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.
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.
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.
I’ve toyed with creating a personal website for years—something beyond just a blog. I’ve started many before but never had the discipline to maintain them. Let’s see if this time is different.
I am on a journey of exploring digital gardens of AI agents memory management. I’m trying to carve out time to finish reading “AI Engineer” by @chipro—it’s exceptionally well-written and thoroughly researched, warm recommendation.
I’m working to hit at least 10k steps daily and bring more structure to my day. Dedicating enough time for new intake, which seems impossible with all AI releases.
Apparently, @timoreilly considered it noteworthy to highlight that I agree with @KentBeck 🤷♂️ honored and humbled!