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Side Reader – Better PDF Chat Experience for ChatGPT and Claude
Hacker NewsSide Reader – Better PDF Chat Experience for ChatGPT and Claude
Side Reader is a Chrome extension that lets you read and interact with PDFs directly on ChatGPT, Claude, and DeepSeek websites. I built Side Reader because I got tired of the constant tab-switching between PDF readers and AI chat - copying text, switching tabs, pasting, repeat. It was driving me crazy. Over time, I added more features like custom highlight buttons, screenshot functionality, and local storage.
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2points
Did not reach leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, chatgpt · Missing: mac, agents, macos85%85% 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, podcasting67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal40%40% predicted probability of success on TrustMRR, 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 released28%28% 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, revenue20%20% 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, cryptocurrency2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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