u/Emergency-Shine-2656

I got really fed up with the back-and-forth. Describe a scene in Claude, get code back, paste it into Unity, hit an error, go back to Claude. Repeat 15 times.

So I just... connected them. Unity-MCP Pro lets Claude Desktop, Cursor, or Gemini CLI actually operate inside the Unity Editor. Not generate code for it — work inside it.

It can move objects around, read what's already in the scene, change any component property on the fly, and take a screenshot mid-task to check if things look right. That last one is probably my favourite part — the model can literally see what it just built.

A few things that make it actually usable rather than just a cool demo:

• No hardcoded properties. It uses reflection, so it can reach anything public on any component.
• Everything runs on the main thread. Unity won't explode when the AI does something unexpected.
• It loads the full scene graph first, so the model has actual context before it starts touching things.

Setup is honestly pretty quick — three steps and you're in.

Code's on GitHub if you want to poke around: github.com/NishantJLU/Unity-MCP

Genuinely curious what you'd use this for. Agentic level design? Having it catch its own bugs visually? Quick prototyping? I have ideas but I want to hear yours.

Hey everyone,

Like a lot of you, I use AI coding assistants (Cursor, Claude, Copilot) daily. But I kept running into the same frustrating problem: The AI forgets.

Every new chat session, I have to re-explain the project architecture, our specific coding conventions, why we chose library X over Y, and the bugs we've already fixed.

To solve this, I built AI Memory Layer. It’s an open-source, production-ready memory infrastructure specifically designed for software engineering agents.

How it works:

1. Ingestion: It hashes and ingests your Git history and codebase.

2. Structuring: It extracts the semantics (procedural rules, episodic decisions) and detects if a new architectural decision contradicts an old one.

3. Storage: It uses PostgreSQL with pgvector for semantic search and tsvector for keyword search.

4. Retrieval: It connects to your agents via an MCP (Model Context Protocol) server or REST API, using hybrid search (BM25 + Vector) ranked by a recency decay algorithm.

Features:

* Zero Lock-In: You can run it entirely locally using sentence-transformers and Ollama, or scale it with OpenAI/Anthropic.

* Smart Deduplication: It hashes content so you don't store redundant memories when re-ingesting the repo.

* MCP Ready: Exposes tools like recall_memory, store_memory, and flag_contradiction directly to your agent.

It’s built with FastAPI, PostgreSQL, and pgvector.

I’m a 1st-year CS student and this is my first time building infrastructure like this, so I’d love to get feedback from experienced devs. Have I approached the retrieval logic

correctly? Is there a better way to handle the conflict detection?

GitHub Repo: https://github.com/NishantJLU/ai-memory-layer

Feel free to tear the code apart, or throw a ⭐ if you think it's a cool concept!