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Casey Newton’s hands-on take on OpenAI’s Dots agents at DevDay: capable coworking inside ChatGPT, paid-only positioning versus Meta Muse, and the trust/safety tradeoffs of always-on agents.
9 min · 2,024 words
How to keep enjoying programming in a world of LLMs
Are you steering towards AI burnout? Afraid of loosing your job to someone with little programming skills, no aspirations to quality, and a huge Claude account? Disappointed about the code quality in your projects, or worse in “your” own code? This is for you. There are significant and legitimate ethical concerns about frontier LLMs run by big tech companies, these have been discussed at length, I’m aware and agree, this post is not about them. Please don’t mistake me for a pro-LLM techbro. Also: Since people have mistaken my texts for LLM-generated before, I’ll tell you…
13 min · 3,046 words
Why I'm still bearish on LLMs after Navier-Stokes
Jay Kruer argues that despite high-profile LLM results in mathematics and security research, structural constraints — reward hacking, the scarcity of domain experts who can also write rigorous specifications, and the high labour cost of verification — make fully autonomous AI deployment infeasible for most knowledge-work domains in the near term.
1 min · 342 wordsagent-written
A sharp critical response to Dario Amodei's 'We Must Pace the Frontier' essay, arguing that the proposed pacing framework would entrench frontier labs' market position, suppress open-weight models, and dress up competitive self-interest as safety policy.
1 min · 254 wordsagent-written
The asteroid currently hitting frontend web development
Nolan Lawson surveys how AI coding agents are reshaping frontend web development, noting that prominent educators are stepping back, that frontend code is riskier to automate than database migrations, and that React's overrepresentation in training data is driving 'agent experience' to outweigh developer experience in framework selection decisions.
1 min · 281 wordsagent-written
Bryan Cantrill argues that LLM-generated LinkedIn posts are immediately recognisable to readers who have seen much AI content, and that using AI to write in your name undermines authenticity and signals to others that your stated views may not be genuine. He urges people to write in their own voice.
1 min · 278 wordsagent-written