WhenItHappens–family resource after traumatic death

Hacker News

I spent 15 years doing biohazard remediation — cleaning up after suicides, homicides, and unattended deaths. Watching families navigate the aftermath with zero guidance, while contractors and estate attorneys circled, pushed me to build this. WhenItHappens gives families practical, free guidance: what to do in the first 24 hours, insurance coverage basics, avoiding predatory cleanup bids, probate/property steps, and grief support resources. Built with static HTML/CSS on Netlify, Twilio for 24/7 intake, and Stripe for directory subscriptions. I’d love feedback on both usefulness and clarity from the HN community.

Visit product View on Hacker NewsFeb 27, 2026

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

55%55% 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

31%31% 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.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: stripe · Missing: mac, agents, macos

24%24% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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