by steve-faulkner
The abstraction layers we built to manage human cognitive limits may not survive AI — vinext proves a single engineer can reimplement an entire framework in a week by directing AI through 800+ sessions, and the real bottleneck was taste, not typing.
by lex-fridman
Fun beats funding — OpenClaw's 180K GitHub stars prove that personality, self-modifying software, and a lobster mascot can outcompete every well-funded AI agent startup that takes itself too seriously.
by every-inc
Engineering work should make future work easier, not harder—this plugin inverts the technical debt spiral by investing 80% in planning and review so each cycle compounds reusable patterns.
by kieran-klaassen, peter-yang
The simplest form of compound engineering is telling Claude to update CLAUDE.md after every mistake — but the full four-step loop (Plan, Work, Assess, Compound) with specialized sub-agents turns each development cycle into a training run for the next.
by alexander-opalic
Claude Code's agent teams upgrade the subagent workflow from star topology to mesh — agents can now message each other, coordinate through shared task lists, and collaborate in real time. Three real sessions show the patterns in action.
by tom-crawshaw
Claude Code's native agent teams replace single-agent sequential work with a lead agent that delegates to parallel teammates, each with their own context window and inter-agent messaging.
by gergely-orosz
Effective agentic coding requires closing the loop—giving AI agents the ability to verify their own work through tests and tooling—while developers shift from writing code to orchestrating parallel agents and designing systems for verifiability.
by jeffrey-way
Jeffrey Way declares he's done lamenting AI's disruption to coding—after experiencing layoffs at Laracasts due to AI, he's found more joy programming than ever by embracing agents as pair programmers while maintaining code ownership.
by ryan-carson, geoffrey-huntley
Ralph ships code while you sleep—a bash loop that pipes prompts into your AI agent, picks tasks from a JSON backlog, runs tests, commits passing code, and repeats until done.
by peter-steinberger
Output speed now bottlenecks on inference time, not developer skill—the game shifts from writing code to orchestrating agents, selecting stacks for AI friendliness, and maintaining project documentation that persists across sessions.