Feed

The Architecture Overview isn't really architecture. It's "what would I want to know if I had amnesia?" The Technical Considerations aren't really instructions. They're "what would frustrate me if we had to repeat it?" The Workflow Process isn't really process. It's "what patterns emerged that I don't want to lose?" The Story Breakdown isn't really planning. It's "how do I make progress when everything resets?" Maybe that's all any documentation is. Messages to future confused versions of ourselves.

the winners will be teams building constrained, domain-specific tools that use AI for the hard parts while maintaining human control or strict boundaries over critical decisions. Think less "autonomous everything" and more "extremely capable assistants with clear boundaries."

This article made me laugh... a lot.

I found out about my death the way everybody finds out everything: from Google. What happened was, I Googled my name ("Dave Barry") and what popped up was something called “Google AI Overview.” This is a summary of the search results created by Artificial Intelligence, the revolutionary world-changing computer tool that has made it possible for college students to cheat more efficiently than ever before.

The gap between social acceptance and expert acceptance of delegating a given task to AI is a point of negotiation that will occur more often, in more domains over the coming years. Watch these points of friction to better understand the distribution of AI. First as a sign that AI is performing a task sufficiently against expert standards. And second, as a sign that either regulation will arrive or cultural innovations are needed to enable the technical ones.

Through our experiments, we demonstrate that LLMs do not maintain consistent performance across input lengths. Even on tasks as simple as non-lexical retrieval or text replication, we see increasing non-uniformity in performance as input length grows.

Our results highlight the need for more rigorous long-context evaluation beyond current benchmarks, as well as the importance of context engineering. Whether relevant information is present in a model’s context is not all that matters; what matters more is how that information is presented. We demonstrate that even the most capable models are sensitive to this, making effective context engineering essential for reliable performance.

Reachy Mini is an expressive, open-source robot designed for human-robot interaction, creative coding, and AI experimentation. Fully programmable in Python (and soon JavaScript, Scratch) and priced from $299, it's your gateway into robotics AI: fun, customizable, and ready to be part of your next coding project. Whether you're an AI developer, hacker, researcher, teacher, robot enthusiast, or just coding with your kids on the weekend, Reachy Mini lets you develop, test, deploy, and share real-world AI applications from your desk, using the latest AI models!

There is so much in this one article that is amazing that I am struggling to pick anyone thing so I'll just quote the first part. Read the whole article you won't be disappointed.

Why this matters – scalable authoritarianism: One of the things that makes authoritarianism expensive is the overhead that comes from building out and running a large-scale police state. One of the things AI does is make it much, much cheaper to do large-scale surveillance. Datasets like CrowdTrack are a symptom of the way AI is making it cheaper and easier to do surveillance that the dictators of the 20th century would have fantasized about but always been unable to fully fund. “Our dataset can be used for tasks like visual grounding, captioning, and appearance feature extraction,” the researchers write.

But, with these benefits comes a potential offsetting cost to all of us. For example, there are millions of people across the globe that comprise the staff of call centers and customer support. What becomes of them when an AI can answer nearly all questions with zero dollars in salary for human employees? Is the world prepared for this level of potential unemployment? Where are governmental and enterprise level programs for upskilling and retraining to keep these individuals from being left behind?

A lot of developers are feeling scared and hopeless about the changes being wrought by all this. Yes, AI is being used as an excuse by executives to lay people off and pad their margins. Yes, how foundation models were trained was unethical and probably also illegal. Yes, hustle bros are running around making bullshit claims. Yes, almost every party involved has a reason to make exaggerated claims about AI. All of that can be true, and it still doesn't matter. Your job as you knew it is gone.

Get serious about learning and using these new tools. You will, like me, recoil at first. You will find, if you haven't already, that all these fancy AI tools are really bad at replacing you. That they fuck up constantly. Your new job starts by figuring out how to harness their capabilities anyway. You will gradually learn how to extract something that approximates how you would have done it yourself. Once you get over that hump, the job becomes figuring out how to scale it up. Three weeks ago I was a Cursor skeptic. Today, I'm utterly exhausted working with Claude Code, because I can't write new requirements fast enough to keep up with parallel workers across multiple worktrees.

AI doesn't damage our brains, but unthinking use can damage our thinking. What's at stake isn't our neurons but our habits of mind. There is plenty of work worth automating or replacing with AI (we rarely mourn the math we do with calculators), but also a lot of work where our thinking is important. For these problems, the research gives us a clear answer. If you want to keep the human part of your work: think first, write first, meet first.