Self-hosted AI command center. 11 services. 69 APIs. Zero cloud.
LLM agents, SMM autopilot for 7 platforms, image/video/3D/music generation,
RAG, LoRA fine-tuning, voice cloning, Telegram bot with vision — all from localhost:9000
Why another AI dashboard? Because no other open-source project gives you LLM orchestration, automated SMM for 7 platforms, image/video/3D generation, RAG, fine-tuning, Telegram bot with 14 personas, voice cloning, and MCP integration for Claude — all in a single self-hosted panel with zero cloud dependencies.
- 🤖 11 AI Services managed from one UI — Ollama, ComfyUI, Whisper, Qdrant, SearXNG, and more
- 📱 SMM AI Department — discover trends → generate posts → create images → auto-publish to Telegram, Twitter, Facebook, Instagram, Threads, LinkedIn, Discord simultaneously
- 🧠 Multi-Agent System — 13 roles, 3 modes (Solo/Team/Orchestrator), 9 tools including web search, code execution, RAG
- 🎨 Full Generation Pipeline — Image (FLUX) → Video (Wan2.2) → 3D (Hunyuan3D) with smart VRAM management
- 📊 69 API Endpoints — everything is programmable, extensible, and automatable
- 🔒 100% Local — your data never leaves your machine. No API keys required for core features
What is this? · AI Model Stack · Features · Dashboard · Agents · RAG · LoRA · Pipeline · Telegram Bot · SMM · MCP Server · Quick Start · Requirements · FAQ
A self-hosted web panel (localhost:9000) that unifies your entire local AI infrastructure into one powerful dashboard. No subscriptions, no cloud APIs, no data leaving your machine.
┌──────────────────────────────── NeuralForge ──────────────────────────────────┐
│ │
│ Dashboard Agents RAG Telegram LoRA SMM │
│ ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐ │
│ │GPU/VRAM│ │13 Roles│ │Qdrant +│ │14 Meme │ │Unsloth │ │7 Socials│ │
│ │Services│ │Solo │ │ONNX GPU│ │Personas│ │LoRA │ │Trend AI │ │
│ │Metrics │ │Team │ │1800/sec│ │Voice │ │16 base │ │Post Gen │ │
│ │Alerts │ │Orchestr│ │Multi-DB│ │Cloning │ │models │ │Analytics│ │
│ └────────┘ └────────┘ └────────┘ └────────┘ └────────┘ └────────┘ │
│ │
│ Pipeline: Image ──→ Video ──→ 3D │ MCP Server: 24 tools for Claude │
│ (ComfyUI) (Wan2GP) (Hunyuan3D) │ + Music, TTS, STT, Search... │
└────────────────────────────────────────────────────────────────────────────────┘
Every model runs locally via Ollama — no API keys, no cloud, no subscriptions.
| Model | Size | VRAM | Used for |
|---|---|---|---|
| Qwen 3.5 | 35B (A3B MoE) | ~20 GB | Primary workhorse — agents, SMM posts, trend analysis |
| Nemotron 3 Nano | 30B | ~18 GB | RAG answers, balanced quality/speed |
| Mistral Small | 24B | ~14 GB | Summarization, translation, email |
| Qwen 3.5 | 9B | ~6 GB | Telegram bot — fast persona responses |
| Gemma 3 | 27B | ~16 GB | Alternative general-purpose |
| DeepSeek R1 | 14B | ~9 GB | Math, reasoning, code |
| + 9 more | 1B–35B | 1–20 GB | User-selectable per task |
| Model | Size | VRAM | Used for |
|---|---|---|---|
| MiniCPM-V | 8B | ~5 GB | Telegram bot photo analysis — describes images, answers questions about photos sent to your account |
| Qwen2.5-VL | 27B | ~16 GB | Agent image analysis tool — detailed visual Q&A |
| Model | Size | Speed | Used for |
|---|---|---|---|
| bge-m3 (ONNX) | 560M | 1,800 docs/sec | GPU-accelerated document indexing |
| bge-m3 (Ollama) | 560M | 10 docs/sec | Fallback CPU embedding |
| Model | Size | VRAM | Used for |
|---|---|---|---|
| Whisper (faster-whisper) | base/large | 2-10 GB | Speech-to-text, 99 languages, diarization |
| Qwen3-TTS | ~4 GB | ~4 GB | Text-to-speech, 3-second voice cloning |
| ACE-Step 1.5 | ~4 GB | 4-6 GB | AI music generation — lyrics + style → full song |
| Model | Engine | VRAM | Used for |
|---|---|---|---|
| FLUX Klein | ComfyUI | 8-12 GB | Image generation (SMM posts, pipeline) |
| Wan 2.2 | Wan2GP | 12-24 GB | Video generation from image + prompt |
| Hunyuan3D v2 | Gradio | 13-20 GB | 3D model generation from image |
| Model | Size | Training time |
|---|---|---|
| NVIDIA Nemotron 3 Nano | 4B | ~30 min |
| Llama 3.1 / 3.2 | 1B–8B | 30 min – 2h |
| Qwen 2.5 | 7B / 32B | 1–4h |
| Gemma 2 | 2B–27B | 30 min – 3h |
| Mistral v0.3 | 7B | ~1h |
| Phi 3.5 | 3.8B | ~40 min |
Total unique AI models available: 30+ — all running locally, swappable per task, with automatic VRAM management.
| Feature | Description | |
|---|---|---|
| GPU | Smart VRAM Management | Exclusive groups auto-stop conflicting services. Never OOM again |
| Dashboard | Real-time Monitoring | GPU temp, VRAM, RAM, CPU, disk — live metrics with health alerts |
| Agents | Multi-Agent Orchestration | 13 roles, 3 modes (Solo/Team/Orchestrator), shared memory, RAG tools |
| RAG | Vector Search at GPU Speed | ONNX embeddings at 1,800 docs/sec, Qdrant DB, multi-collection search |
| Bot | 14 Telegram Personas | Each with unique personality — from Philosopher to Crypto Maniac |
| Voice | Real-time Voice Cloning | Send voice → get reply in your own voice with AI-generated text |
| LoRA | Fine-Tuning UI | 16 base models, dataset upload, live training output, adapter export |
| Gen | Image→Video→3D Pipeline | Automated chain with smart VRAM switching between steps |
| MCP | Claude Code Integration | 24 tools — let Claude manage your entire AI stack |
| SMM | 7-Platform Social Media | Trend Scout → AI Post Writer → Image Gen → Auto-Publish to all platforms |
| Ext | YAML Module System | Add any new service in 10 lines of YAML |
The main hub. Everything starts here.
Live Metrics:
- GPU VRAM usage with free memory indicator
- GPU temperature and power draw
- RAM usage with available memory
- CPU load across all threads
- Disk usage with free space alerts
Service Management:
- Start/stop any service with one click
- Exclusive GPU groups — when you start ComfyUI, Wan2GP auto-stops (and vice versa). No more VRAM crashes
- Service health indicators (running/stopped/starting)
- Quick actions: "Start basics", "Stop heavy", "Free VRAM"
Monitoring:
- Active Ollama models with per-model VRAM usage
- GPU process list (what's eating your VRAM right now)
- Qdrant RAG collections with vector counts
- Storage breakdown by service (ComfyUI outputs, Wan2GP videos, etc.)
- Health alerts: GPU overheating, low disk, service down — all visible at a glance
YAML Module System — add any service:
name: My New Service
category: generation
start_cmd: "python3 app.py --port 7777"
port: 7777
vram_estimate: "4-8 GB"
exclusive_group: heavy_gpu # auto-stops conflicting servicesDrop it in modules/ → restart panel → it appears. That's it.
A full multi-agent framework built into the panel.
13 Role Presets:
| Role | What it does | Default model |
|---|---|---|
| Researcher | Web search, source analysis, fact compilation | Qwen 3.5 35B |
| Analyst | Data analysis, pattern recognition, insights | Qwen 3.5 35B |
| Coder | Write, debug, refactor code in any language | Qwen 3.5 35B |
| Writer | Articles, reports, creative writing | Qwen 3.5 35B |
| Critic | Quality review, scoring, improvement suggestions | Qwen 3.5 35B |
| Summarizer | Condense long texts into key points | Mistral Small 24B |
| Translator | Multi-language translation with context | Mistral Small 24B |
| Email Writer | Professional emails from brief instructions | Mistral Small 24B |
| Tester | Generate test cases, find edge cases | Qwen 3.5 35B |
| Trade Analyst | Market analysis, trend identification | Qwen 3.5 35B |
| Tutor | Explain concepts at adjustable complexity | Qwen 3.5 35B |
| Security Auditor | Code/config security review, vulnerability scan | Qwen 3.5 35B |
| Image Analyst | Describe and analyze images | Qwen Vision 27B |
3 Execution Modes:
| Mode | How it works | Best for |
|---|---|---|
| Solo | Single agent with tools | Quick tasks, Q&A |
| Team | Agent chain — each passes context to next | Complex multi-step tasks |
| Orchestrator | AI creates plan → delegates to agents → reviews result (retries if score < 7) | Ambitious tasks with quality control |
15 Team Presets: Pre-configured agent chains for common workflows — "Research → Analyze → Write", "Code → Test → Review", "Translate → Edit", and more.
Agent Tools:
web_search— search the internetread_url/deep_scrape— fetch and parse web pagesrun_python— execute Python coderead_file/write_file— file operationsanalyze_image— vision model for imagesrag_search— search your vector databaseanalyze_file— PDF/CSV/code analysis
Memory System:
- Shared memory between agents in a team
- Long-term memory with keyword tokenization and search
- Context passing modes: full chain or previous-agent-only
Ask questions about your documents. The AI retrieves relevant passages and answers with citations.
Performance:
| Method | Speed | GPU VRAM |
|---|---|---|
| ONNX GPU (bge-m3) | 1,800 texts/sec | ~2 GB |
| Ollama embeddings | 10 texts/sec | ~4 GB |
That's 180x faster indexing with ONNX.
Capabilities:
- Multi-format indexing — PDF, TXT, MD, DOCX, CSV, HTML
- Batch processing — index entire directories recursively
- Multi-collection — separate databases for different topics (e.g., "laws", "docs", "codebase")
- Smart search — auto-detects which collection to search based on query
- Context memory — remembers previous Q&A in the same chat session
- Embedding cache — repeat queries are instant
Built-in chat interface:
- Markdown rendering with syntax highlighting
- Copy button on every response
- Export conversation to Markdown file
- Collection selector and search settings
- localStorage persistence — your chat survives page reload
Example use case:
Indexed all 390 Estonian laws (52,314 vectors) — now ask legal questions in any language and get answers with article references.
Train custom model adapters directly from the panel UI.
16 Base Models Ready to Fine-Tune:
| Model | Size | Notes |
|---|---|---|
| Llama 3.1 | 8B | Great all-rounder |
| Llama 3.2 | 1B / 3B | Lightweight, fast |
| Mistral v0.3 | 7B | Strong reasoning |
| Qwen 2.5 | 7B / 32B | Multilingual |
| Gemma 2 | 2B / 9B / 27B | Google's latest |
| Phi 3.5 | 3.8B | Microsoft, compact |
| + custom | any | Enter any Unsloth-compatible model ID |
Training UI Features:
- Dataset upload (JSON, JSONL, CSV) or HuggingFace dataset ID
- Auto-format detection (instruction/output, messages, or raw text)
- Configurable: LoRA rank, alpha, epochs, batch size, learning rate, max sequence length
- Live training output — see loss, progress, ETA in real-time
- Timer showing elapsed training time
- Stop button to cancel mid-training
- Trained adapters listed with size and date
Powered by Unsloth — 2x faster training, 60% less memory than standard LoRA.
Automated Image → Video → 3D chain with smart VRAM management between steps.
| Step | Engine | VRAM | Automation |
|---|---|---|---|
| Image | ComfyUI (FLUX Klein 4B) | 8-12 GB | Fully automated API |
| Video | Wan2GP (Wan 2.2 / LTX) | 12-24 GB | Gradio API + manual fallback |
| 3D | Hunyuan3D | 13-20 GB | Gradio API + manual fallback |
VRAM is automatically freed between steps — only one heavy service runs at a time.
# 5 built-in examples
python3 pipeline.py --example robot # chibi robot → animate → 3D model
python3 pipeline.py --example dragon # crystal dragon → animate → 3D
python3 pipeline.py --example car # cyberpunk car → animate → 3D
python3 pipeline.py --example cat # cat astronaut → animate → 3D
python3 pipeline.py --example sword # magic sword → 3D (skip video)
# Custom prompt
python3 pipeline.py "a golden crown with gems" --steps image,3d
python3 pipeline.py "a phoenix" --video-prompt "spreads wings and flies"Not a Telegram bot — responds from your own account via Telethon User API.
14 Unique Personas:
| Persona | Style | |
|---|---|---|
| 🧘 | Philosopher | "You wrote 'hi', but what is a greeting if not a scream of loneliness into the void?" |
| 🧢 | Street Philosopher | "bro, your argument is logically inconsistent, purely by Kant" |
| 👾 | IT Demon | "segfault in your logic, recompile that thought" |
| 👵 | Granny from 2077 | "sweetie, browsing without a firewall again? you'll catch a virus!" |
| 🕵️ | Noir Detective | "The message came at 3am. Like all bad news in this city" |
| 🏴☠️ | Nerd Pirate | "arrr, your meme is a true treasure!" |
| 🐱 | Cat Overlord | "I'd help, but I need to lie down for 14 more hours" |
| 🔺 | Conspiracy Nut | "Telegram was created by Masons to track memes" |
| 🎭 | Budget Shakespeare | "To be online or not to be — that is the question!" |
| 🧟 | Polite Zombie | "good evening, could you... share some brains?" |
| 📋 | Corporate Robot | "let's sync on this in the next sprint" |
| 🫎 | Capybara | "why stress when you can just... not" + random capybara photo |
| 🚀 | Crypto Maniac | "RED CANDLE, I'M BANKRUPT, wait... GREEN! I'M RICH!" |
| 🛠️ | Custom | Write your own character |
Voice Clone Pipeline:
🎤 Voice in → ffmpeg (OGG→WAV) → Whisper STT → LLM response
→ unload LLM → Qwen3-TTS (clone voice) → ffmpeg (WAV→OGG) → 🔊 Voice out
Vision — Photo Analysis (MiniCPM-V 8B):
📸 Photo in → MiniCPM-V (image description) → LLM (persona-styled response) → 💬 Reply
Send a photo to your account → the bot describes it through the vision model → responds in character. Toggle on/off from panel UI.
Features:
- Vision mode — understands photos via MiniCPM-V 8B (auto VRAM swap: unload LLM → load vision → analyze → unload → reload LLM)
- Auto-detects language → responds in same language
- Conversation memory (5 exchanges per user)
- Session-based logs grouped by contact
- Voice clone toggle from panel UI
- Capybara persona sends random capybara photos via capy.lol API
Fully automated social media management system — from trend discovery to publishing across 7 platforms.
Complete Workflow:
Trend Scout → Post Writer → Image Gen → Content Queue → Auto-Publish
│ │ │ │ │
▼ ▼ ▼ ▼ ▼
6 Sources 2-Pass LLM ComfyUI FLUX Schedule + 7 Platforms
(Reddit,HN, (Scrape→ + ffmpeg Calendar simultaneously
GitHub,RSS, Summary→ resize view with retry
SearXNG, Platform
GoogTrends) posts)
7 Connected Platforms:
| Platform | Auth Method | Features |
|---|---|---|
| Telegram | Bot API | Text + Photo, channel posting |
| Discord | Webhook | Text + File upload |
| Twitter/X | OAuth 1.0a | Text + Media upload (Pay-Per-Use) |
| Page Token (permanent) | Text + Photo, Page posting | |
| Graph API via FB | Photo + Caption (via imgur) | |
| Threads | Threads API | Text + Image |
| OAuth 2.0 | Text + Image (3-step upload) |
Key Features:
- Trend Scout v2 — Multi-source intelligence with niche routing (tech, crypto, food, fitness, art, gaming, education, business), geo-detection, CJK filtering
- GitHub Trending — Hybrid search (API + trending page scrape), categories (Agent/LLM/RAG/Tool), velocity ranking, "already posted" markers
- Post Writer — 2-pass generation: scrapes source article → LLM summary → platform-specific posts with correct tone/length/hashtags. Custom context support
- Image Generation — AI-generated prompts → ComfyUI FLUX Klein → auto-resize for each platform. ComfyUI auto-starts and stops (VRAM management)
- Content Queue — SQLite-backed, edit/duplicate/regenerate posts, schedule with date/time picker, auto-publish via background scheduler
- Content Calendar — Weekly view with navigation, color-coded by status
- Batch Generation — Generate N days of content in one click with auto-scheduling
- Analytics — Metrics collection from FB/IG/Threads/LinkedIn APIs, per-platform breakdown, top posts ranking
- Token Health — Auto-refresh for expiring tokens (Threads, LinkedIn), dashboard monitoring
- Hashtag Manager — Per-platform limits (Instagram 28, Twitter 3, Discord 0), auto-trim at publish
- Publish Preview — Review all posts + image before publishing with platform selection
24 tools that let Claude Code directly manage your AI infrastructure:
| Category | Tools |
|---|---|
| System | get_system_status get_gpu_processes ollama_loaded_models check_health |
| Services | start_service stop_service stop_all_and_free_vram |
| RAG | rag_search rag_list_collections rag_index_file rag_index_directory ask_rag |
| Agents | run_agent run_agent_team run_orchestrator |
| Generate | generate_image run_pipeline |
| Fine-tune | finetune_start finetune_status finetune_stop |
| Utils | get_storage_info cleanup_storage run_backup convert_audio |
// Add to your project's .mcp.json
{ "mcpServers": { "neuralforge": { "command": "/path/to/neuralforge/run_mcp.sh" } } }Now Claude can: check GPU status, start services, search your RAG database, run agent teams, generate images, manage fine-tuning — all from natural language.
git clone https://github.com/DefinitelyN0tMe/neuralforge.git
cd neuralforge
chmod +x install.sh
./install.shcurl -fsSL https://ollama.com/install.sh | sh
# Pick a model for your VRAM
ollama pull qwen3.5:35b-a3b # 20GB VRAM — powerful
ollama pull nemotron-3-nano:30b # 18GB VRAM — balanced
ollama pull mistral-small:24b # 14GB VRAM — lighter# Qdrant for RAG (optional)
docker run -d --name qdrant -p 6333:6333 -v qdrant_data:/qdrant/storage qdrant/qdranthttp://localhost:9000
The Telegram AI Bot runs entirely from the panel UI — no code editing required.
- Open t.me/BotFather →
/newbot→ get your Bot Token - Get your Telegram API ID and API Hash from my.telegram.org
- In the panel → Telegram tab → paste your credentials → click Start
- The bot comes with 14 pre-configured personas (Philosopher, Crypto Maniac, etc.) — customize or create your own
Publish AI-generated posts to 7 social networks. All configuration is done through the panel UI.
- In the panel → SMM tab → + New Profile → follow the 4-step wizard
- On Step 2 (Platforms), each platform has built-in setup instructions:
- Telegram: Create bot via @BotFather → paste token + channel
- Discord: Server Settings → Integrations → Webhooks → copy URL
- Twitter/X: developer.x.com → Create App → OAuth 1.0a keys
- Facebook: developers.facebook.com → Create App (Business) → Page Token
- Instagram: Same Meta app → connect IG Business Account to FB Page
- Threads: Meta Developer App → Threads API → authorize
- LinkedIn: linkedin.com/developers → Create App → OAuth 2.0
- Scan trends → Generate posts → Publish to all platforms with one click
Tip: Start with just Telegram + Discord (easiest, no API keys needed beyond bot token/webhook). Add other platforms later.
| Component | Minimum | Recommended | Tested on |
|---|---|---|---|
| GPU | NVIDIA 12GB VRAM | 24GB VRAM | RTX 3090 24GB |
| RAM | 16 GB | 64+ GB | 128 GB DDR4 |
| Disk | 50 GB free | 200+ GB | 2TB NVMe |
| CPU | 4 cores | 16+ cores | Threadripper PRO 5955WX |
| OS | Ubuntu 22.04 | Ubuntu 24.04 | Ubuntu 24.04.2 |
| Python | 3.10 | 3.12 | 3.12.3 |
Also needed: NVIDIA drivers, CUDA, Docker, ffmpeg, Ollama
Browser ◄──────► FastAPI server.py :9000 ◄──────► Ollama :11434
│ (LLM inference)
├──► Module Manager
│ ├── ComfyUI :8188 (image gen)
│ ├── Wan2GP :7860 (video gen)
│ ├── Hunyuan3D :7870 (3D gen)
│ ├── ACE-Step :7880 (music gen)
│ ├── Qwen3-TTS :7890 (voice clone)
│ ├── Whisper :7895 (speech-to-text)
│ └── ... (add your own via YAML)
│
├──► Qdrant :6333 (vector DB for RAG)
├──► Telegram Bot (Telethon user API)
└──► MCP Server (Claude Code bridge)
telegram_bot.py ◄──► Ollama (text) + Whisper (STT) + Qwen3-TTS (voice clone)
pipeline.py ◄──► ComfyUI → Wan2GP → Hunyuan3D (sequential, VRAM-managed)
mcp_server.py ◄──► server.py API (24 tools exposed to Claude Code)
neuralforge/
├── server.py # FastAPI backend (69 API endpoints)
├── telegram_bot.py # Telegram bot — 14 personas, voice clone, vision
├── pipeline.py # Image → Video → 3D generation pipeline
├── mcp_server.py # MCP server — 24 tools for Claude Code
├── smm/ # SMM AI Department (modular package)
│ ├── __init__.py # Router registration
│ ├── routes.py # All SMM routes + scheduler + publishing
│ └── db.py # SQLite: queue, trends, analytics
├── templates/
│ └── index.html # Single-page frontend (vanilla JS, no framework)
├── modules/ # YAML service definitions (drop-in)
│ ├── ollama.yaml # LLM inference
│ ├── comfyui.yaml # Image generation
│ ├── wan2gp.yaml # Video generation
│ ├── hunyuan3d.yaml # 3D model generation
│ ├── ace-step.yaml # Music generation
│ ├── qwen3-tts.yaml # Text-to-speech + voice cloning
│ ├── whisper-webui.yaml # Speech recognition
│ └── ... # add your own!
├── install.sh # Automated installer with path patching
├── requirements.txt # Python dependencies
├── run_mcp.sh # MCP server launcher
├── backup.sh # Backup script
├── telegram_config.example.json
├── LICENSE
└── README.md
Can I use this without a GPU?
Partially. Ollama can run on CPU (slow). RAG chat and Telegram text personas work fine. Image/video/3D generation and voice cloning need NVIDIA GPU.Will this work on WSL2 / Windows?
Not tested. Designed for native Ubuntu. WSL2 with CUDA passthrough might work but YMMV.Can I add my own Telegram persona?
Yes — use "Custom" in the panel UI, or add a new key to the personas dict in telegram_config.json.How much disk space do I need?
Panel itself is ~1MB. Models are what take space: a 30B model ≈ 18GB. Budget 50-200GB depending on models and services.Is my data private?
100%. Everything runs locally. No telemetry, no cloud calls, no external API keys required.Can I use a different LLM provider?
The panel is built around Ollama, but any OpenAI-compatible API on localhost would work with minor code changes.Which LLM models are supported?
Any model available through Ollama — Qwen, Mistral, Llama, Gemma, DeepSeek, Phi, Nemotron, and 100+ more. The panel ships with 15 pre-configured models. You can add any Ollama model through the UI.How does the Telegram bot analyze photos?
When Vision mode is enabled, the bot uses MiniCPM-V (8B) to understand photos sent to your account. It automatically swaps VRAM: unloads the chat LLM → loads the vision model → analyzes the image → unloads vision → reloads the chat LLM. All automatic.Can I use this for commercial social media management?
Yes. The SMM module supports 7 platforms, batch content generation, scheduled publishing, and analytics. It's designed for professional use — but you'll need API access for each platform (some are free, some require developer accounts).PRs welcome. The codebase is intentionally simple — vanilla JS frontend, single FastAPI backend, no build step.
Good first contributions:
- New Telegram personas
- New module YAML definitions
- UI improvements
- Automated Gradio API for Wan2GP / Hunyuan3D
- Documentation / translations
MIT — do whatever you want with it.
| Project | Used for |
|---|---|
| Ollama | Local LLM inference (Qwen, Mistral, Gemma, DeepSeek, Nemotron) |
| FastAPI | Backend API (69 endpoints) |
| Telethon | Telegram User API |
| Qdrant | Vector database for RAG |
| bge-m3 | Multilingual embeddings (ONNX GPU-accelerated) |
| MiniCPM-V | Vision model — Telegram photo analysis |
| ComfyUI | Image generation (FLUX Klein) |
| Wan2GP | Video generation (Wan 2.2, LTX-Video) |
| Hunyuan3D | 3D model generation |
| faster-whisper | Speech recognition (99 languages) |
| Qwen3-TTS | Text-to-speech & 3-second voice cloning |
| ACE-Step | Music generation |
| Unsloth | LoRA fine-tuning (2x faster, 60% less memory) |
| SearXNG | Privacy-first meta-search for AI agents |
| Perplexica | AI-powered search engine |
| Open WebUI | Chat interface for LLM models |
Built with obsession by @DefinitelyN0tMe and Claude Code
Keywords: self-hosted AI, local LLM, AI dashboard, Ollama GUI, AI agents, multi-agent system, social media automation, SMM AI, Telegram bot, voice cloning, RAG, vector search, LoRA fine-tuning, image generation, video generation, 3D generation, MCP server, Claude Code, FLUX, ComfyUI, Whisper, text-to-speech, VRAM management, GPU dashboard, AI control panel, open source AI platform