Learn the "Grill Me" AI skill—a powerful technique to deeply explore coding plans, requirements, and ideas through structured AI conversations.
AI coding wisdom from the people who would know
Essays and threads from experienced developers who've gone deep on AI-assisted coding.
AI Makes the Easy Part Easier and the Hard Part Harder for Developers
AI handles writing code but leaves the hard work: investigation, context, validation. Why vibe coding has limits and AI assistance can backfire.
AI Coding will Prevent Expertise | Lars Faye
The need for ongoing friction in long-term skill formation.
Embracing Gen AI at Work
Today artificial intelligence can be harnessed by nearly anyone, using commands in everyday language instead of code. Soon it will transform more than 40% of all work activity, according to the authors’ research. In this new era of collaboration between humans and machines, the ability to leverage AI effectively will be critical to your professional success. This article describes the three kinds of “fusion skills” you need to get the best results from gen AI. Intelligent interrogation involves instructing large language models to perform in ways that generate better outcomes—by, say, breaking processes down into steps or visualizing multiple potential paths to a solution. Judgment integration is about incorporating expert and ethical human discernment to make AI’s output more trustworthy, reliable, and accurate. It entails augmenting a model’s training sources with authoritative knowledge bases when necessary, keeping biases out of prompts, ensuring the privacy of any data used by the models, and scrutinizing suspect output. With reciprocal apprenticing, you tailor gen AI to your company’s specific business context by including rich organizational data and know-how into the commands you give it. As you become better at doing that, you yourself learn how to train the AI to tackle more-sophisticated challenges. The AI revolution is already here. Learning these three skills will prepare you to thrive in it.
I work, I think? - Annotated
How AI may quietly dismantle the feedback loop that turns inexperienced people into competent ones, and why my work matters to me.
In Search of Vibe Coding Nirvana - Day 1 - Wesley's notes
Deliberate practice in exploring and experimenting with AI tooling
DrCatHicks/learning-opportunities
A Claude or Codex skill for deliberate skill development during AI-assisted coding
10 things I learned from burning myself out with AI coding agents
Opinion: As software power tools, AI agents may make people busier than ever before.
The antidote to AI fatigue — Answer.ai Solveit
Devin Review: AI to Stop Slop | Cognition
As code generation gets easier, code review is the new bottleneck. That's why we're launching a new way to quickly review and understand complex PRs in our latest tool for codebase understanding - augmenting human attention with AI.
How AI Impacts Skill Formation
AI assistance produces significant productivity gains across professional domains, particularly for novice workers. Yet how this assistance affects the development of skills required to effectively supervise AI remains unclear. Novice workers who rely heavily on AI to complete unfamiliar tasks may compromise their own skill acquisition in the process. We conduct randomized experiments to study how developers gained mastery of a new asynchronous programming library with and without the assistance of AI. We find that AI use impairs conceptual understanding, code reading, and debugging abilities, without delivering significant efficiency gains on average. Participants who fully delegated coding tasks showed some productivity improvements, but at the cost of learning the library. We identify six distinct AI interaction patterns, three of which involve cognitive engagement and preserve learning outcomes even when participants receive AI assistance. Our findings suggest that AI-enhanced productivity is not a shortcut to competence and AI assistance should be carefully adopted into workflows to preserve skill formation -- particularly in safety-critical domains.
How AI Impacts Skill Formation
AI assistance produces significant productivity gains across professional domains, particularly for novice workers. Yet how this assistance affects the development of skills required to effectively supervise AI remains unclear. Novice workers who rely heavily on AI to complete unfamiliar tasks may compromise their own skill acquisition in the process. We conduct randomized experiments to study how developers gained mastery of a new asynchronous programming library with and without the assistance of AI. We find that AI use impairs conceptual understanding, code reading, and debugging abilities, without delivering significant efficiency gains on average. Participants who fully delegated coding tasks showed some productivity improvements, but at the cost of learning the library. We identify six distinct AI interaction patterns, three of which involve cognitive engagement and preserve learning outcomes even when participants receive AI assistance. Our findings suggest that AI-enhanced productivity is not a shortcut to competence and AI assistance should be carefully adopted into workflows to preserve skill formation -- particularly in safety-critical domains.
AI, Learned Today
AI, Learned Today is a learning-in-public journal about how to use modern AI through my everyday use. It’s a place to share what I tried, what I noticed, and what I’m learning. Honest field notes from someone exploring the field of rapidly evolving AI tools.
The New SDLC With Vibe Coding
Discover what actually works in AI. Join millions of builders, researchers, and labs evaluating agents, models, and frontier technology through crowdsourced benchmarks, competitions, and hackathons.
