I built Pensiv, an AI-supported journalling app that grows with you

Hacker News

Hi everyone, I’ve been using ChatGPT to analyze my journal entries and to reflect with. It works great, I really liked it. I’ve managed to gain some good insights about myself and made improvements. However, there are a few problems: - ChatGPT only remembers key facts about you, it has limited memories - ChatGPT couldn't recall the content of conversations you had with it - For individual ChatGPT products, your private conversations might be used to train future models (see: https://help.openai.com/en/articles/5722486-how-your-data-is... ) So, I decided to build my own AI journal system. What started out as a scrappy app on my terminal, eventually turn into a full fledge journalling app. And thus, Pensiv is born. What can Pensiv do for you? - You can journal. - You can reflect with Pensiv AI. - You don't have to repeat yourself. Context is build on the fly for Pensiv AI. - Easily organize and index key people and topics that appear in your journal. I have tried a number of AI-journalling apps, but most of their core experience emphasize on interacting with AI first, journalling second. My vision with Pensiv is to have journalling still be the core of your experience, and having AI to support you for deeper analysis and more insightful reflections. The goal is to have a DeepReserach-like AI Agent that could analyze all your past entries and conversations and give you tailored insights and advice. If this interests you, I’m looking for early beta testers for Pensiv. It’s completely free to use. Sign up here! https://pensiv.me

Visit product View on Hacker NewsApr 20, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, apps · Missing: mac, agents, macos

93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% 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

47%47% 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

26%26% 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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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