Slack knowledge curator to extract FAQ from discussion threads

Hacker News

Hi HN! Excited to share Snowshoe, a Slack bot that transforms your Slack threads into a searchable StackOverflow-style Q&A. Snowshoe automatically curates key discussions from Slack, creating a dynamic, easy-to-navigate knowledge hub. A lot of technical discussions among engineers happen in Slack threads. Yet the unstructured thread makes its inherent knowledge difficult for search and thus the discussion insights aren’t often well documented & maintained. LLM is surprisingly good at summarization & extraction. Thus I think it’s good timing to give it a try. Currently Snowshoe focuses on knowledge extraction in Q&A format. What’s next? I feel like there would be a lot of value in the auto-curated Q&A, e.g. auto question answering, knowledge hub, Slack archive for SEO. Happy to hear your thoughts!

Visit product View on Hacker NewsDec 20, 2023

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

77%77% 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.

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

66%66% 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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

32%32% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

30%30% 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

17%17% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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