ChiefOnboarding: open-source employee onboarding platform (+ Slack bot)

Hacker News

I have been building ChiefOnboarding for about 4 years now. With ChiefOnboarding, you can easily design timelines for new hires. Add to do items, courses, badges, introductions, custom text/Slack/email messages, create user accounts (i.e. Google and Slack) and so much more. You can drag and drop them into the timelines and then re-use those timelines for other new hires. You can onboard new hires through the Slack bot or through the web portal. ChiefOnboarding is available in multiple languages and uses timezones (even per new hire - for remote companies). It used to be a SaaS, but a few months ago, I decided to make it completely open source. I still provide hosting for customers, but you can deploy it on your own servers if you want (docker-compose file included for easy deployment). Documentation (under 'How to/Demo' there is a transcript of a demo with screenshots and all): https://docs.chiefonboarding.com Source code: https://github.com/chiefonboarding/chiefonboarding Let me know if you have any questions/feedback!

Visit product View on Hacker NewsDec 23, 2020

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5points

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Did not reach leaderboard

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

92%92% 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

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

70%70% 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: month, google · Missing: mobile apps, ios, personal

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

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

23%23% 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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