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

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