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The author describes building a custom AI research pipeline using multiple subscriptions and cheaper models to reduce token costs, learning firsthand how to optimize token usage while researching tokenomics.
Cognition replaced the Opus model with Fable in Devin's Fusion architecture, achieving higher performance at lower cost despite Fable's higher per-token price, through better delegation and reduced lead model turns.
Open-sourced a pre-call spend gate and tamper-evident audit log to prevent runaway API costs from recursive loops in autonomous agents, MIT-licensed with Postgres+TypeScript demo.
The author shares lessons from instrumenting AI agent tool calls, revealing that tools like web_search can account for ~50% of spend, and highlighting the importance of tracking p95 latency and attributing costs per workflow or customer to avoid surprises.