unstuck-scaling

name	unstuck-scaling
description	Use when AI agents frequently hit dead ends, when reliability is the main constraint on scaling utility, or when general model improvements don't solve specific blockers

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

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Source: Anton Osika (Lovable, GPT Engineer) via Lenny's Podcast