Sleio – Nonprofit Google Alternative

Hacker News

Based on http://paulgraham.com/ambitious.html I asked myself what would it take to build a Google-like search engine? What would change people's minds about using different search engine than Google and then it hit me out of the blue. I would love to use nonprofit open-source search engine. That's why I built Sleio (http://www.sleio.com/) - a search engine that donates 100% of its profits to causes you love. It's powered by Google at the moment, but I believe it's a good start. I am eager to hear ideas how to make this happen. Your feedback is very appreciated.

Visit product View on Hacker NewsMay 14, 2014

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, using, open · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

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

Correct prediction on native model

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