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Extension for searching and exploring your full browsing history

Hacker News

Extension for searching and exploring your full browsing history

The issue with the standard Chrome browsing history is that it only stores page titles, which often doesn't help when trying to find specific text. For example, your Chrome history won't show anything for "rsync" if the page title for https://www.reddit.com/r/DataHoarder/comments/10r5z6g/how_to... is "How to copy data for...". Google search is great but only covers public internet content. I often lose track of work documents, social media posts, emails, etc. that Google can't index. Initially, I tried integrating with the search API's of different platforms like Notion, GSuite, Slack, etc., but it only works for a limited set of sites and requires slow OAuth for each user on each platform. Inspired by Fullstory and session replay analytics, I took a different approach. Instead of using the search API of each site, I built an index of the text seen client-side. This, in addition to the full session replay built into the extension, allows you to search and explore full browsing sessions even if the page was updated over time. Getting these data into users hands feels important to me, especially since most sites already use some form of session replay (like Fullstory) for analytics. I built an early prototype which is free for personal use, and I plan on monetizing a teams version for searching across a company's documents down the line. Check out the demo on the homepage, or sign up and install the extension to try it out on your own history. Would love to hear everyones feedback!

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, google, user · Missing: mac, agents, macos
89%89% 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 · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, google, users · Missing: mobile apps, ios, entrepreneurs
49%49% 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
44%44% 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
35%35% 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
18%18% 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.

Correct prediction on native model

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