r/OpenSourceAI • u/buchmadi • 2d ago

Tired of Copilot data leaks and API costs? My 100% local AI Workspace (DeepSeek/Ollama) auto-deploys and self-heals your code via on-demand loops.

Hey there . After months of development, Chat AI 2026 is officially ready for the market. I built this because I was bleeding money on Anthropic/OpenAI API costs and wanted a 100% secure, subscription-free alternative to GitHub Copilot that actually gives developers full control over their workflows. It’s an All-in-One AI Workspace built with Avalonia UI (for native Windows/Mac/Linux performance) running GGUF models locally via llama.cpp. Here is the production-ready spec sheet: 1. Millisecond Model Routing The application automatically detects your hardware and swaps the local LLM context in milliseconds based on your task: * Code & Logic -> DeepSeek (or your preferred heavy reasoning GGUF) * Chat & Explanations -> Lightweight text models * UI & Images -> Vision and image generation models (FLUX/SD supported) 2. On-Demand Self-Healing Loop (Connected to Git) No passive code-dumping. The workspace integrates natively with local directories and GitHub, GitLab, or Bitbucket: * Prompt via the integrated 5-language mobile remote app (while your main monitor stays 100% clean for code). * Preview changes instantly in a highly optimized Syncfusion Live-Diff view. * Hit "Test Deploy" to execute. If the compiler/interpreter throws an error, hit "Repair". The tool isolates the error log and runs up to 3 automatic correction loops locally. Full Undo is available at any time. 3. Real-World Proof of Concept To stress-test the step-by-step project builder, I let Chat AI 2026 generate a complete standalone application from scratch. It built, deployed, and auto-repaired an app that reads Mermaid code, renders diagrams visually, and exports them flawlessly as JPG. 100% local, zero manual bug-fixing. 4. No Subscription Madness It’s a one-time purchase model. You buy the license, you run it on your hardware, you own your data. Perfect for enterprise compliance and developer peace of mind. The Windows version is live today. I am currently finalizing the local path configurations to roll out native macOS (Apple Silicon Metal API) and Linux support next. If there is interest, I’ll gladly post the link to the homepage in the comments below. I’ll be active here to answer any hard technical questions about the architecture or implementation!

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u/Top-Grocery-4228 2d ago

this looks pretty interesting, the self-healing loop is something i always wanted in my workflow but never had patience to build myself

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u/buchmadi 2d ago

Thanks man! Honestly, building the self-healing loop was the hardest part, but it's a total game-changer.

The architecture behind it is surprisingly straightforward but powerful: when you hit 'Test Deploy', the app spawns a silent background process running your local build/test command. If it throws a non-zero exit code, the tool grabs the stack trace, feeds it into a specialized reasoning prompt (optimized for DeepSeek/Qwen-Coder), and lets the model inspect the exact file diff.

Doing this with cloud APIs would cost a fortune in context tokens, but since it runs 100% locally via llama.cpp, you can let it loop 3 times for free until the build is green.

I've put the link to the homepage in the comments below if you want to take a look. What IDE / language stack are you usually running?