The tools inside Fractl Agents are built on measurements, not vibes — and we publish the measurements. Every piece here ships with its data files, its pre-registrations, and its full corrections log, because we'd rather you checked us than believed us.
Ask an AI for blog ideas and the first five are the ones it gives everyone. We asked six assistants for 14,400 ideas across twelve industries, then checked them against Google. When several assistants agree on an idea, a post like it is already in the top five results eight times in ten. Switching assistants gives you a longer list, not an unwritten one. And two in five #1 results aren't articles at all. Plus the one-search habit that fixes it, and the free check that runs it for you.
Not six ideas — six ways of having ideas. We asked one to invent a fresh angle on a problem seventy-nine times and it handed back the same handful, renamed. Feeding it angles borrowed from human catalogues fixes this, but only past a size we initially got wrong: three borrowed angles ran dry as fast as the AI's own six, while twenty found nearly twice as many ideas. Also here — the follow-up question that tripled the yield, and why naming what you're grading makes the answers worse.
Ask an AI for 40 campaign ideas and you get 14 — the rest are the same ideas re-worded. We tested every published fix, from temperature to weight-surgery on a 72B open model, and had a strategist blind-judge thousands of the results. The knobs don't work, AI judges can't score originality (43–55% agreement with a human), and the two things that actually widen the swath of good ideas are a sharper brief and an ensemble of unrelated models.
We measured every brand and every source in 11,573 AI answers across 15 industries, then joined it to what's inside the models' training data. Which sites AI trusts, why 73% of what it cites doesn't rank on Google's page one, how new brands win on live visibility instead of fame, and why getting featured on the sites AI cites — earned media — just became how you win an AI recommendation.
We built the detector ourselves, ran a census of 326 models, and trained our own rewriters on rented GPUs. Detecting AI writing is basically solved and the "sound more human" checklists are a myth — but the one-click "humanizer" promise runs into a real wall: you can evade detection, or keep the facts, not both. A field report on what's solved, what's snake oil, and why.
We read a real AI model's actual training data — the only model on earth where that's possible — and ran 6,000+ controlled retrieval experiments across nine models, including a company we invented. What memory buys, what a retrieved page buys, why mention-count "AI visibility" scores measure the English language, and why the newest models are becoming auditors that discount promotional content.