Sleuth, open source workspace search in natural language

Hacker News

Hey everyone, We know how hard it can be to ramp up and learn the ins and outs of a new company. - “Who should I talk to about customer onboarding?” - “What was that project the onboarding team shipped in June, that had a massive impact on step 3 completion rate?” Instead of asking someone the same question that’s been asked hundreds of times before, it’s more efficient to find answers in existing documents and past conversations. The problem is, this data is spread out across dozens of workplace apps, with search features that all work differently. That’s why we’ve created Sleuth, an open source library that allows you to search through your company’s entire history using natural language. It understands the intent of your question, not just the keywords. Here’s a demo: https://www.loom.com/share/71625cce862f4d4ea12b8a87ad94e407 You can fork our repo ( https://github.com/getsleuth/Sleuth ) and try it right now, or book a 15 min call ( https://calendly.com/triton-founders/sleuth-feedback ) with us to share your feedback. How does it work? Vector embeddings are generated for slack messages using OpenAI’s text-embedding-ada-002 model and stored in a Pinecone vector database for easy querying. How is this different from Glean? Glean is great, but we wanted to introduce a product that anyone can fork, use, and customize without ever talking to a sales team. Building in public makes for better products. What integrations do you support? Just Slack to start. What other integrations would you like to see?

Visit product View on Hacker NewsJan 12, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, slack, apps · 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

76%76% 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, answers · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% 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.

Correct prediction on native model

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