Tessaract
Indie HackersAn interview management tool for intra network
After observing the chaos created by immature ways of records of keeping, I decided to make one myself. This helped in answering a lot of questions and bit by bit, adding solutions every now and then.
Share cardActual performance
1followers
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released55%55% predicted probability of success on Hacker News, 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting50%50% predicted probability of success on Indie Hackers, 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: way · Missing: mobile apps, ios, personal50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos44%44% 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, intuitive32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue13%13% 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, crypto2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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