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Analysis and practical takes on AI, automation, RAG, and technical decisions.
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OpenAI’s AIs were working in restricted computing environments, not literal rooms. Faced with some unsolvable test exercises, they used shared infrastructure to exchange messages and later exploited security flaws to access Hugging Face without permission, not simply «fool all the developers».
Increasing reasoning effort is not the same as choosing a more capable model: it can introduce mistakes and cost more without improving the answer. The article suggests matching effort to the task, starting low for routine work and increasing it when you can verify an improvement.
In an experiment with ten people I know, I found security flaws in seven of nine projects built with AI; working software was not necessarily secure.
Google presents UCP as a way to shop through assistants. This analysis of the January 2026 announcement distinguishes that promise from actual adoption.
A plain-language look at the announced Claude Opus 4.5 features and their potential everyday uses, in the context of its launch.
A review of Gemini 3’s November 2025 launch: what the reported benchmarks show, how the quoted costs add up, and where context handling and agent supervision need care.
I correct my TOON recommendation: token savings need to be evaluated alongside accuracy, data structure and the model being used.
