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Git Web Manager

Hacker News

Git Web Manager

Git Web Manager (GWM) is a self-hosted dashboard for deploying Git repositories to local and remote paths. It manages rollbacks, health checks, preview builds, and automated dependency updates. Key features: - Per‑project deploys + rollbacks - Deployment queue with cron management and queue processing control - Health checks with status badges - Preview builds by commit (great for staging) - Workflows and post-deploy management - Dependency actions (composer/npm) + audit output - Automatic updates when repos change - Security tab for unresolved dependabot issues - User management with forced password change - FTP/SSH Deployment options - Manageable work queue for deployment management It’s open‑source and I’m looking for feedback/testers. Repo: https://github.com/WallabyDesigns/gitmanager Docs (GitHub Pages): https://wallabydesigns.github.io/gitmanager Note: Not affiliated with Git/GitHub.

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Actual performance

3points
1comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: user, open · Missing: mac, agents, macos
91%91% 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 · Missing: supports, reddit linkedin, podcasting
55%55% 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: io · Missing: https docs, excited, just released
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% 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
10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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