Search engine for finding the best of anything

Hacker News

Bestlist is a search engine that is focused solely on helping users discover the best of anything. All of our results are non-biased, non-paid, and are dynamically generated via our search algorithm. It’s not perfect, but we’re working extremely hard and are determined to make it ubiquitous in the lives of the everyday internet user. Other Features Voting If you’re feeling strongly about a search results, you can up or downvote the listing to voice your opinion. When you vote, you’ll be asked to state why you voted. This gives other users better insights into the listing. Collections With collections you can easily save and keep track of all of your favorite listings. Collections can be public or made private, and they’re easily edited, reordered, and shared. Submissions If you find that we’re missing a result for a particular query, you can easily submit it as a suggestion. We’ll review it, and publish it if it makes the cut. Current Limitations •It’s currently English only. •We don’t handle searches that start with “Best way..” or “Best settings ..” or “Best route..” very well. •Also, searches for professionals (doctors, etc,) and recipes have issues. I’m sure there are a lot more we’re missing. I'd love for you to try and it and let us know what you think. Thank you, Tyler

Visit product View on Hacker NewsAug 17, 2022

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31points

41comments

Made the leaderboard

Launch Intel predictions

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

72%72% 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: user · Missing: mac, agents, macos

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

60%60% 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 · Strong signals: users · Missing: plus, platform, intuitive

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

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

35%35% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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