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Visibly – More Collaborative and Efficient Code Reviews on GitHub

Hacker News

Visibly – More Collaborative and Efficient Code Reviews on GitHub

Hey HN! I’m Nick, the founder of Visibly ( https://visibly.dev ). Visibly is a GitHub App and Chrome extension that makes code review more efficient, collaborative, and visible by enabling you to: - See review comment thread statuses so that you can quickly address each thread and focus your attention on the ones that actually require it - Indicate whether feedback is blocking or non-blocking so only crucial feedback blocks PR merge - Ensure all blocking review threads are addressed before merge via a status check - Track total active review time per reviewer so that you can understand the effort and review time going into each PR - See reviewer presence so that you can see if someone is already reviewing and avoid unnecessary pings. In addition to these workflow improvements, Visibly provides a personalized metrics dashboard so you can understand your real-time performance and better advocate for yourself and advance your career. You can see a demo and a breakdown of all of the current features at https://visibly.dev . I created Visibly after seeing the same pain points across Microsoft, Snap, Headspace, and Brex: - Code review was frequently undervalued and invisible. Developers recognized this and prioritized code production instead, which resulted in knowledge silos, poor code quality, and a culture of rubber stamp reviews. - Engineering performance review was broken. From the manager’s perspective, it was difficult to identify top performers and to provide timely, actionable feedback. From the IC’s perspective, feedback was infrequent and subjective around many aspects of the job. The process was also time-consuming and required everyone to sift through 3-6 months of work to quantify “impact and scope”. Visibly solves these issues by using objective measures to make code review more visible and efficient. Visibly values privacy and security above all else; it does not require source code access and requests the fewest possible permissions to provide these experiences. Visibly is also differentiated in its belief that individuals should know how they’re doing and have personalized metrics so that they can advance in their careers. If you use GitHub, you can try Visibly today for free by heading to https://visibly.dev/login . Visibly is currently free, but we plan to introduce paid features and pricing plans soon. Our pricing is still under development, but based on existing customer feedback, we expect to price at $20/seat/month. For those that sign up over the next week, we plan to provide 50% off for the first year once our pricing plans launch. We look forward to your thoughts and feedback!

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Actual performance

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17comments
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Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: using, code · 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, code review · Missing: https docs, excited, just released
67%67% 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: reviews, soon, efficient · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, month · Missing: mobile apps, ios, entrepreneurs
30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · 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 · Strong signals: paid, introduce · 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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