• Freezing and hitting context limits. Bloated workspace files exhaust the context window mid-response.
• Slow responses. 15-20KB+ of context injected every message = hundreds of milliseconds of latency per reply.
• Forgetting everything. New session = blank slate. No memory of yesterday's work or decisions.
• Inconsistent behavior. Without clear rules, personality drifts between sessions.
• Doing everything the expensive way. Main model writes code, does research, AND orchestrates — all at top-tier pricing.
• Flying blind. No web search means guessing at anything after training cutoff.
• Wrong model choice. Using whatever was default without considering the tradeoffs.

What This Fixes

After this setup:

| Metric | Before | After | |--------|--------|-------| | Context per msg | 15-20 KB | 4-5 KB | | Time to respond | 4-8 sec | 1-2 sec | | Memory recall | Forgets daily | Remembers weeks | | Token cost/msg | ~5,000 tokens | ~1,500 tokens | | Long sessions | Degrades | Stable | | Concurrent tasks | One at a time | Multiple parallel |

How It Works

You ask a question
    ↓
Orchestrator (main model, lean context ~5KB)
    ↓
┌─────────────────────────────────────────┐
│  memory_search() — 45ms, local, $0     │
│  ┌─────────┐  ┌──────────┐  ┌────────┐ │
│  │MEMORY.md│→ │memory/*.md│→ │vault/* │ │
│  │(index)  │  │(quick)   │  │(deep)  │ │
│  └─────────┘  └───────...