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Rezible – Open-Source Mission Control for Oncall Teams

Hacker News

Rezible – Open-Source Mission Control for Oncall Teams

Hi HN, Tex here! Excited to share what I've been working on solo recently. Rezible is a "mission control" platform for oncall teams, aiming to automate, support, and report on the important but often overlooked, unglamorous work involved being oncall. Github repo: https://github.com/rezible/rezible While working as an SRE in different teams across Google & Canva, I saw the impact an unhealthy oncall rotation can have on engineers as individuals and as teams. I believe oncall is a huge missed opportunity for many teams - it is often viewed as a necessary evil rather than as a source of growth & learning. This is not surprising considering the continuous administrative upkeep needed to keep a rotation healthy. So while all dysfunctional rotations are somewhat unique, there are common practices that strong ones share - which I am building as features in Rezible to provide "healthy oncall on rails": - Oncall shift event annotation (e.g. flag noisy alerts, measure toil) - Automated shift handovers - AI powered post-incident debriefs - Real-time collaborative incident retrospectives - Searchable & discoverable knowledgebase (populated from retrospective learnings & analysis) - Structured oncall training & onboarding The backend is in Go + Huma/OpenAPI, and the frontend is Svelte 5 + SvelteKit (which has been a pleasure to use despite not being a frontend engineer). It's still very rough around the edges, but I'm sharing now to avoid it never being "ready" :) Github: https://github.com/rezible/rezible If you're interested in receiving updates: https://tally.so/r/wLJ5ll Would greatly appreciate any feedback & a star on Github!

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, open · Missing: mac, agents, macos
82%82% 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: excited, ide, io · Missing: https docs, just released, exist
62%62% 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
56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · 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 · Strong signals: growth, training · Missing: arr, mrr, revenue
15%15% 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.

Incorrect prediction on native model

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