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AI agent billing spikes come from three places: using a stronger model than the task requires, no depth limit on tool call loops, and context window waste from passing full history every round. The correct cost control strategy is matching model capability to task complexity, not using the strongest model for everything.
The hardest part of designing Uber isn't picking the right technologies — it's breaking a vague, enormous problem into discussable sub-problems
Companies that genuinely self-improve with AI don't just adopt tools — they build closed feedback loops: data collection → model inference → automated execution → evaluation → better data. This requires organizational structure and incentive alignment to match.
Cursor is an AI-powered code editor by Anysphere, built by four MIT graduates, that hit $500M ARR within two years of launch. This article distills the real engineering lessons they've shared publicly: why they forked VSCode instead of building an extension, how Tab prediction's latency engineering works, and the hard production lessons from shipping Agent Mode.