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A short reading recommendation: a strong post by Mike Caulfield on how to get more out of LLMs in real-world research. The key idea is simple and applies to ChatGPT, Gemini, and any other tools: first “ground” your query in Wikipedia and its sources, then expand the search to primary and domain-specific publications. This two-step route gives models context, terms, and facts—and noticeably improves answer quality on non-new (non-SOTA) questions. ...

To really understand a concept, you have to “invent” it yourself in some capacity. Understanding doesn’t come from passive content consumption. It is always self-built. It is an active, high-agency, self-directed process of creating and debugging your own mental models. This profound insight, shared by François Chollet, cuts through the illusion of modern “learning” culture. We scroll through tutorials, watch explainer videos, consume podcasts—and mistake familiarity for understanding. The Illusion of Passive Learning The uncomfortable truth: you can watch a hundred videos about riding a bicycle and still fall flat on your face the first time you try. You can read endless articles about programming and still stare blankly at a text editor. Why? Because understanding is not transfer—it’s construction. ...

If you’re still convinced you choose a language “by objective criteria,” check not the documentation but your pulse—it might be your amygdala talking, not the engineer. The author rips the mask off: a language is the single most expensive choice a company makes, yet we heroically hide our ego behind benchmarks and tidy checkmark tables. Hired an expert to “evaluate Rust”? Congratulations: Rust is already chosen—now we’ll wrap it in a two-million-dollar “justification study” so our conscience won’t squeal. And the bill for this “invisible conversation” doesn’t show up on Twitter; it lands in the P&L: velocity drops, the budget burns, the runway shrinks, but the title of the “right CTO” gleams like a fresh conference badge. ...

The latest news about the International Criminal Court’s decision to move away from Microsoft software in favor of European open-source solutions—and the unexpected public reaction to OpenAI’s changes to its policy on medical and legal advice—illustrate one broader trend: society has already become deeply dependent on large technology platforms. Today’s “noise” and the emotional response of ChatGPT users to restrictions on providing medical and legal advice clearly demonstrate that neural-network tools are now embedded in everyday and professional workflows. One could even say that, for many, the absence of instant access to such tools is already painful. But it is far more important to look ahead—to a five-year horizon. ...