Cerebro: a librarian for the 463-exabyte-a-day internet

Hacker News

You missed a miracle while you slept. Between midnight and breakfast people uploaded five hundred hours of video every single minute. That is seven hundred and twenty thousand fresh hours in one day, enough footage to keep you watching until 2107 if you pressed play right now. And that is only YouTube. IDC says humanity will push out about 463 exabytes of new data every day in 2025. Stack that on hard drives and the column reaches the Moon and comes halfway back. Insane right? it get's worst. McKinsey finds that knowledge workers already burn a full work-day each week just hunting for information they have created or paid for. Now bring AI into the picture. Europol’s analysts warn that up to ninety percent of everything you read online could be machine-generated by 2026. The models will write, then quote themselves, then train on the quotation. Noise that amplifies itself. Why should you care? Because the next time you need a fact whether to build, vote, or treat a fever you’ll have to dig it out of a an exponential wave of information that is still rising. Yesterday one reliable page could tilt the odds for anyone lucky enough to find it, today the same page is buried under a billion noise or masked by fake data and social proof. If we cannot tell signal from chatter we stop trusting anything. Research stalls, democracy wobbles, and every decision takes longer. Knowledge once tasted like power. Now it tastes like sand. What I am trying I am building Cerebro. Think of it as a librarian, not a factory. It ingests the PDFs, videos, and papers you care about. It verifies statements against originals before they reach your eyeballs. It maps concepts so you see only what changes the decision in front of you. No summaries that hallucinate. No extra content to clog the pipe. Just a shield that separates fact from filler. Version 0.1 is live for dog-fooding and every commit happens in public. Why bother telling you? Because if this resonates then you are exactly the person who can break it, improve it, or tell me it is the wrong fight. Questions for HN Which metric tells you that you have crossed from curiosity into overload? Where do existing curators fail you? If one slice of your reading workflow could be automated today what would you pick? I will be in the comments all day.

Visit product View on Hacker NewsApr 27, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, new · Missing: agents, macos, agent

90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: created, para, ios · Missing: supports, reddit linkedin, podcasting

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: ios, video, way · Missing: mobile apps, personal, entrepreneurs

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat, paid · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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