Ca

Cahier – A knowledge base with native support for research

Hacker News

Cahier – A knowledge base with native support for research

I'm happy to be on Hacker News to present my most recent project. Cahier is a personal knowledge management system created to support out of the box the research workflow. It allows you to both store and consume study documents (PDFs, web pages, etc.), manage the annotations from those documents, and produce written content based on them. It goes further than existing reference managers because we chose to make the annotation management a part of the application, so you can organize and centralize highlights in notes and special document elements. It's a local-first, native application for Windows and macOS, created to be a research companion for serious readers. Here's a more detailed method that uses the app: https://felipefarinon.com/articles/a-method-for-doing-resear...

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Actual performance

2points
4comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
69%69% 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: personal · Missing: mobile apps, ios, entrepreneurs
61%61% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, hacker news · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, new · Missing: agents, agent, cursor
55%55% 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
40%40% 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
12%12% 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.

Incorrect prediction on native model

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