Squirrels – A Scalable Framework for Dynamic API Endpoints

Hacker News

Hey All! We've just made Squirrels, a Data API framework packed with features like dynamic SQL & Python query generation using Jinja templating, cascading parameters, and in-memory caching. We've also tried to use a project layout similar to that of DBT, so that people familiar with it can get started quicker. It's published on PyPI along with comprehensive documentation. We're on a mission to evolve it into a full-fledged solution, and we'd love your feedback. Give it a whirl and let us know how it fits into your workflows or where we can improve. Check out the GitHub page: https://github.com/squirrels-nest/squirrels And dive into the documentation/home page: https://squirrels-nest.github.io/

Visit product View on Hacker NewsApr 15, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: using · 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: io · Missing: https docs, excited, just released

64%64% 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 · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

36%36% 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

33%33% 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

10%10% 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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