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I build a self-service, fair, predictable oncall scheduling solution

Hacker News

I build a self-service, fair, predictable oncall scheduling solution

The way most of us do oncall scheduling kind of sucks. Most automated solutions are just a round-robin list of people who get scheduled in that order. When someone leaves the team, their slots are filled by just moving everybody else up, which scrambles everybody's plans. Some teams use a spreadsheet with some self-service entry of preferences, but then someone has to create a tentative schedule, talk to everybody about whether it works, and then iterate, which takes a lot of time and is not a joyful process. The most caring orgs have built some kind of automated system, but it's one of those internal apps which doesn't get much love because it's a low-priority side-gig for someone. To fix this, I build https://oncallscheduler.com . It gives self-service control to everybody to both pick shifts they prefer to work, and to enter preferences about dates/holidays they want to avoid working. It solves the "person left the team" scenario with something of an auction for the shift. People who work less desirable shifts, including taking on shifts from someone who left the team, end up working fewer shifts in total over time. Check it out! And let me know if you think there's something missing from how it works.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps · Missing: mac, agents, macos
88%88% 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: including · Missing: supports, reddit linkedin, podcasting
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: apps, way · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
33%33% 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
19%19% 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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