Libredesk – Open-source customer support desk. Single binary app

Hacker News

Libredesk is a 100% free and open-source customer support desk, the backend is written in Go and the frontend is written in Vue JS with ShadnCN for UI components. Unlike many "open-core" alternatives that lock essential features behind enterprise plans, Libredesk is fully open-source and plans to always stay this way. I built this because I wanted a truly open and self-hosted alternative to platforms like Chatwoot, Intercom, and Zendesk. It's in alpha right now, but there’s a working demo available. Would love feedback, suggestions, and thoughts. GitHub: https://github.com/abhinavxd/libredesk Demo: https://demo.libredesk.io

Visit product View on Hacker NewsFeb 24, 2025

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

89%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 NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

69%69% predicted probability of success on Hacker News, 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.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews

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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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