OpenHands' GitHub Action

Hacker News

Just created an open sourced Github Action and published on Marketplace: "xinbenlv/openhands-action": This GitHub Action brings the power of OpenHands' AI automation directly into your GitHub workflows. Now you can execute complex tasks using natural language prompts right in your CI/CD pipelines! Check it out here: https://github.com/xinbenlv/openhands-action Example usage: ```yaml - name: Execute OpenHands Task uses: xinbenlv/openhands-action@v1 with: prompt: "Your natural language task description here" llm_api_key: ${{ secrets.LLM_API_KEY }} ``` Would love to hear your thoughts and see what creative ways you'll use this in your workflows! Let me know if you have any questions or need help getting started. :blush:

Visit product View on Hacker NewsApr 18, 2025

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2points

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Product HuntOn track for Day 1 leaderboard · Strong signals: tasks, using, open · Missing: mac, agents, macos

80%80% 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.

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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% 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: way · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

25%25% 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, revenue

12%12% 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, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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