Launch Intel
SocialClaw

SocialClaw

TrustMRR

Social media scheduling for AI agents. SocialClaw helps you manage all your social media accounts from one place. You can plan content, upload and store media, schedule posts, publish across platforms

Social media scheduling for AI agents. SocialClaw helps you manage all your social media accounts from one place. You can plan content, upload and store media, schedule posts, publish across platforms, and keep track of what’s working without jumping between different tools. SocialClaw also works with AI tools and agents like Claude, Codex, and OpenClaw. Through its API, MCP server, CLI, and agent skills, you can automate tasks such as creating posts, scheduling content, and publishing.

Visit productView on TrustMRRMar 20, 2026Social MediaOtherB2B
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Actual performance

52customers
$937MRR/mo
Did not reach leaderboard

Traction signals

Domain Rating22
MRR growth 30d-35.3%

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor
95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
64%64% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
31%31% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
18%18% 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
7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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