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AI-Powered Encyclopedia – Beautiful TLDR Articles

Hacker News

AI-Powered Encyclopedia – Beautiful TLDR Articles

While summarizing videos (I'm the Eightify founder), I've been secretly working on an AI-generated encyclopedia, and here's why. I found myself thinking a lot about AI and content. Now we have knowledge (think Wikipedia) and intelligence (LLMs). They will both significantly improve soon. What we lack is *a knowledge interface for humans*. What I don't like about current interfaces: - ChatGPT: to get an answer how I want it, I need to craft a perfect prompt - Google: need to spend time avoiding affiliates and unreliable sources - Wiki: great, but boring to read, too much information What I love is Kurzgesagt: 1. Concise 2. Visually appealing (dopamine-inducing, as I call it) 3. Fact-checked, so I can trust it 4. Up-to-date I'd love a 5-minute Kurzgesagt video for every question I have, like "How do I hire a marketer?" or "Is it bad to eat bananas every day?" (this question was on my mind the other day). So, I made an MVP — trying to generate Kurzgesagt-like content for any prompt I have. My current MVP nails the first two principles (concise & beautiful). The other two are achievable, but it'll take longer. These are the articles I like most: - https://explore.eightify.app/Universe - https://explore.eightify.app/Black-Holes - https://explore.eightify.app/Psychedelics - https://explore.eightify.app/Yoga Also, you can order your own article. I am now starting the generation machine, so please complete this form if you want one: https://forms.gle/UkjRJCTUA8Fay4Vb7 Before I go any further, I'd love to hear your thoughts: - Do you like the idea? - Would you find it helpful? - What do you think should be my next step and why?

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
82%82% 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: mac, google, visual · Missing: agents, macos, agent
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: video, google · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface, soon · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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 · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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