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

┌─────────────────────────────────────────────────────────────────┐
│                                                                  │
│     ┌───────────────────┐                                       │
│     │  IDENTIFY         │                                       │
│     │  'Stuck' Points   │                                       │
│     │  (auth, payments) │                                       │
│     └─────────┬─────────┘                                       │
│               │                                                  │
│               ▼                                                  │
│     ┌───────────────────┐                                       │
│     │  ADDRESS          │                                       │
│     │  Specific         │                                       │
│     │  Bottlenecks      │                                       │
│     └─────────┬─────────┘                                       │
│               │                                                  │
│               ▼                                                  │
│     ┌───────────────────┐                                       │
│     │  QUANTITATIVELY   │                                       │
│     │  Tune System      │                                       │
│     │  (pass/fail rate) │                                       │
│     └─────────┬─────────┘                                       │
│               │                                                  │
│               ▼                                                  │
│     ┌───────────────────┐                                       │
│     │  FAST FEEDBACK    │─────────────────────────┐             │
│     │  Loop             │                         │             │
│     └───────────────────┘                         │             │
│               ▲                                   │             │
│               └───────────────────────────────────┘             │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

Key Principles

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