Sentinel – my second side project

Hacker News

Hey everyone I'd like to show you the new version of one of my side project: Sentinel. It's aimed at product managers or marketing people (or even founders) and let them monitor a page and watch for visual changes. Why? As a product manager I'd love to be warned when one of my competitors changes its homepage or pricing page. The big question: are there people with the same need? The second big question: I have no idea on how much I should charge for this. Please check it out and give me your opinion. Have a great afternoon. https://sentinelapp.com

Visit product View on Hacker NewsMay 30, 2016

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, visual · Missing: mac, agents, macos

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

54%54% predicted probability of success on Indie Hackers, 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

47%47% 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

39%39% 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.

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 · 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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