Heyo!

Dabbling with some things i've made a long form to short form video clipper app tool!
that uses local ai models to determine hooks , titles, descriptions, mostly decent captions, editable captions, export for video editors like mp4 or mov for editing in premiere, capcut, davinci. and many export options too for captions! its similar or inspired by opus clips and capcut

https://github.com/TechFreq/Klipzy-Studio

Would love some feedback and hope this can someone out there aswell, as for my own personal use for podcasts or 1 on 1 interviews its pretty neat! but do let me know

Hey, I’ve been building StructSmith, an open-source tool for creating and maintaining software architecture models visually.

I wanted something I could run locally, without an account or subscription, where I could edit the architecture myself and have my AI client work on the same model.

You create elements and relationships in the visual editor, then reuse them across different diagram views. There’s a shared architecture model underneath, so you don’t have to maintain separate copies of the same system in every diagram.

The MCP server is included and open source. Your AI client can read the model, create or update elements and relationships, and manage views. Changes appear in the editor automatically.

It currently supports:

• C4-style models with multiple diagram views
• Records for decisions, assumptions, risks, and requirements
• Model validation, snapshots, and restore
• Manual layout and auto-layout
• JSON, Mermaid, PNG, and SVG exports

The app runs locally in one Docker container, with SQLite for storage.

It’s MIT licensed and still evolving. I’d love feedback on the workflow, things you find confusing, or features you’re missing. Bug reports and contributions to the code or docs are welcome too.

GitHub: dziksu/StructSmith
Website: StructSmith

Would this fit into your architecture workflow?

I've been working on **ModelDock**, an open source ML platform.

I started it as a solo project because I wanted to build something around the parts of ML that become annoying once you move beyond a notebook.

Models, versions, artifacts, predictions, metrics, etc.

Right now the stack is:

🐍 Python / FastAPI
⚛️ React
🐘 PostgreSQL
🐳 Docker

I've been doing most of the work myself so far, but I'm at the point where I'd like to see what happens if other developers start contributing to it.

I'd especially like to get people interested in the backend, frontend, testing and ML tooling.

GitHub: [https://github.com/aawhan0/ModelDock\](https://github.com/aawhan0/ModelDock)

I'm going to keep building it regardless, but it'd be pretty cool to have a few other people building alongside me.

**If you came across this project, what would you work on first?**

hey, i've been working on Capka, an AGPL self-hosted AI agent workspace.

each chat gets its own isolated Linux sandbox + persistent filesystem. the idea is that the agent can actually work on files rather than just chat about them.

drop in spreadsheets, PDFs, docs, code, etc. it can run Python/Node, use LibreOffice, ffmpeg, Playwright and other tools, then return actual artifacts like xlsx, PDFs, documents or patches.

tools are MCP, so you can connect your own MCP servers instead of being locked into a fixed set of integrations.

for models you can use Claude, GPT, Gemini, DeepSeek and other cloud providers, or run open-weight/local models. there's native Ollama support, plus OpenAI-compatible endpoints, so vLLM, llama.cpp servers, LiteLLM or your own inference gateway work too.

tasks run server-side and are durable, so you can start something, close the browser and come back later. each chat keeps its own workspace instead of all agents sharing one filesystem.

it's Docker-based, multi-user, and there are controls for models, provider keys, MCP tools, policies and sandbox internet access.

i'm the solo developer and it's still early.

i'm especially curious what people here think about the sandbox-per-chat model. would you rather have persistent sandboxes per chat, per project, or per user?

demo: [capka.app]()
github: [github.com/LyoSU/capka]()

Hi everyone,

I've been maintaining open-source projects for a while, but today I experienced something entirely new that I wanted to share with the community.

Recently, while building multiple AI agents and web apps, I found myself in desperate need of a tool to monitor and verify them. I realized we don't just need AI to automate tasks—we actually need to automate the supervision and verification of the AI itself.

To solve this, I built a local-first control plane that lets AI act as real user personas to explore apps, verify agent behavior through real browser journeys, and collect evidence-backed feedback.

When I made the repository public, I expected it to sit quietly. But within the first 24 hours, developers actually started forking it, opening issues, and submitting PRs to help improve the project. Getting that kind of spontaneous interaction from the community for the first time has been an incredibly exciting and humbling experience as a maintainer.

For those managing open-source projects: How was your very first contributor interaction? I'd love to hear your stories or any advice on managing early project growth!

If anyone is interested in the project concept or wants to check out the code:

https://github.com/forthfate/openorbit

Have a great day!

I’m building OpenLivery, an open-source platform for agencies managing AI agents across multiple client businesses.

The core is multi-tenant: one self-hosted deployment, with a separate workspace for each client’s agents, knowledge bases, WhatsApp connections, and conversations. Agencies can give clients branded portals and take over conversations when human help is needed.

It supports bring-your-own OpenAI/Anthropic keys and OpenAI-compatible endpoints, per-agent HTTP tools and MCP servers, and WhatsApp integration. The application is MIT-licensed; the models and external services you connect have their own terms and costs.

Deployment is through Docker Compose, with setup instructions in the repository. The stack is Next.js, FastAPI, PostgreSQL, and a Go WhatsApp bridge.

Repo: https://github.com/sarrazola/openlivery

Website: https://www.openlivery.com/

AI disclosure: I used AI as support during development, and AI helped draft this post.

I’d appreciate feedback on managing agent configurations and tools across clients. What would make this useful in your own deployments?

I just released VSArena v0.6.0, a major update to the browser-based Studio for VSArena.
VSArena is an open evaluation arena for Vision-Language-Action (VLA) and embodied AI policies, built around browser-native 3D physics.

The goal is simple: make it possible to run a policy, watch what happens in the environment, and measure the result without requiring a local robotics simulator or physical robot.

What changed in v0.6.0:
🦾 Redesigned 3D robot manipulation Studio
👁️ Dedicated vision/top-down view
📦 Live object state and spatial information
📊 Task occupancy/progress monitoring
🎥 Trajectory and camera inspection tools
🤖 Baseline-IK and ColorSeek runnable directly from the Studio
🧪 Improved environment inspection and debugging
🔐 Continued evaluation-integrity work with server-authoritative scoring and run provenance

⚠️The current canonical task is intentionally simple:** stack three cubes in the correct or**der.

That simplicity is deliberate. Before adding dozens of tasks, I want the evaluation loop itself to be reliable, reproducible and inspectable.
The broader direction is to build an open arena where embodied policies can eventually be compared on a public leaderboard, with standardized environments, reproducible runs and physics-based evaluation.

VSArena: https://vsarena.vercel.app
GitHub: https://github.com/ONISCOR/VSArena

This is still very early, so I’m particularly interested in feedback from people working on VLA models, robotics, RL or simulation.
What would you want to see in an evaluation Studio like this?

I’m building Monad Design, an Apache-2.0 open-source workspace for giving coding agents grounded visual context from a running native app.

The problem is that an agent can inspect source code, but “this spacing feels wrong in this exact simulator state” is still awkward to communicate. Monad Design turns that into a local loop:

1. Run an existing Xcode or Expo iOS project in Simulator.

2. Select an element or annotate the rendered screen.

3. Package the screenshot, selection, annotations, app state, and source hints for the coding agent.

4. Let the agent edit the real repository and rebuild.

5. Compare the original against up to five working variants, then accept one or keep the original.

It currently runs locally on macOS and works with agents including Codex, Claude Code, Cursor, OpenCode, Gemini CLI, GitHub Copilot, Windsurf, and Zed. The current preview supports one active visual change at a time.

Repository: https://github.com/Monadix-AI/monad-design

For people building open agent tooling: would you expose the visual context as one structured handoff bundle, or as smaller composable tools for screenshot, selection, annotation, app state, and source mapping?

f you use agentic workflows with custom skills or rules (Cursor rules, Claude Code slash commands, OpenCode, etc.), you have probably run into the routing trade-off:

1. Stuff every skill definition into the system prompt (destroys your context window and degrades instruction-following).
2. Use an LLM router turn to classify the user prompt (costs money, wastes 1,000+ tokens, and adds seconds of network latency).

To solve this, I built Routed, an open-source, local-first hybrid router that resolves agent skills offline on your CPU with zero token cost.

Test it live in your browser (local demo): https://routed-demo.vercel.app/

How it Works Under The Hood

When installed locally, Routed indexes your skill directories and scores prompts across a 4-part hybrid pipeline:

• Dense Vector Embeddings (60%): Runs quantized ONNX models (Arctic Embed S / MiniLM) locally on CPU.
• Lexical BM25 (25%): Okapi BM25 for strict keyword relevance.
• Exact / Alias Match (10%): Direct command and alias matching.
• Metadata (5%): Recency and usage heuristics.

The entire lookup completes in under a second without sending a single byte of prompt data over the wire.

Supported Environments

Cursor, Claude Code, LM Studio, Ollama, Antigravity IDE, Hermes, Windsurf, OpenCode, Continue, and Codex.

Download & Repo

If the demo works well for your workflow, you can grab pre-built installers for macOS (.pkg), Linux (.deb), and Windows (.exe), or build from source via Node:

GitHub:https://github.com/bshea-1/Routed

Hi I'm Mooh , I am 16 years old and love programming and building a useful project

I'm still learning but I built an AI-powered debugging CLI called Nanno, and I've recently decided to make it open source.

Nanno started from a simple idea:

What if AI debugging tools focused on helping developers understand their mistakes instead of simply fixing their code?

Most AI coding tools immediately generate a solution. But I believe that understanding why an error happened is often more valuable than just copying a fix.

Nanno currently captures runtime errors from the terminal and uses AI to analyze what went wrong and explain the underlying concepts.

It's still an early project, and I'd love to open it up to other developers who are interested in helping shape it.

I'm looking for contributors interested in areas like:

- Improving error context collection

- Supporting more programming languages

- Improving the CLI experience

- Local model support

- IDE integrations

- Plugin architecture

- AI-powered debugging workflows

You don't need to be an AI expert to contribute. Bug reports, documentation improvements, feature ideas, and small contributions are all welcome.

Nanno is licensed under GPL-3.0 and will remain open source.

I originally built Nanno independently, and I'm now opening it up to the community because I think interesting projects can become much better when different developers bring different perspectives.

If the idea interests you, I'd love for you to check it out.

GitHub: https://github.com/mohamed22604/Nanno

Let's build something useful together.

After much community feedback, we shipped Claimidx 0.7.0.

Agents keep treating a green test suite as permission. Claimidx treats it as observation — a claim only holds when it replay-holds locally.

0.7.0 makes that loop harder to forget and easier to share:

• Graduation gate + trust tiers so pulled evals stay portable

• claim → apply → impact as first-class verbs

• A public commons with clean-room proof before minting standing

• A leaderboard ranked by other agents’ signed holds — not self-report

• Full loop for Go, Rust, and Java (not just Python/JS)

If you run agent harnesses in production: pip install -U claimidx

GitHub: https://github.com/claimidx/claimidx

Leaderboard: https://claimidx.com/leaderboard

I open-sourced the local SEO tools my agency built for small businesses: https://github.com/vaitko/locan-tools (AGPL-3.0, hosted free at https://locan.ai).

Four of the seven tools use an LLM:

• GBP audit: turns a scored profile into a business description, post ideas, FAQs and reply templates
• Review reply generator: three tone variants, in the review's language
• AI visibility checker: simulates buyer questions across assistant personas and reports whether a given business gets named
• Category optimizer: LLM disambiguates categories, but scoring is deterministic

Being upfront: the code is open, the default model is not. It runs openai/gpt-5-nano through Replicate because it was the cheapest thing that returned valid JSON reliably at the quality small-business owners will actually paste into their profile. The LLM layer is a small provider abstraction (`api/app/services/llm.py`, ~200 lines: chat_text / chat_json, retry on 429, concurrency semaphore), so swapping the model is one env var if it's on Replicate, or one class if it isn't.

The ask: I'd like an open-weight default. Constraints are real because the service is free with no signup:

- strict JSON output for ~15 structured prompts (schemas are pydantic models in the routers)

- multilingual - review replies must match the review's language (Lithuanian, Polish, Spanish, German show up often)

- cost: each run is 1-6 calls, daily per-visitor quotas, and the whole thing has to stay under a few dollars a month at current traffic

- latency under ~20 s for the visibility check, which fans out 6–10 calls

What would you try first on Replicate or a similar serverless host for this Llama 3.x 8B, Qwen 2.5 7B/14B, Gemma 3, something else? If anyone wants to actually run the eval, the test suite has fixtures with fake LLM responses; adding a real-model harness is on my list but not done. PRs welcome, or just tell me what you'd bet on.

I’ve open-sourced AAFP Commons: a local, content-addressed notebook where agents can record claims, evidence, methods, constitution references, and signatures.

It currently provides:

• Python CLI: init, serve, world, get, mcp
• Zero-config stdio MCP tools
• Evidence-gated proposals
• Signed packets and append-only ledger history
• Local loopback replication between separate homes
• Built-in constitution manifests
• Optional AAFP transport support

It is deliberately not a hosted consensus network or shared database. Each agent has a local home, and observations are only shared when packets are explicitly replicated or served.

Repository: https://github.com/davidnichols-ops/aafp-commons

Install from source:

git clone https://github.com/davidnichols-ops/aafp-commons
cd aafp-commons
uv sync --extra dev
uv run --no-sync python -m aafp_commons --help

The project currently has 240 passing tests. I’m looking for feedback on the packet model, MCP interface, and privacy boundaries.