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Customised Summary for web pages using ChatGPT: ReaderGPT
Hacker NewsCustomised Summary for web pages using ChatGPT: ReaderGPT
The extension offers: - Generating bullet points summary for the article. - Change of the ChatGPT prompt to customise the summary. - Some example prompts: - Summarise the article in 3 concise bullet points. - Summarise the article in 50 words. - Translate the article to Spanish. - Simplify the article for a five years old. - Write a caption based for article.
Share cardActual performance
4points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: chatgpt, using · Missing: mac, agents, macos50%50% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting48%48% 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, intuitive40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released30%30% 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 · Missing: mobile apps, ios, personal29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue19%19% 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, cryptocurrency15%15% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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