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Top 5 JavaScript demo-app Projects
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React-Native-Light-Dark-Starter
React Native Expo simple starter with full light/dark theme control using async storage and context api, in addition to login and register screens
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blackjack-basic-strategy
A computer vision powered Blackjack basic strategy app powered by Roboflow.
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SurveyJS
Open-Source JSON Form Builder to Create Dynamic Forms Right in Your App. With SurveyJS form UI libraries, you can build and style forms in a fully-integrated drag & drop form builder, render them in your JS app, and store form submission data in any backend, inc. PHP, ASP.NET Core, and Node.js.
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cicd-with-herokuci
This is a demo factorial app for the building CI/CD workflow with Heroku CI article https://blog.mikemwanje.dev/build-a-cicd-pipeline-with-heroku-ci. The application calculates the factorial of a number.
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autoidle-saving
This is a demo application for how to cut costs on Heroku with AutoIdle in the article https://blog.mikemwanje.dev/autoidle-cuts-your-heroku-bill-by-auto-putting-your-staging-and-review-apps-to-sleep. Start fom the start branch as you follow along untill the code version in the main branch is reached.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
Project mention: Show HN: Pip install inference, open source computer vision deployment | news.ycombinator.com | 2023-08-23It’s an easy to use inference server for computer vision models.
The end result is a Docker container that serves a standardized API as a microservice that your application uses to get predictions from computer vision models (though there is also a native Python interface).
It’s backed by a bunch of component pieces:
* a server (so you don’t have to reimplement things like image processing & prediction visualization on every project)
* standardized APIs for computer vision tasks (so switching out the model weights and architecture can be done independently of your application code)
* model architecture implementations (which implement the tensor parsing glue between images & predictions) for supervised models that you've fine-tuned to perform custom tasks
* foundation model implementations (like CLIP & SAM) that tend to chain well with fine-tuned models
* reusable utils to make adding support for new models easier
* a model registry (so your code can be independent from your model weights & you don't have to re-build and re-deploy every time you want to iterate on your model weights)
* data management integrations (so you can collect more images of edge cases to improve your dataset & model the more it sees in the wild)
* ecosystem (there are tens of thousands of fine-tuned models shared by users that you can use off the shelf via Roboflow Universe[1])
Additionally, since it's focused specifically on computer vision, it has specific CV-focused features (like direct camera stream input) and makes some different tradeoffs than other more general ML solutions (namely, optimized for small-fast models that run at the edge & need support for running on many different devices like NVIDIA Jetsons and Raspberry Pis in addition to beefy cloud servers).
[1] https://universe.roboflow.com
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- ML-powered, human-in-the-loop system to manage wildlife camera trap images & annotations
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A note from our sponsor - InfluxDB
www.influxdata.com | 27 Apr 2024
Index
What are some of the best open-source demo-app projects in JavaScript? This list will help you:
Project | Stars | |
---|---|---|
1 | React-Native-Light-Dark-Starter | 33 |
2 | blackjack-basic-strategy | 26 |
3 | ralix-todomvc | 5 |
4 | cicd-with-herokuci | 3 |
5 | autoidle-saving | 0 |
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