Agent graphs address a real context problem, but their headline percentages do not share a denominator. A guide to separating architecture, spending, and evidence.
DeepMind argues that agents can shift science’s bottleneck from generating ideas to testing them with data, laboratories and review.
Training data, model weights and outputs are different objects. Rules, licences and litigation concern each one, but no court has set a general ownership rule for model weights.
Tokens are pieces of text a model processes. Understanding them helps anticipate limits and costs, but a counter cannot judge answer quality.
Not every false answer fails in the same way. Separating factual error, invented citation, overreach and acknowledged uncertainty helps verify AI without assigning intent.
"According to an FBI report, pages 39-40" sounds like a verified fact. It isn't, until there's a link right there. With an uncomfortable number from this very newsroom — 44 of its last 50 pieces missing that link — and an academic source on the authority fallacy, this piece teaches the two-minute check that separates a real citation from an appeal to a name.
A new preprint warns that self-consistency and agreement between LLMs are weak, context-dependent confidence signals.
Every summary of OPT-175B's training said the same thing: 992 GPUs, two months. None mentioned the 35 manual restarts at three in the morning. They were sitting in the actual document, under a plainly-named section almost nobody opens. This piece teaches which parts of a paper to read, in what order, and walks the exact route — minutes, not a research career — through the real case that changed the argument of my previous piece.
"How much GPU do you need" is the wrong question. Compute, data and knowledge don't add up — they're three gates in series, and the one almost nobody clears isn't the hardware one. With today's real pricing, a real open model's corpus, and the real logbook from a 175-billion-parameter training run, this piece does the full math — and tells you which gate you're actually standing at.
In 2023, researchers asked ChatGPT to repeat the word 'poem' without stopping, and the model ended up reciting Poe's 'The Raven' word for word. That doesn't prove models store a copy of the internet, but it doesn't prove they never memorize anything either: both claims are true, depending on the case. The four questions that separate a serious lawsuit from an empty headline.
We explain gradient descent, hyperparameters and overfitting, but almost never the thing that actually gets adjusted: the weight. It's a number, and a model is millions of those numbers. That single idea collapses a lot of AI mythology — and with the right sources, you can say precisely how much memorization is real and why "70B" doesn't measure quality.
'Open weights' is not 'open source,' and the difference has real consequences: from user thresholds to territorial exclusions buried in the fine print. Using Llama 4's actual license and the counterexample of OLMo 2, this piece teaches three questions to check for yourself, document in hand, whether an AI model billed as 'open' really is.
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