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A Notion-Chrome Extension for categorizing and synthesizing knowledge

Hacker News

A Notion-Chrome Extension for categorizing and synthesizing knowledge

ResearchPRO is Notion-Chrome Extension that allows you to do two things: (1) Create and categorize web-highlights on any website or pdf and sync them in real-time into Notion. With buckets, and the SHIFT SHIFT shortcut, you can do this all on the fly. Defining a new bucket or category, creates a toggleable lists of grouped highlights in the Notion page of your choosing, which allows you to keep track of ideas and references across different articles. (2) Export bibliographies for a given research “topic.” Of course this just allows you to bucket links related to a given topic, if you need to go back later and remember them. As an analyst way back when, I needed to draft a lot of market research documents. The first big part of that job was reading and extracting information from a variety of sources and text (sometimes just copy and paste), which I would pull into a document and later synthesize into something distributable. Just trying to keep track of snippets of text that I wanted to reference later and my sources was a massive manual pain. As an avid reader and internet explorer, I had the same problem. I was constantly coming across articles that had some great idea which I wanted to preserve and connect to other ideas I was reading elsewhere - but I couldn’t find a great note-taking and web-annotating engine for this type of on-the-fly referencing. And certainly not one for Notion, which is my document editor of choice. So I built it myself. Partly, its an ode to the pain points above and partly to I should say I was influenced heavily by the memex ideas and man-computer symbiosis that a lot of the early computer pioneers discussed. This is certainly not that idea in its entirety - but it is something in a similar vain. I wanted a way to capture and categorize and synthesize the knowledge extraction process, and a second-brain repository to do it in, that I could reference back later. I use it now as a second brain for ideas and things I come across. I hope you all love it, and absolutely would LOVE honest feedback. It is behind a paywall, because honestly I wanted to test my ability to make something that people pay for. There is a 14 day free trial, and its only 4/month which is one good cup of coffee. However, if people really want to use it and engage here and upvote this because they’re interested - and the free trial is not enough time - I’m happy to pass in some sort of coupon code for anyone who reaches out directly. The demo and landing page are here - [ https://www.researchpro.club/](https://www.researchpro.club/... which add a good visual reference to the product. Chrome extension is linked.

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Product HuntOn track for Day 1 leaderboard · Strong signals: computer, new, visual · Missing: mac, agents, macos
87%87% 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
52%52% 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: ios, month, way · Missing: mobile apps, personal, entrepreneurs
41%41% 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
33%33% 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
13%13% 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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