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Health Insurance sucks!

Hacker News

Health Insurance sucks!

So I built Health Care Compare! I built a way to compare different plans to estimate your yearly total based on your premium, and other medical visits/costs. Every year it seems like I have to do this all in a spreadsheet, so I thought I would use AI to help make it a web app. All data stays local in your browser—no server, no tracking, only 1 cookie (to not show the disclaimer for a week after you clicked it). You can export/import your data as JSON. There’s also an AI-powered plan import: upload your Summary of Benefits and Coverage (SBC) into an LLM with the provided prompt to convert it to JSON to import. Feel free to audit the calculations and make a pull request with any fixes :)

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
65%65% 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.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, 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
35%35% 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 · Missing: arr, mrr, revenue
20%20% 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.

Correct prediction on native model

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