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Inventr – My new startup

Hacker News

Inventr – My new startup

Landing page is > http://inventr.org Submitted in Ask HN first, someone mentioned I should also post here. Hoping that over time the Inventr boards ask more complex questions especially around problem solving and better giveaways. Each Inventr has their own publicly available profile which shows their Inventr status, each brand has their own Inventr page (click on a board to get there). Sharing is built into the platform with the option to share an idea once submitted for as many upvotes as possible. Business model likely to be subscription based version for the enterprise to drive change, problem solving and innovation. We were featured on product hunt in mid December. https://www.producthunt.com/tech/inventr

Visit productView on Hacker NewsJan 17, 2016
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
87%87% 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: model, new · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, 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
42%42% 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: platform · Missing: plus, intuitive, reviews
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
21%21% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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