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TripAdvisor for sustainable companies

Hacker News

TripAdvisor for sustainable companies

Hello everyone, 1 year ago I was doing MA-thesis about greenwash when I discovered that there’s no place on the web to check which stock companies are (un)sustainable. Now there is. It’s called Ekofy www.ekofy.com Idea is to help everyone choose the most sustainable brands by crowdsourcing both positive and negative user statements into one global ranking list. Every statement can be either ‘agreed’ or ‘argumented’ which affects the ‘leaf’ rating. The platform is build with Ruby, Heroku (with HireFire managing dynos) and AWS for static files Any feedback would be greatly appreciated.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
64%64% 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
56%56% 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 · Missing: plus, intuitive, reviews
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released
51%51% 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 · Missing: mobile apps, ios, personal
34%34% 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
17%17% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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