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SaaSHub Experts

Hacker News

SaaSHub Experts

Hey fellow makers! I'd like to introduce SaaSHub Experts here. This is my Nth attempt to organize an active community around promoting software products. I'd describe SaaSHub Experts as an ongoing product tournament. Why a tournament? Because every workday, the community selects the "Top 5 products of the day" (out of 15). Then, the results are aggregated every week by taking the Top 3 products and nominating them for "Product of the Week". And we have Product of the Month & Year similarly. Why experts? Product owners and marketers grow their "Expert Rating" by selecting the Top 5 products. You have to choose the Top products according to the community. The more community-matching your selection is, the more points you get. With that in place, new users start with minimal influence and can only influence the result a little. This resolves the issue when someone invites people to register and vote for them. Who's the top competitor? I think that it is Product Hunt. My goal for SaaSHub Experts is to be the more objective version of Product Hunt. I.e. the best products should be featured instead of the products with the biggest following (as it is on PH). Why would I want to take part? Every expert can set a product on focus to be featured on their profile and on the homepage of SaaSHub (as a nomination). You will have fun (I hope). You could consider it as a game for product makers as well. Please let me know what you think. Let's have fun together - promoting the best on the net. Cheers! - https://www.saashub.com/experts/about

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
70%70% 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, new · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, users · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
31%31% 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
23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas, active · 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 · Strong signals: introduce · 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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