Ask questions to your tech stack documentation

Hacker News

The app uses Xata AI to query ChatGPT with context from the up-to-date docs. This provides more recent information and reduces hallucinations. It uses this high-level algorithm: - Pass the question to ChatGPT and ask it to provide keywords - Use the Xata search functionality to retrieve the most relevant docs - Form a prompt using this context and the question and pass it to ChatGPT - This blog post provides more details on the general approach All the code is open source: https://github.com/xataio/ask-your-stack The web-crawler can be found here: https://github.com/tsg/xata-crawler I think this is similar to GitHub Copilot for Docs but I don't have access to it.

Visit product View on Hacker NewsMar 31, 2023

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4points

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Launch Intel predictions

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

81%81% 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: open source, ide, io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · 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 · Missing: arr, mrr, revenue

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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

Incorrect prediction on native model

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