Bring order to Slack channels

Hacker News

Hey there, You are an Engineer or Engineering Manager, your team has a Slack channel where people come and: * ask questions about product * ask for code reviews * one-off requests to help with a bug When there are >30 people distributed across time zones, you get lost in incoming requests, code reviews get delayed, people ping your team again and again to review the PR they created 10 days ago. You lose visibility on what's already responded, what is not. You scroll through the messages and try to respond them or assign a someone in your team. Now multiply this with number of channels you are in and you have bunch of your own open requests waiting to get resolved. Let me introduce you to a "@work" bot: https://slack.com/apps/A04KSRC0606-work "@work" creates a virtual queue in the Slack channel. Every request can be added to the queue as a task. You can assign task to someone in your team and eventually mark it as completed. Best thing is, every morning, engineer in on-call rotation can get list of all open questions/review requests, quickly navigate to the thread and go one by one to resolve them. No need to scroll through messages and trying to figure out which requests are still open. For some use cases, you can even replace Jira. How? For each project create a new channel, add tasks as you go and you have visibility over the tasks across channels Would love to hear your feedback! [0] - https://slack.com/apps/A04KSRC0606-work

Visit product View on Hacker NewsJun 19, 2023

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

91%91% 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: created · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

20%20% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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