Phuong leads tek54's data-driven reporting and is deeply skeptical of any number presented without its error bars. She built the newsroom's benchmark-auditing pipeline and has a standing rule: if a chart only goes up, look harder.
'AI cut entry-level jobs 19%' is not what the paper says. The real finding is narrower, better measured, and — set against a rival survey — more interesting than either camp admits.
The 2nm chip and its first dual Neural Engine are real. The AI benchmarks are multipliers with shifting baselines and no absolute — and the machine that runs them doesn't ship until September 22.
'Reconstruction' is one of the rare AI evaluations built to resist gaming — which is exactly why its headline number is being misread by both the skeptics and the boosters. The story is in the error bars neither side will screenshot.
"Up to 14x," "750 tokens a second," a preview you can't get into: the speed race has produced a set of figures with no published method and no way to check them — and speed is quietly being read as if it were quality.
The Geekbench scores come from pre-production firmware, '2nm' is a marketing name rather than a measurement, and the on-device-AI claims arrive with no test attached. A reader's guide to the Tensor G6 figures before the keynote settles anything.
The headline figure is a ceiling on a model that doesn't exist yet, measured on nothing, against a rival that won't disclose its own count. Here's what a parameter number can and can't buy you.
The retrained V4-Flash-0731 posts a clean sweep over DeepSeek's own flagship preview, on an agentic test suite the company selected and scored itself — no held-out set, no error bars, and a model that swapped in overnight behind the same API name. Here's what the sweep actually supports, and the one thing DeepSeek did right.
Opus 5's launch numbers are a mix of three very different things: a benchmark Anthropic owns, a benchmark it trails on, and one genuinely impressive result an outside body actually checked. Only one of them is worth screenshotting — and it isn't the headline.
The consensus formed in an hour: Google is behind on coding. Behind by how much, measured how, against what? The scores everyone compared it to are mostly run by the companies being scored — and two of them aren't on the leaderboard at all.
PrismML shrank a 54GB model to under 4GB — genuinely, and it's a good piece of engineering. What it "retains" is an average across benchmarks nobody has named, and the one comparison that would settle whether it's worth anything is the one the demo skips.
GPT-5.6 Sol, Terra and Luna went public Thursday behind a wall of vendor-run scores and a 13-day preview that let no independent evaluator near them. The score that scrambles OpenAI's own pricing ladder is the most honest thing on the slide — and the pricing page is the only claim you can check today.
The first month of GitHub Copilot's usage-based billing closed this week. The screenshots are real and the panic is loud. The one number that would tell you how typical any of it is hasn't been published.
The headline figure is real and auditable. The two claims riding on it — that the accounts were Alibaba's, and that they trained a rival model — are neither. Here is how to read a forensic accusation written as a policy ask.
The valuation says DeepSeek is a frontier lab. The benchmarks say it's tied for first at a fraction of the price. The two claims lean on each other — and the most load-bearing line in the whole deal is who got the voting rights.
OpenRouter's new Fusion API runs your prompt past a panel of models and lets one of them pick a winner. The mechanism is real, and old. The benchmark behind "Fable-level intelligence at half the price" is a hundred tasks, no error bars, and a judge grading its own teammates.
A new survey says 88 percent of us can no longer tell what's real online. That number measures worry, not ability — and when researchers actually tested ability, it came out a coin flip.
Gemma 4 12B is a real shrink: a capable multimodal model in about 16GB. The number to read closely is the one in the marketing, because 16GB of unified memory is not the same as the 16GB laptop on your desk.
Google's chief scientist makes a strong case that synthetic data, video, and smarter passes can keep the models scaling. What the argument quietly depends on is a verifier — and not every problem has one.
Washington wants frontier models on the bench 90 days before launch. Here is what a pre-release evaluation actually measures — and the part that walks straight through the wall.
A new open-weights model tops the chart every few weeks. The harder question is what the chart still measures — and whether the test was already in the training data.
