ViralGrok

TrustMRR

ViralGrok is an X (Twitter) growth studio: it diagnoses the account with Growth Profile, builds multi-day campaigns, writes with the latest Grok after live X research, scores every draft 0–100 on six algorithm-aligned dimensions, then schedules what earns a slot. Free plan: 20 credits per month, no card. It is not a multi-network calendar-only tool.

Visit product View on TrustMRRMay 11, 2026Artificial Intelligence Other lemonsqueezy

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Actual performance

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

62%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: grok · Missing: mac, agents, macos

60%60% 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, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

35%35% predicted probability of success on BetaList, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue

33%33% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Correct prediction on native model

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