I

I built a tool to analyze X/Twitter profiles into PDF reports

Hacker News

I built a tool to analyze X/Twitter profiles into PDF reports

I built a tool called XInsight that analyzes public Twitter profiles and generates a custom PDF report. You just enter the X/Twitter username, the number of tweets to analyze, what you'd like to focus on (e.g., engagement trends, tone, etc.). It processes your request and emails a detailed report that includes insights like the most engaging tweets, posting times, writing style patterns, engagement trends, tone breakdown, and many other useful metrics. It's paid for now, but I'm happy to send a few free reports if anyone here wants to try it. I’d really appreciate any feedback. Are there any features you'd like to see added? Live here: xinsight.me Thanks!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
73%73% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
60%60% 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, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, email · Missing: mac, agents, macos
33%33% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
25%25% 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
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · 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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