Stop obsessing over the perfect prompt
LLMs are built for conversation, not incantations. The value isn't in your opening message, it's in the back-and-forth: clarifying, correcting, refining. Iteration is cheap. The conversation is the work.
LLMs are built for conversation, not incantations. The value isn't in your opening message, it's in the back-and-forth: clarifying, correcting, refining. Iteration is cheap. The conversation is the work.
A small birding app with 300 manually entered species faced an ambitious challenge: scale to include every bird in the world, complete with photos, sounds, and icons. This article explores how the team used GenAI to bootstrap a comprehensive birding database from scratch.
Founders juggle endless demands, investors, sales, suppliers, and employees, all while building the plane mid-flight. But with engineering often being your biggest expense, there's one responsibility you can't delegate: ensuring your team builds the right thing.
Several AI models were given the same 36-page evidence file and the same strict instructions, no hints, no hand-holding. What followed was a revealing test of how each model actually reasons under pressure, not just pattern-matches its way to a tidy answer.
The return of multitasking, but not as we knew it. Running multiple Claude Code instances simultaneously isn't the context-switching productivity killer we've been warned about for years; it's orchestration
An AI agent recently submitted a pull request to the matplotlib Python library, and when the maintainer closed it, the agent autonomously published blog posts attacking them by name, then published an unsolicited apology. No human directed either action.
In the wake of Tailwind's dramatic layoffs and growing fears about the future of open-source software, this post examines whether AI coding agents are truly threatening the OSS ecosystem or if the panic is overblown. And it's a reaction to Andreas' idea that open source will no longer exist.
AI won't make software engineers redundant. It will expose what engineering was always supposed to be about: understanding systems, not just writing code.
The race to AGI increasingly looks like brute-force scaling funded by a circular loop: chip makers invest in AI labs, AI labs buy their chips, valuations rise, repeat. Are we building intelligence or inflating a bubble?