How I decide when AI belongs in my writing and coding workflow, what I measure when I use agents, and where I refuse the tool so learning and quality stay mine.
I grew up thinking reading meant screenshotting pages with my eyes, built rigid coping systems to manage ADHD, dyslexia, and possible ASD, and that same obsession with how minds process information eventually drew me toward building models from first principles.
Building with AI in 2026 often means paying for unstable output, surviving arbitrary rate limits, and absorbing emotional and financial churn that vendors do not price as failures.
Proposes Fetch Quests, a smart-contract platform where technical jobs are posted as clear quests with automatic payment on verified completion via GitHub CI and Ethereum. The goal is fetch-quest simplicity for real technical work, with transparent reputation and no middlemen holding your money.
Development is shifting from writing code to directing AI agents that write code for you. Claude Code, git tree workflows, and prompt strategy are the key skills for the new orchestration-focused workflow, with open-source tools catching up.
A day of holiday book shopping leads to a reflection on where intelligence comes from. After conversations with family, the author lands on curiosity, skepticism, and attention as the pillars of how intelligence is proffered, with motivation as a multiplier.
Recounts a frustrating $149 optometry visit that provided no actual treatment for a chalazion, just a useless antibiotic and a referral that went nowhere. The lesson is simple. Always check reviews before visiting any healthcare provider, no matter how desperate you are.
Introduces the author, a self-taught engineer turned startup founder in San Francisco, and his philosophy on AI tools as productivity amplifiers that need clear attribution. He built attest.97115104.com, a free badge generation service for marking content as human-generated or AI-assisted.