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URL to Get an AI Agent for Your GitHub Repo
Hacker NewsURL to Get an AI Agent for Your GitHub Repo
We created a URL similar to https://vscode.dev/github/<organization>/<repo> with a small catch that it creates an AI agent indexed on the repo code + issues. Just use, https://app.commanddash.io/agent?github=your_repo_url . Here is the marketplace of all the agents users have created on Open Source Projects: https://app.commanddash.io/marketplace Add yours?
Share cardActual performance
3points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, user · Missing: mac, macos, cursor89%89% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, io · Missing: https docs, excited, just released39%39% predicted probability of success on Hacker News, 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: users · Missing: mobile apps, ios, personal37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting30%30% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive22%22% 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, 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, crypto4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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