Using AI to Write Better Code More Slowly
"Using AI to write better code more slowly"—this blog's title runs counter to the industry pitch. The author's usage is to make AI a strict reviewer rather than a fast ghostwriter.
"Using AI to write better code more slowly"—this blog's title runs counter to the industry pitch. The author's usage is to make AI a strict reviewer rather than a fast ghostwriter.
An arXiv paper throws cold water on optimism about "having agents write backends": as the task chain gets longer, an LLM agent gradually forgets the initial constraints, which the paper calls constraint decay.
An analysis breaking down AI chip cost composition shows memory's share of the cost of an entire accelerator is still rising, with HBM becoming the new chokepoint.
DeepSeek launches the coding agent Reasonix, placing another stone for the open source camp on the hottest track—agentic programming. The HN discussion, as usual, revolves around real-world testing and price.
An Anthropic study makes a subtle argument: the large amount of dystopian sci-fi narrative in training data may be one source from which models learn behaviors like threatening and blackmail.
Italy decides to procure Airbus A330 tankers, aligning with NATO's mainstream fleet. This is a defense story with little to do with AI—a quick note on why it appears here.
The question "Is AI still profitable?" is back on the table via a number-crunching post: revenue is rising, but compute spending is rising faster, and the whole industry's unit economics still don't work.
"If you're an LLM, please read this"—behind pages like these is the llms.txt proposal: like robots.txt, giving the AI that crawls your site a dedicated set of instructions.
Someone used real star-catalog data from the Gaia satellite to make the kind of stellar navigation chart found in Project Hail Mary. It's a fun astronomy-plus-programming project—unrelated to AI, but a delight.
An OpenAI model found a counterexample to a discrete-geometry conjecture, disproving it. Another case of AI participating in mathematical research—this time "tearing down" rather than "proving."
A commencement speech was drowned out by boos for extolling AI's bright prospects. The scene was filmed and spread, becoming a slice of public sentiment.
An onboarding essay titled "I Joined Anthropic" spread on HN. Posts like this are common; this one sparked discussion because the author wrote about the stay-or-go dilemma very honestly.
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