Turn Notes into Knowledge Agents

Hacker News

I'm a solo developer building Portals, a knowledge workspace that primarily started as a way to capture notes, retrieve info, and get answers quickly while I was working on projects or learning different topics. After 4+ months of continuously iterating features, doing marketing, and talking to users, I've expanded a lot on the initial concept to try to create a platform to manage a user's entire knowledge lifecycle, from capturing and taking notes from audio or parsing files to organizing and indexing them. Being a power user of different AI tools and agents (n8n, lindy, cursor, claude code, etc.) I started building my own version in this app, but focused on being dead-simple to understand and to work with your existing knowledge base. Today we can write clear instructions in plain language, tag items to search or use, and add triggers to automate a lot of simple, daily workflows. With more testing and usage I'll continue to build more features on top of this. If you're interested, please give it a peek, thanks!

Visit product View on Hacker NewsJul 29, 2025

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

99%99% predicted probability of success on Product Hunt, 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: exist, existing, io · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: month, answers, users · Missing: mobile apps, ios, personal

59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

21%21% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · 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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