The Unstuck Scaling Framework
Overview
A systematic approach to improving AI reliability by treating "getting stuck" as the primary bottleneck. Instead of broad improvements, painstakingly identify specific failure modes and create tight feedback loops.
Core principle: Address specific bottlenecks, not general intelligence.
The Cycle
code
┌─────────────────────────────────────────────────────────────────┐ │ │ │ ┌───────────────────┐ │ │ │ IDENTIFY │ │ │ │ 'Stuck' Points │ │ │ │ (auth, payments) │ │ │ └─────────┬─────────┘ │ │ │ │ │ ▼ │ │ ┌───────────────────┐ │ │ │ ADDRESS │ │ │ │ Specific │ │ │ │ Bottlenecks │ │ │ └─────────┬─────────┘ │ │ │ │ │ ▼ │ │ ┌───────────────────┐ │ │ │ QUANTITATIVELY │ │ │ │ Tune System │ │ │ │ (pass/fail rate) │ │ │ └─────────┬─────────┘ │ │ │ │ │ ▼ │ │ ┌───────────────────┐ │ │ │ FAST FEEDBACK │─────────────────────────┐ │ │ │ Loop │ │ │ │ └───────────────────┘ │ │ │ ▲ │ │ │ └───────────────────────────────────┘ │ │ │ └─────────────────────────────────────────────────────────────────┘
Key Principles
| Principle | Description |
|---|---|
| Specific blockers | Identify exact points where AI fails |
| Quantitative tuning | Measure stuck rates, not vibes |
| Fast feedback | Rapid iteration on fixes |
| Bottleneck focus | Specific roadblocks > general intelligence |
Common Mistakes
- •Focusing on general model improvements
- •Failing to measure "stuck" rates quantitatively
- •Slow feedback loops preventing rapid iteration
Source: Anton Osika (Lovable, GPT Engineer) via Lenny's Podcast