I spent 5 years building a financial planning and optimization tool

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It took me half a decade[0], but I finally landed the original vision for ProjectionLab. It started out as a personal finance simulator, and I always wanted to build an optimization layer on top. This month I did! I've added a tax strategy engine to automatically coordinate Roth conversions, tax-aware withdrawal blending, and gain harvesting, around constraints like targeting a federal tax bracket, preserving ACA subsidies, respecting IRMAA cliffs, or avoiding NIIT. A beam search wraps around this to help find the best strategy for your objectives (maximizing net legacy, minimizing lifetime taxes, etc.) It's been pretty cool to see that in the median case this often saves about $300k in lifetime taxes. If you tried PL years ago in one of my original Show HNs, I'd love to hear what feels different now, or what areas still need more attention. I've incorporated a lot of this community's feedback, e.g. there is now a free tier with basic forecasting that does save your data. And I've added more international depth for folks in Canada, UK, AU, and some other locations. Also noteworthy: - Flexible spending based on portfolio performance to help simulate more realistic spending profiles and boost Chance of Success. - Net legacy estimation, estate planning, and charitable giving (QCDs, DAFs, etc) - Government benefit estimation (US: Social Security, Medicare, ACA subsidies, Canada: CPP, OAS, GIS, etc) - Monte Carlo options like block bootstrap (stitch random blocks of consecutive years to mix eras while keeping year-to-year patterns) - Changelog [1] [0] previous Show HNs: 2021 ( https://news.ycombinator.com/item?id=26969173 ), 2022 ( https://news.ycombinator.com/item?id=31083093 ), 2023 ( https://news.ycombinator.com/item?id=36849502 ) [1] https://projectionlab.com/changelog

Visit product View on Hacker NewsApr 27, 2026

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

89%89% 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 NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

55%55% 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: personal, month, way · Missing: mobile apps, ios, entrepreneurs

47%47% 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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

17%17% 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

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.

Incorrect prediction on native model

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