Livecycle – Get visual feedback, in context, on every pull request

Hacker News

Hi HN! This is Assaf, Matan, and Yshay from https://livecycle.io/ Livecycle enables dev teams to collaborate and comment in context, on top of any preview environment. Using Livecycle, developers get clear feedback earlier in the release cycle leading to higher-quality products, a faster release cadence, and fewer context switches and misunderstandings. Livecycle builds and pushes a dev-like environment for every branch in your repo (or, if you prefer, you can bring your own environments). Any containerized application will work, and support for multiple containers via docker-compose is coming soon. You get a unique, shareable link for every branch, which automatically updates for every commit pushed to that branch. Each link contains not only your deployed environment but also includes: - A dashboard to view and manage all of your environments and users - Collaboration features - create screenshots, record audio/video clips, suggest CSS/content changes, and leave comments with rich text and internal threads - Integration with Jira/Linear (view tickets associated with a PR or create new tickets from comments users left on the environment) - Integration with GitHub/GitLab - view your build status in the PR/MR (with a link to the environment), comments left on Livecycle will be synced to PR/MR comments so that devs can easily gather feedback from both devs and other stakeholders in one place - Even more stuff: Slack integration, integrated network, and console logs, etc… We’re thrilled to see a wide variety of teams already benefitting from Livecycle - large companies, startups, freelance developers, dev shops, and more. And we invite you to check out how Livecycle can bring value to you and your team. And please let us know if you have any comments or questions :-)

Visit product View on Hacker NewsMar 9, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, user, dock · Missing: mac, agents, macos

96%96% 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

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

AppSumoStrong fit for a featured deal · Strong signals: soon, users · Missing: plus, platform, intuitive

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

50%50% 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: video, users · Missing: mobile apps, ios, personal

34%34% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio, collaborate · Missing: web3, chat, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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