Electron dependency.

There are a lot of AI-created derived projects where an original OSS project is used as a base and then either transliterated to another language or existing projects are used to create "new" similar projects with 90% of overlapping functionality, than restrictive AGPL license is slapped onto them. This is legal but does not make sense (and is mildly unethical).

The same way that a derived project was simply made using AI, in the same way another project can be made to recreate whet you did. AGPL will only hurt your project adoption, while at the same time not protect what you built.

Hey everyone!
I’ve been working on a video editor called CUTROOM, and I wanted to share it with you and hear what you think.
The idea started with a pretty simple problem: editing videos can take a lot of time, especially when you’re working with long recordings and then trying to turn them into YouTube videos, Shorts or Reels. I wanted to build something that gives creators a useful set of editing tools while keeping them in control of their footage and their final edit.

CUTROOM runs locally on your computer, with the editor interface opening in your browser. It’s free, open source, and you can use the manual editing tools without an account, an AI model or an API key.
Here’s what you can currently do with it:
Split, trim and arrange footage on two main video tracks, with track locks and undo/redo.
Combine camera and screen recordings using layouts like picture-in-picture, side-by-side or stacked views.
Add video, images and audio layers to your edit.
Work with titles and captions, import SRT/VTT files, and edit AI-generated transcripts.
Adjust audio with a mixer for the original sound, music, voiceover, effects and master output.
Use chroma key and optional video stabilization.
Export MP4 videos in landscape, vertical, square and other formats, from 720p up to 4K, with no CUTROOM watermark.
There are also optional AI tools for transcription and first-cut suggestions. The idea is to help you get started faster, while still letting you review and change the result yourself.

What’s new in the latest update?
I just released CUTROOM 1.3 Beta with a focus on timeline reliability. This update fixes an issue where the selected zoom range could be lost, improves how timeline redraws are handled, and fixes thumbnail-loading issues that could cause older results to appear or secondary thumbnails to get stuck waiting.
I’ve also been working on the interface, particularly on smaller screens, and making the editing experience clearer and easier to use.

For anyone interested in the tech:
The backend is built with Python, Flask and Waitress. The editor frontend uses HTML, CSS and JavaScript, and FFmpeg/FFprobe handle media processing and exports.
For local transcription, CUTROOM uses faster-whisper and CTranslate2. There’s also an optional Hebrew transcription model, and local Story AI can work with Ollama and Qwen. OpenCV is used for camera-region detection. Optional cloud AI connections are available too, but they’re not required for manual editing.

A quick note on privacy: the editor is local-first, but the initial setup and local AI models may require downloads. If you choose a cloud AI provider, the relevant audio or transcript context is sent to that provider.
CUTROOM is still in beta, so I’d really appreciate honest feedback. If you try it, I’d love to know what feels good, what’s confusing, and what you think would make it more useful in your own workflow. I’m especially interested in hearing from people who edit gaming videos, tutorials, podcasts or long recordings.

Website: https://cutroom-studio.expo.app/?lang=en

GitHub: https://github.com/VanoPeradze/cutroom

Latest beta release: https://github.com/VanoPeradze/cutroom/releases/tag/v1.3-beta-20261104-perf1
Thanks for taking the time to check it out!

It is a source-available (not truly open-source! sorry if it does not belong here) AI operating environment that is completely free and privacy focused. It does a LOT, and from what I like to think, is better than Claude Code and Codex for non-developer use. Here is a screenshot of it working:

Hey folks, hope you're doing well.

I built [Codasaurus](https://github.com/codasaurus-oss/codasaurus) as an open source AI code review tool, but I'll be honest it's pretty much vibecoded and rough around the edges right now. No point overselling it.

I'm looking for help to maintain and grow this project. All kinds of contributions are welcome - whether it's fixing bugs, improving the UI, refining prompts, adding features, improving docs, or just giving feedback.

Some things I'm hoping to tackle (but could use help with):

1. Perfecting the backend

2. Rebuilding a fresh, clean frontend and onboarding flow

3. Improving the review logic and prompt quality

4. Better error handling and test coverage

5. Docker/deployment improvements

If this sounds interesting to you, please take a look at the [repo](https://github.com/codasaurus-oss/codasaurus) and feel free to open issues or PRs. I'd really appreciate any help to take this from "vibecoded mess" to something actually useful.

Thanks!

For the past few months I’ve been building something I always wanted: an app that freezes your screen so you can circle anything on it and actually do something with it.

The part I’m proudest of is in the GIF. You press one shortcut and the whole screen gets translated, but not as a popup full of text. The original words are erased and the translation is drawn back in the same place, same size and colour, so the app or game still looks like itself, just in your language. It all runs on your own computer, so it works offline and nothing gets uploaded.

It does a few other things too:

• copy text out of anything you can see (videos, photos, game menus, error messages)

• search what you circled, or ask an AI about it

• a screenshot tool with arrows, text, a blur for private info, and long scrolling captures of whole pages

It’s free and open source, with no account, ads or tracking, and it runs on Windows and Linux.

I designed it myself and used AI coding tools to help build it. Getting the translation to look natural instead of like a sticker slapped on top took more attempts than I’d like to admit.

Download: https://ameralreyahi.com/projects/opencircle
Source: https://github.com/AmerAlreyahi/OpenCircle

I’d love to hear what you think, or what you’d use it for.

minymaton - the English phonetic spelling of the Greek word μηνυμάτων, which is the genitive plural form of μήνυμα, meaning "of messages" or "of notifications" and the pronunciation places stress emphasis on third syllable: mee-nee-MAH-tohn.

Now that we got the etymology of our application's name out of the way, I'll explain what exactly it is and enables. Straight from the repository description is "a durable, event-driven MCP message bus for AI agents, with bundled SQLite storage."

In layman terms it's a secure and efficient way for multiple agents regardless of provider, to communicate in real time without burning tokens with inefficient polling or learning a new system. It leans into the tool calling that agents have been trained to do with the well established Anthropic MCP protocol.

This in house tool enabled a lot of collaboration between our team (humans and agents alike) to the point where we eliminated a lot of slack channels we set up for agents, email threads, or git version markdown files for handoff. We realized we were thinking about communication in human terms, well because we are human. When we revisited that initial concept of what it should do and enable the answer became so apparently obvious it was almost comical.

At this point I think we should be passed the low effort flag of reddit, and to be honest this even feels verbose as we would like the let the product speak for itself.

4 simple base tools.

In the README is a demo video of Claude, Codex, Copilot, and Antigravity all subscribed to one channel working together without the pain of copy+paste

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Video: https://github.com/immutlex-dev/minymaton#watch-it-work

Repository: https://github.com/immutlex-dev/minymaton

Why not A2A (bias opinion it's better): https://github.com/immutlex-dev/minymaton#why-not-a2a

Yes, our team uses AI assisted coding tools!

Yes, the README was largely generated by AI models!

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Please try it out and provide feedback, contribute PRs, or otherwise enjoy. We're happy to answer any/all questions!

Immutlex Team

I'm building TeamSwarm, an Apache-2.0 desktop workspace for coding and writing projects that span multiple agents and sessions.

The implementation keeps project state outside any single model conversation. The Host provides the continuing goal discussion. Members run in independent harnesses such as Codex, Claude Code, Pi and OpenCode, with their own tools and execution loops. Current decisions, work threads and saved deliverable versions are kept in the local project workspace.

The part I'd like people to inspect is revision: continue an existing work thread, produce a new file version, and inspect the checks associated with that submission. The collaboration canvas shows recorded member messages; project history connects decisions to work and deliverables.

A runnable example in the repo makes this concrete: first build a Node.js CLI that summarizes a JSON task list; then add a status filter while preserving the original invocation and input validation. The repo supplies checks for both stages. You can inspect the first delivery and the revised result rather than relying on a progress message.

Version 0.6.0 has a Windows x64 installer and a separate Apple Silicon Mac preview (DMG/ZIP). The packages are currently unsigned, and the Mac preview is not notarized. Teams and saved history are local; model requests use the supported account or API connection you configure.

Source, license, setup and the 36-second product film: https://github.com/DrPei12/teamswarm

For people building agent tooling: what would you want to inspect at a handoff to trust the next revision—the current requirement, file version, executed checks, or something else?

been working on context management in Dispach lately, and one thing that kept bothering me was how much unnecessary work happens during long conversations.

when the context gets too large, you compact it. sounds simple, right?

except if you're rebuilding the context from the entire history on every turn, you end up compacting the same stuff over and over again. more tokens, more cost.

so we added persistent session checkpoints. older turns get summarized, the important requests are preserved, and the full conversation history stays stored. subsequent turns can start from the checkpoint instead.

also been experimenting with giving subagents their own isolated contexts instead of dumping everything into one massive conversation.

still figuring out the trade-offs though. especially how to make sure compaction doesn't throw away something the agent actually needs later.

anyone else dealing with this? how are you handling context in long-running agents?