How to build an AI agent: the loop and four things that break it
The loop takes twenty lines. Error compounding, absent memory, no stopping condition and excess permissions are where the work actually is.
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Author: Rundowns AI Desk
Why calling it a hallucination makes AI errors harder to fix
Hallucination implies a malfunction. Correct and incorrect output come from the same process, and the vocabulary decides what people trust.
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How much an AI feature costs to run: a worked example
Output tokens cost up to five times input. A support assistant at 10,000 conversations a day runs $2,700 or $9,000 a month depending only on model choice.
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How to detect AI-generated text, and why most detectors fail
Classifiers measure how ordinary writing looks, which is not the same as who wrote it. Watermarking is better and still proves processing, not authorship.
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The case against putting AI in everything
Latency, reliability, interruption and the cost of checking. Most AI features trade a function that always works for one that usually does.
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The EU AI Act explained: what it requires and who it reaches
Risk tiers, not technology tiers. The same model can sit in two different categories depending on the decision it participates in.
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Every software company says it does AI now. Four questions that check
Repositioning and real change look identical in a press release. Pricing, delivery and attributed revenue separate them.
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What an AI valuation actually means, and what it hides
A $190B valuation is the price of a 2.6% slice extrapolated across everything that did not trade. Run-rate, preferences and compute commitments are all invisible in it.
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Reasoning models explained: what thinking out loud actually buys
A transformer does fixed computation per token, so extra tokens are the only way to spend more effort. That constraint explains the gains and the bill.
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How AI companies make money, and which model is failing
Five business models with wildly different economics. Selling compute works, selling implementation works, and selling tokens is squeezed from both sides.
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Who keeps the money in the AI stack: chips, models or apps
Silicon, infrastructure, models and applications have completely different economics. Margin concentrates where substitution is hardest, which is not where the attention is.
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What it really costs to train a frontier model
The published figure is the final run. It leaves out failed runs, research compute, data licensing, annotation and salaries, which together usually cost more.
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