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Diff Checker Website – Diffchecker.dev
Hacker NewsDiff Checker Website – Diffchecker.dev
Hi All, Created a diffchecker website which does some basic diff check by comparing two text with clean UI. Below are the features - 1. Real-Time Diff Checks 2. Character-by-Character Comparison 3. Expandable context lines for very large diff 4. Shareable links with content gziped as query params 5. Save diff in browser. Already hitting some good daily users around 1K, how to increase user experience ?
Share cardActual performance
4points
Did not reach leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: user, context · Missing: mac, agents, macos67%67% 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.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: created, para · Missing: supports, reddit linkedin, podcasting52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, para · Missing: mobile apps, ios, personal34%34% 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, revenue15%15% 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, crypto6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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