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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
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.
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
ā ā
ā āāāāāāāāāāāāāāāāāāāāā ā
ā ā IDENTIFY ā ā
ā ā 'Stuck' Points ā ā
ā ā (auth, payments) ā ā
ā āāāāāāāāāāā¬āāāāāāāāāā ā
ā ā ā
ā ā¼ ā
ā āāāāāāāāāāāāāāāāāāāāā ā
ā ā ADDRESS ā ā
ā ā Specific ā ā
ā ā Bottlenecks ā ā
ā āāāāāāāāāāā¬āāāāāāāāāā ā
ā ā ā
ā ā¼ ā
ā āāāāāāāāāāāāāāāāāāāāā ā
ā ā QUANTITATIVELY ā ā
ā ā Tune System ā ā
ā ā (pass/fail rate) ā ā
ā āāāāāāāāāāā¬āāāāāāāāāā ā
ā ā ā
ā ā¼ ā
ā āāāāāāāāāāāāāāāāāāāāā ā
ā ā FAST FEEDBACK āāāāāāāāāāāāāāāāāāāāāāāāāāā ā
ā ā Loop ā ā ā
ā āāāāāāāāāāāāāāāāāāāāā ā ā
ā ā² ā ā
ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā ā
ā ā
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
| 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 |
Source: Anton Osika (Lovable, GPT Engineer) via Lenny's Podcast
