AI doesn't just make work faster--it amplifies hidden constraints. At Anthropic, eliminating coding bottlenecks revealed decision-making, integration, and context as the real limitations. Every breakthrough follows this pattern: solve one constraint, amplify the next.
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Today's AI agents excel at computer operation and research, maintain coherence for hours, favor curious problem-solvers over technical experts, and are democratizing software creation while challenging traditional employment models.
A practical guide to Claude Code, including setup, codebase Q&A, tool usage, context best practices, scripting, and power user tips, distilled from Boris Cherny's talk.
Senior engineers often dismiss small AI coding power-ups (like smarter autocomplete or better error messages), not realizing these tweaks can totally change how users feel about a product.
The only way you’re going to figure this out is by getting your hands dirty and seeing what works.
Claude 4 Sonnet loves complex dashboard visualisations. I have been playing with my Garmin data to better understand agentic future of data science research.
Current AI coding agents have misaligned pricing—users pay for agent inefficiencies and over-iteration. Credit burn rates are unpredictable and scale with agent behavior, not user value. Solutions include fair-use models, temporal arbitrage, outcome-based pricing, and hybrid local/remote approaches.
It is wild watching an AI agent pursue dependency chains with robotic determination, burning computational resources chasing “just one more fix.” It’s just what happens when you engage with complex systems, whether you’re carbon-based or running on silicon. The yak always needs shaving, apparently.
Wrote a short research paper with help from Cursor and based on the survey I did with v0 and distributed during my O’Reilly talk.
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