Why Senior Engineers Overlook Small AI Wins
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.
AI Coding Agent Pricing
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.
Human requests are binary: fix this thing, answer this question. But agents operate in probabilistic space, spawning subprocess after subprocess, each one justified by some internal logic tree I never asked for. The billing model assumes perfect alignment between what I want and what the machine thinks I need. Spoiler: there isn’t any.
Claude 4 as image critic.
Blink, and the entire AI landscape could shift
The AI developer tooling market is moving faster than ever, with big players acquiring startups and releasing powerful coding agents. Interfaces are becoming commoditized, token economics will drive cost efficiency, spec-driven workflows prevail, memory persistence is key, and incumbents' flywheel grows stronger.
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