RotaHog rotas for teams. No more spreadsheet hell

Hacker News

Hey HN, I’m a manager who’s been burned by the chaos of organizing rotas in spreadsheets. Think all-hands facilitation, retros, or even team newsletters. It’s unfair (someone always gets stuck), messy, and forget about reminders. And it’s not just work; my partner and I have had our share of “who’s cooking this week?” squabbles. So, I built RotaHog: a tool to make any rota fair, trackable, and automated with smart notifications. Whether you’re a team lead juggling meeting duties or a couple splitting chores, it’s designed to kill the spreadsheet nightmare. The MVP is live. Basic task rotation, fairness algorithm, and Slack/email notifications. It’s rough around the edges, but it works. Right now, I’m iterating fast based on feedback from early users (shoutout to the Eng managers and couples who’ve kicked the tires!). Features on the horizon: calendar sync, Teams integration, and a slicker UI. Tech stack: Ruby-on-Rails + Hotwire + Postgres + Kamal Try it at rotahog.com (free, no login required). I’d love your thoughts. What’s your worst rota pain? Any must-have features? Or just roast the landing page and that I'm still using Bootstrap in 2025. I can take it. :) — João P.S. If you’re curious about the build journey, I’m sharing updates on X (x.com/joaoqalves)

Visit product View on Hacker NewsApr 24, 2025

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

90%90% 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

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users, way · 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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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