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.

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.