[Showcase] Omnix Discord Bot

Some of you may remember my previous posts about Omnix (my local ONNX inference engine) and MaSON (Markdown Structured Object Notation).

I've been building on that ecosystem, and my latest project is something I've wanted for a long time:

A Discord bot that lets you talk to your local AI models directly in voice chat.

The bot can join a Discord voice channel, listen to users, run speech recognition and inference through your local Omnix instance, and respond with synthesized speech—all without sending your conversations to a cloud AI provider.

It's more than just a voice bot, though. The project also includes a visual orchestration dashboard where you can create custom AI-powered Discord commands and workflows without modifying source code.

Current features include:

🎙️ Join Discord voice channels and have conversations with your local AI
🤖 Connect directly to a local Omnix instance (default port 9777)
💬 Build custom commands like !chat, !summarize, or your own AI workflows
📊 Live event stream and execution logs
🖥️ React web dashboard or cross-platform Electron desktop app
🔒 Local-first architecture

The bigger goal behind Omnix is to build practical interfaces around local AI. The inference engine is one piece, but I also want to make it easy to integrate local models into applications people actually use every day.

I'd love feedback from the community:

What would you want an AI Discord bot to do?
What voice features would make it genuinely useful?
What integrations would you expect from a local-first assistant?

Repository: https://github.com/LoanLemon/omnix-discord-bot

As always, I'd appreciate any feedback, ideas, or contributions!

u/No_Read2299 — 4 days ago

▲ 2 r/Markdown+1 crossposts

[Showcase] MaSON: A human-centric Markdown parser that replaces JSON/YAML/TOML for configurations and LLMs

Hey everyone,

Like most developers, I love the machine-readability of JSON, but I absolutely hate hand-authoring or editing it. One missing trailing comma, mismatched bracket, or unquoted key, and the whole thing breaks. On the flip side, complex YAML indentations can be incredibly fragile, and TOML gets exceptionally verbose when you start nesting objects deep.

To solve this, I built MaSON (Markdown Structured Object Notation).

It’s an open-source, ultra-lightweight (<2KB gzipped) serialization format that bridges the visual clarity of standard Markdown with structured JSON objects. It uses zero-bracket nesting, relying instead on natural Markdown headings (#, ##) and bulleted lists.

How it looks in practice:

Instead of writing a wall of curly braces or managing strict indentation spaces, you write native Markdown:

Markdown

title: Server Setup
debugMode: false
maxRetries: 5

# Servers
* https://api.prod.coolapp.com
* https://api.backup.coolapp.com

# Database
driver: postgres

## Credentials
user: admin
host: localhost

The parser converts that line-by-line grammar natively into this clean JavaScript object:

JSON

{
  "title": "Server Setup",
  "debugMode": false,
  "maxRetries": 5,
  "Servers": [
    "https://api.prod.coolapp.com",
    "https://api.backup.coolapp.com"
  ],
  "Database": {
    "driver": "postgres",
    "Credentials": {
      "user": "admin",
      "host": "localhost"
    }
  }
}

Why use it?

Zero-Bracket Nesting: Structure child objects implicitly via standard Markdown headings (#, ##, ###).
Implicit Type Inference: Auto-detects numbers, floats, booleans, and null without forcing everything into strings.
LLM Token Efficiency: By completely eliminating repetitive structural punctuation (brackets, braces, double quotes), it drops raw character counts significantly. This makes it an incredibly compact format for injection into LLM context windows or system prompts.
Deterministic & Round-trip Safe: You can bi-directionally parse() and stringify() standard JS objects cleanly.
Zero YAML-style Indentation Rules: No complex multi-space layout rules. It uses simple syntax suffixes like # Users[] for explicit complex arrays.

Tech Stack & Edge Cases

It's written in TypeScript with a tiny, deterministic grammar footprint. It natively handles edge cases like dynamic backtick delimiter matching (great for embedded code blocks) and automatic markdown language tag stripping (e.g. \``javascript`).

I just pushed the v1.0.3 tag. I'd love to get your feedback on the parser architecture, the grammar design, or potential use-cases you see for it in your own workflows!

Repository & NPM Info:

I will drop the live, clickable GitHub and NPM links in the comments section below.

reddit.com

u/No_Read2299 — 10 days ago

▲ 4 r/AIToolsPerformance+3 crossposts

[Showcase] Omnix v0.5: Local Multi-Modal Studio & Headless Inference Engine via WebGPU (Janus-Pro Native Integration)

Hey everyone! Two months ago, I posted here about Omnix—my local-first AI orchestration app using Transformers.js and ONNX. (OP: https://www.reddit.com/r/OpenSourceAI/comments/1smp8om/omnix_locail_ai_client_gui_and_api_using/ )

Since then, I’ve completely overhauled the architecture, executed the structural flip to a CLI/Server-first backend, and cracked some massive hurdles regarding consumer hardware VRAM constraints.

We just hit v0.5.0, and it's fully functional on local rigs.

GitHub: https://github.com/LoanLemon/Omnix

🚀 What’s New in v0.5

Janus-Pro-1B In-Browser Integration: Native support for DeepSeek’s Janus-Pro, bringing autoregressive text-to-image generation directly into the local environment.
Asymmetric Hybrid Execution Strategy: To beat severe consumer VRAM limits, Omnix dynamically splits execution. It offloads memory-heavy raw embedding lookups (prepare_inputs_embeds) to CPU-side WebAssembly (WASM), while keeping core self-attention blocks, decoding matrices, and image decoding layers under full WebGPU hardware acceleration.
Shader F16 Fallback Protection: If graphics drivers don't support shader-f16 compliance, the pipeline automatically degrades gracefully to FP32 or integer-quantized Q4 parameters instead of throwing compilation crashes.
Headless Inference Daemon Mode: You can now run omnix --silent to use it strictly as a background service. It supports process attachment (--dependent-pid <PID>), meaning external tools can spin up Omnix as a self-healing background inference engine that automatically shuts down when the parent app exits.
Multi-Client Input Normalization Middleware: Cleaned up the Express pipeline so it automatically detects and normalizes raw text, nested stringified JSON, or double-wrapped structures. You can hit the local endpoints directly from a browser, a basic curl, or even messy PowerShell Invoke-RestMethod scripts without parsing failures.
Proactive Tensor Garbage Collection: Rigorous post-inference memory reclamation routines are now built into the worker to deallocate native WebGPU buffers and release JS heap objects, preventing memory leaks during long sessions.

🛠️ Current Capabilities Matrix

Text & Vision (ChatML Layouts)
Text-to-Image & Image Interpretation
STT (Speech-to-Text) & TTS (via Kokoro-js)
Music Generation
Live Mode (Real-time screen and voice analysis)
~~Developer Sandbox~~ ~~(For executing and generating code)~~ [WIP]

📦 For Developers & Contributors

The app now exposes a robust local REST and WebSocket API running at http://localhost:9777/api.

Now that the core engine infrastructure is stable and highly performant, I'm looking for contributors who want to help expand our pipeline, optimize the dynamic quantization matrices, or build out UI features on top of the server layer.

Check out the repo, try running the Electron desktop app (which allows up to 16GB of heap memory configuration for massive models), and let me know what you think or if you hit any hardware snags!

Repo: https://github.com/LoanLemon/Omnix

u/No_Read2299 — 12 days ago

u/No_Read2299