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An AI tool that filters high-quality info from the noise

Hacker News

An AI tool that filters high-quality info from the noise

AI is bombing us—bombing our attention. The explosion of generative AI has unleashed an unprecedented wave of content creation. Polishing, drafting, illustrating, one-click cross-platform publishing… You’ve probably seen a hundred tools like these already. The result? AI-generated content is flooding social media—and this trend is now irreversible. What does this mean? Not a flourishing of content, but a collapse of quality. Mediocre, low-value content—whether AI- or human-generated—is flooding the information ocean. And no matter how advanced AI gets, it can’t change one harsh reality: In the noise of an information explosion, truly high-quality content is rapidly shrinking in proportion. And we, as consumers of information, still only get a meager 24 hours in a day. We spend the same time, but get less and less value. You’ve heard the famous phrase — Attention is all you need. No Attention is all THEY need Attention is all WE HAVE So what do we do? We fight back. Clearly, we need a system—a mechanism—to shield us from the overwhelming noise. And this mechanism must have a few essential abilities: - Rapidly read and comprehend massive volumes of information - Accurately identify what’s important and high quality - Discover and build meaningful connections across information - Deeply process and restructure key insights into customized, actionable formats Sounds like something you could throw 20 interns at— But a faster, smarter, more scalable solution is obviously AI. AI is a natural-born information-processing engine: - It digests enormous data volumes in seconds - It scores and ranks key content with surgical precision - Hidden relationships across information? To AI, it’s just a matter of vector space distances - And yes—people will use AI to generate noise (you can’t stop that). So we’ll use AI to build the shield. You might ask: isn’t AI-processed information just secondhand content? That depends entirely on how you use it: - If used for filtering: AI helps surface the best first-hand sources, cutting through the clutter - If used for summarizing or synthesis: its unique lens and processing methods often generate brand-new insights—valuable perspectives humans might never notice Which leads us to what I’m building: An AI-powered Information Intelligence Layer. Its job: - Autonomously gather and absorb information - Filter and rank based on real value - Distribute it efficiently and personally The goal: Deliver only the most essential, most relevant information to you each day—so you can spend your time on what really matters. Of course, before building a full-scale intelligent platform, I started with a focused MVP for the crypto and finance domain—and I’ve open-sourced the whole thing: https://github.com/cctv2206/mimir-opensource - It continuously scrapes and digests all crypto/finance data you can imagine - It only pushes the most urgent, most critical updates, straight to the point I call it Mimir—yes, that wise head hanging from Kratos’ belt. I welcome your thoughts and suggestions. In this war against the flood of noise, your support matters.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, efficiently · Missing: supports, reddit linkedin, podcasting
97%97% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, open · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, efficient · Missing: plus, intuitive, reviews
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released
34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto, smart · Missing: web3, chat, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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