BigEd CC is a centralized AI agent orchestration platform for managing local and cloud LLMs from a single interface. It coordinates a fleet of 130+ specialized AI agents across code review, security auditing, research, ML training, and knowledge management — with enterprise-grade security, multi-tenant support, and hardware-aware scaling.
Built through AI-assisted development, BigEd CC is both a functional platform and a case study in directing complex software architecture through iterative AI collaboration. The human role was architectural direction, quality control, debugging, and system design — the kind of work that doesn't show up in lines-of-code metrics but determines whether a system works.
- Model Management — Switch between local (Ollama), Claude, and Gemini from one interface. See which model is active, what it costs, and how fast it responds.
- 130+ AI Skills — Code review, security audit, web research, ML training, knowledge indexing, and more — all dispatched to agents automatically.
- Dynamic Agent Scaling — 4 core agents + demand-based scaling up to 16, based on your hardware.
- Dr. Ders — A dual-supervisor hardware governor that monitors thermals, VRAM, and model health with event-driven wake-up timing.
- Fleet Dashboard — Real-time web UI at localhost:5555 showing agent status, task queues, and performance metrics via SSE.
- One-Click Setup — Installer handles Python, Ollama, models, and dependencies. There's also a first-run walkthrough in the GUI.
- Auto-Save Backup — Periodic snapshots so you don't lose fleet state.
- Cost Tracking — Per-call token usage and estimated costs for API providers.
BigEd CC was built through thousands of iterative AI-assisted development sessions. The architecture, quality standards, debugging direction, and system design decisions were human-directed throughout.
| Role | Contributor | What This Means |
|---|---|---|
| Architecture & System Design | Max (human) | All architectural decisions, system scope, feature prioritization, quality standards, and debugging direction |
| Code Generation | Claude Code (Opus 4.6) | Primary code author under human direction — wrote implementations from architectural specs and prompt-driven requirements |
| Review & Iteration | Claude (Sonnet 4.6) | Code review, audits, skill generation, iterative improvements |
| Independent Audit | Gemini Pro (2.5/3.1) | Architecture audits, second opinions, cross-validation |
| Quality Assurance | Max (human) | Testing, debugging, catch-and-correct for AI-introduced bugs, documentation maintenance |
Download Setup.exe from Releases → Run → Follow wizard
git clone https://github.com/SwiftWing21/BigEd.git
cd BigEd
python fleet/dependency_check.py # pre-flight check
python fleet/smoke_test.py --fast # verify smoke tests
python BigEd/launcher/launcher.py # launch GUIBigEd CC
├── BigEd/launcher/ — GUI launcher (customtkinter)
│ ├── ui/ — Settings, consoles, dialogs, boot sequence
│ ├── modules/ — Pluggable modules (Intelligence, Ingestion, Outputs)
│ └── fonts/ — Custom pixel fonts
├── fleet/ — 130+ skill AI worker fleet
│ ├── supervisor.py — Process lifecycle + dynamic scaling
│ ├── hw_supervisor.py — Dr. Ders (thermal + model management)
│ ├── dashboard.py — Web dashboard (localhost:5555)
│ ├── worker.py — Generic task executor
│ ├── skills/ — 130+ registered skills
│ └── knowledge/ — Agent-generated artifacts
├── autoresearch/ — ML training pipeline (inspired by Karpathy)
├── scripts/ — Setup scripts (Windows/Linux/macOS)
└── docs/ — Specs, flowcharts, design docs
| Provider | Models | Auth | Cost |
|---|---|---|---|
| Ollama (Local) | qwen3:8b, 4b, 1.7b, 0.6b | None | Free |
| Claude | Haiku, Sonnet, Opus | API key or OAuth | Per-token |
| Gemini | Flash, Pro | API key or OAuth | Per-token |
OAuth models (Claude Code, Gemini CLI) work through VS Code with pre-configured project files — BigEd writes a task briefing, opens VS Code, and you're ready to go.
- File access control — SOC 2 compliant per-zone permissions (read / read-write / full)
- DLP — Secret detection and output scrubbing before API calls
- Audit logging — Tamper-evident HMAC-signed logs for API calls, file access, and config changes
- RBAC — Role-based access control with 5 roles and 7 permission levels
- Air-gap mode — Full offline operation with local models only
- Fleet federation — Multi-device task routing with peer overflow
- Multi-tenant architecture — 4-tier tenancy scaling from single-user to enterprise
- Training pipeline — Autonomous ML training loop inspired by Karpathy's build-nanogpt
| Repo | Purpose |
|---|---|
| BigEd | Core platform — launcher, fleet, dashboard, skills, ML pipeline |
| BigEd-ModuleHub | Optional modules — UI extensions loaded at runtime |