AI

AI Sidekick – Copilot for team analytics in Slack

Hacker News

AI Sidekick – Copilot for team analytics in Slack

Hey Everyone! We’ve been building in Slack & MS Teams ecosystem for a while & have consistently observed that our users request more & more analytics on the data being shared in standup notes/daily Q&A. With AI Sidekick, we’re set out to do just that! Each team lead could have an AI powered sidekick of their own which can tell them whatever there is to know about their team. How does AI Sidekick work? It’s super simple! Once you add AI Sidekick to your Slack workspace, all you have to do is ask your team members to start filling out daily Q&A or standup notes when they start their day. Sidekick needs at-least 3 days of data to run analytics, until then it will respond from Dummy/Test data to give you a feel of how it would work once it has more data about your team. What’s next? 1) Sidekick Goggles We want your sidekick to have supercharged visibility so that it can always answer who's who and what's what. We are working on integrating Sidekick with your team’s project management tools like Jira, ClickUp, Asana & daily utility tools like Calendar, Github etc. This will enable your sidekick to share insights like “Harry had a productive month. He closed 7 support tickets and 43 tasks for the Acme Inc project in Jira. Additionally, he attended 4 client meetings and 9 team meetings.” 2) Sidekick Actions We want your sidekick to be able to do more as it grows up. It will soon be able to take actions all across your tool stack. You will be able to set up team meetings, create new tasks, or simply ask your sidekick to take notes and remind you stuff. (Slackbot who? ) We’re very excited for all of you folks to take your very own Sidekick out on an adventure!

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, user, new · Missing: mac, agents, macos
97%97% 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
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: month, users, way · Missing: mobile apps, ios, personal
57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
51%51% 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: soon, users · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
21%21% 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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