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Cloudavenue.ai – Prevent breaking your data stack with a GitHub app

Hacker News

Cloudavenue.ai – Prevent breaking your data stack with a GitHub app

Hey all! We built CloudAvenue.ai - LLM-based GitHub app that helps data teams avoid making breaking changes in their data stack. We got "inspired" by working at companies where app teams kept making breaking changes, and the data teams had no control over it. So we decided to build this GitHub app to help fix that. Try it out - no signup required. With CloudAvenue.ai, you can: - Catch errors before they hit production - Generate detailed impact report for every change - Improve data pipeline integrity - Drastically reduce troubleshooting time Here's how to try it: 1. Visit https://www.cloudavenue.ai or by directly installing our GitHub app - https://github.com/apps/cloud-avenue-data-stack-insights 2. Follow the instructions to connect your GitHub repos. 3. Make some code changes and see how CloudAvenue.ai provides real-time feedback. 4. [OPTIONAL] create CloudAvenue account to connect non-repo tools, e.g. Fivetran, Lightdash, etc… We're excited to share CloudAvenue.ai with the Hacker News community and get your feedback! We’re also happy to add support for more tools and technologies - just let us know which ones you’d like to see next!

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, new, code · 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 · Missing: supports, reddit linkedin, podcasting
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, hacker news, ide · Missing: https docs, just released, exist
60%60% 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
37%37% 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
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
16%16% 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
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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