Holroyd Technologies
Indie HackersiOS App Performance Audits & Backend Code Reviews
“We find performance issues before your users do” It’s benefit-focused, creates urgency and speaks directly to what indie app founders fear most — user complaints.
Share cardActual performance
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: user, code, plain · Missing: mac, agents, macos77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting72%72% 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: ios, users · Missing: mobile apps, personal, entrepreneurs50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews, users · Missing: plus, platform, intuitive48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: code review, io · Missing: https docs, excited, just released42%42% predicted probability of success on Hacker News, 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, crypto9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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