FlowStateOS Companion – experiment, modeling human life forces with GPT

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Hi HN, I built FlowStateOS Companion as an experiment in treating human life as a system with measurable forces. Instead of focusing on decisions or optimization, it works with a small set of core life forces (energy, meaning, freedom, connection, etc.) and helps users: observe imbalances evaluate drift or overload reflect on how daily behavior affects system health over time The goal isn’t advice or prescriptions, but making internal state legible the way an operating system exposes resource usage. It’s intentionally non-performative and slow. No motivation layer, no scoring, no productivity framing. I’m curious whether this kind of systems-level self-diagnostics is useful, or whether it collapses under real life complexity. Link: https://yusufshunan.com/gpt Feedback welcome, especially from people interested in systems modeling, human factors, or reflective tooling.

Visit product View on Hacker NewsDec 30, 2025

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TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released

30%30% 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 · Strong signals: real life · 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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