Run Local VLMs from HuggingFace — Even on Mac Mini 8GB Download and run SmolVLM2, Qwen-VL, LLaVA, MiniCPM-V locally. Your AI security camera agent sees through these eyes.

Chat with Your AI Camera Agent "Who was at the door?" — Your agent searches footage, reasons about what happened, and answers with timestamps and clips.

• Skill architecture — pluggable SKILL.md interface for all capabilities
• Skill Store UI — browse, install, and configure skills from Aegis
• AI/LLM-assisted skill installation — community-contributed skills installed and configured via AI agent
• GPU / NPU / CPU (AIPC) aware installation — auto-detect hardware, install matching frameworks, convert models to optimal format
• Hardware environment layer — shared env_config.py for auto-detection + model optimization across NVIDIA, AMD, Apple Silicon, Intel, and CPU
• Skill development — 18 skills across 9 categories, actively expanding with community contributions

Each skill is a self-contained module with its own model, parameters, and communication protocol. See the Skill Development Guide and Platform Parameters to build your own.

✅ Ready · 🧪 Testing · 📐 Planned

Registry: All skills are indexed in skills.json for programmatic discovery.

The easiest way to run DeepCamera's AI skills. Aegis connects everything — cameras, models, skills, and you.

• 📷 Connect cameras in seconds — add RTSP/ONVIF cameras, webcams, or iPhone cameras for a quick test
• 🤖 Built-in local LLM & VLM — llama-server included, no separate setup needed
• 📦 One-click skill deployment — install skills from the catalog with AI-assisted troubleshooting
• 🔽 One-click HuggingFace downloads — browse and run Qwen, DeepSeek, SmolVLM, LLaVA, MiniCPM-V
• 📊 Find the best VLM for your machine — benchmark models on your own hardware with HomeSec-Bench
• 💬 Talk to your guard — via Telegram, Discord, or Slack. Ask what happened, tell it what to watch for, get AI-reasoned answers with footage.

State-of-the-art detection running locally on any hardware, fully integrated as a DeepCamera skill.

YOLO26 (Jan 2026) eliminates NMS and DFL for cleaner exports and lower latency. Pick the size that fits your hardware:

All models detect 80+ COCO classes: people, vehicles, animals, everyday objects.

The shared env_config.py auto-detects your GPU and converts the model to the fastest native format — zero manual setup:

Detection runs as a parallel pipeline alongside VLM analysis — never blocks your AI agent:

Camera → Frame Governor → detect.py (JSONL) → Aegis IPC → Live Overlay
                5 FPS           ↓
                          perf_stats (p50/p95/p99 latency)

• 🖱️ Click to setup — one button in Aegis installs everything, no terminal needed
• 🤖 AI-driven environment config — autonomous agent detects your GPU, installs the right framework (CUDA/ROCm/CoreML/OpenVINO), converts models, and verifies the setup
• 📺 Live bounding boxes — detection results rendered as overlays on RTSP camera streams
• 📊 Built-in performance profiling — aggregate latency stats (p50/p95/p99) emitted every 50 frames
• ⚡ Auto start — set auto_start: true to begin detecting when Aegis launches

📖 Full Skill Documentation →

HomeSec-Bench is a 143-test security benchmark that measures how well your local AI performs as a security guard. It tests what matters: Can it detect a person in fog? Classify a break-in vs. a delivery? Resist prompt injection? Route alerts correctly at 3 AM?

Run it on your own hardware to know exactly where your setup stands.

Running on a Mac M1 Mini 8GB: local Qwen3.5-4B scores 39/54 (72%), cloud GPT-5.2 scores 46/48 (96%), and the hybrid config reaches 53/54 (98%). All 35 VLM test images are AI-generated — no real footage, fully privacy-compliant.

📄 Read the Paper · 🔬 Run It Yourself · 📋 Test Scenarios

• 💬 Slack Community — help, discussions, and camera setup assistance
• 🐛 GitHub Issues — technical support and bug reports
• 🏢 Commercial Support — pipeline optimization, custom models, edge deployment