Query standup data in natural language

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Hey HN, I was a PM for ~ 4 years before deciding to ditch everything to learn how to build software on my own. As a PM, I was doing standups every day with our engineers, but whenever I'd need an update I'd still end up pinging them on Slack. Why? - No notes from those standup calls - Folks being unavailable - No solid insight from those standups. We ended up using a Standup tool but it didn't do much apart from converting our calls to text + making standups async. Sadly, there was still no insight. So decided to do build something myself: howsthisgoing - it's an AI-powered standup bot that lets you query team updates using natural language (built with Gemini, Claude, Python/Django, Celery, Tailwind). Demo: https://www.youtube.com/watch?v=9ijCTM3PmaU Technical details: - Set up slack apis for running standups - Use gemini for standup based summaries - embeddings using voyageai - natural language queries handled in 2-parts (first with claude for extraction, then gemini for filtering) - first time setting up RAG pipeline was fun! (let me know if you'd like to know how I did this) Currently exploring integrations with Github/Linear for deeper context on technical updates. Particularly interested in solving the challenge of connecting different data sources (commits, tickets, standups, sales updates) into a queryable knowledge base. Looking for feedback on: - Which data sources would be most valuable to integrate? - Planning to pivot to an "all-in-one updates app with github/linear/hubspot etc. being as source. What do you guys think of this idea?

Visit product View on Hacker NewsOct 28, 2024

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

96%96% 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: gemini · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

55%55% 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 · Missing: mobile apps, ios, personal

34%34% 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

23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

23%23% predicted probability of success on AppSumo, 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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