Ev

Events for tech people slightly reimagined

Hacker News

Events for tech people slightly reimagined

We created Eventil [1] because we wanted to automate tedious and repetitive tasks related to tech events organisation. The tool was built at the occasion of PolyConf 16 [2]. Eventil helps you organise a tech event: a tech conference, a company tech talk session or a workshop. It seems easy at first, the hard part though is assembling all needed parts together. And that’s what Eventil aims to do radically better. We will figure out all the elements for you: finding a venue, providing catering options, helping select gadgets or building an agenda etc. It’s difficult and time consuming to integrate dedicated 3rd party solutions for each of these problems separately: a possible solution should be simple and easy. It should nicely unify the whole spectrum of problems. We wanted to share the idea with fellow Hacker News readers and ask for your feedback. [1]: https://eventil.com/ [2]: http://polyconf.com/

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Actual performance

5points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, para · Missing: supports, reddit linkedin, podcasting
71%71% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released
44%44% 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 · Strong signals: para · Missing: mobile apps, ios, personal
41%41% 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
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, tasks · Missing: mac, agents, macos
16%16% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
10%10% 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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