Bu
BuildingInPublic Page
Hacker NewsBuildingInPublic Page
http://sankalpsinha.com/buildinginpublic Looking for feedback! Thanks in advance guys. Context-- I'm designer who can code. Have made a few SaaS tools in the past in private and spoke about them only when I launched the product or shipped a feature. With buildinginpublic movement, I am starting to document all my work in public and also made this extensive page, explaining the WHY, HOW, and WHAT of building in public. Would love the eye of HN on the page and some honest feedback on why, how, & what I can change/improve.
Share cardActual performance
1points
Did not reach leaderboard
Launch Intel predictions
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting62%62% 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 HuntOn track for Day 1 leaderboard · Strong signals: context, code, plain · Missing: mac, agents, macos59%59% 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 released49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive35%35% 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, personal30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue20%20% 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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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