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LinkLens – Document and link tracking in one dashboard
Hacker NewsLinkLens – Document and link tracking in one dashboard
I built LinkLens because I was paying $80/month for DocSend + Bitly separately. It tracks: - Documents: who viewed, which pages, how long - Links: clicks, location, device, UTM params Tech stack: Next.js, Supabase, Cloudflare R2 Free tier available. Would love feedback on the product and landing page.
Share cardActual performance
1points
Did not reach leaderboard
Launch Intel predictions
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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting68%68% 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.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive43%43% 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 released35%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.
TrustMRRLess likely to generate early MRR · Strong signals: month, para · Missing: mobile apps, ios, personal26%26% 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 · Missing: web3, chat, crypto3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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