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blackjack-basic-strategy
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wallet | blackjack-basic-strategy | |
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23 | 23 | |
160 | 26 | |
4.4% | - | |
9.8 | 2.0 | |
6 days ago | about 1 year ago | |
TypeScript | JavaScript | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
wallet
- Valora | A mobile payments app that works worldwide | $AMP Verified Coming SOON
- Is there any open-source high-traffic, enterprise React application written in TypeScript with best practices?
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Celo: Financial Prosperity for Everyone | Hex Trust
What really makes the Celo protocol stand out is its ability to solve two fundamental barriers to using crypto as a means of payment: ease of use and purchasing power volatility. The mobile-first Valora wallet built on Celo is a social payment application-oriented around overcoming these exact barriers through phone number verification and stable-value assets. Crypto addresses are linked to using mobile phone numbers, making crypto transactions as easy as sending a text message. The application also provides minimal volatility of assets through the use of stablecoins, countering any concerns of payment made with volatile assets. Access to other dApps in the Celo ecosystem, decentralized exchanges, lending opportunities within DeFi, and exploration of NFT collections are all available on the same platform — facilitating interoperability and enhancing user experience.
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Pilot Spotlight: How Celo and Mercy Corps Ventures Partnered to Benefit Microworkers in Kenya
The initial training, conducted by local partner, NairoBits, provided participants with guidance on cryptocurrency and cUSD transactions, how to complete microwork tasks, and how to cash out earnings to M-Pesa. After that phase, all 200 Kenyan participants went on to complete digital microwork tasks provided by Appen via the Toca app (now Corsali). Within a few seconds of completing their tasks in Corsali, participants were paid in cUSD. After linking their Corsali account to the Valora.app, a popular cryptocurrency wallet built on Celo, participants were able to transfer their earnings into Valora for a fee of only 0.02%, regardless of transaction size.
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Valora Supercharge: 25% APY on Celo stablecoins! (up to $/€1000)
All you have to do is download the Valora app, a Celo wallet, and fund it by buying cUSD or cEUR, which you can do from the app directly.
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Get Your Dapp Featured in Valora (4 dapps will also receive 10k cUSD)
Valora Deeplinks for integrating with Valora
- Solana Pay , Phantom, Citcon, Wormhole....
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Best places to receive some yield on stablecoins ?
you can get 50% APY on cUSD (USD stablecoin on celo) or cEUR (EUR stablecoin on celo) using the Valora wallet on the Celo blockchain. Can learn more on the blog
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Crypto Christmas !? How should I gift crypto to family ? Which coin ?
Gift them Celo using Valora. You can send them money even if they don’t have a wallet yet by sending it to their phone number.
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What is the Celo mobile-first DeFi platform?
Download a mobile-first wallet or Celo wallet and start using Celo ecosystem. Metamask is appropriate for DeFi dapps.
blackjack-basic-strategy
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Show HN: Pip install inference, open source computer vision deployment
It’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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Open discussion and useful links people trying to do Object Detection
* Most of the time I find Roboflow extremely handy, I used it to merge datasets, augmentate, read tutorials and that kind of thing. Basically you just create your dataset with roboflow and focus on other aspects.
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TensorFlow Datasets (TFDS): a collection of ready-to-use datasets
For computer vision, there are 100k+ open source classification, object detection, and segmentation datasets available on Roboflow Universe: https://universe.roboflow.com
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Please suggest resources to learn how to work with pre-trained CV models
Solid website and app overall for learning more about computer vision, discovering datasets, and keeping up with advancements in the field: * https://roboflow.com/learn * https://universe.roboflow.com (datasets) | https://blog.roboflow.com/computer-vision-datasets-and-apis/ * https://blog.roboflow.com
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Suggestion for identification problem with shipping labels?
If you're lacking training images, you can also use [Roboflow Universe](https://universe.roboflow.com) to obtain them (over 100 million labeled images available)
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Ask HN: Who is hiring? (November 2022)
Roboflow | Multiple Roles | Full-time (Remote) | https://roboflow.com/careers
Roboflow is the fastest way to use computer vision in production. We help developers give their software the sense of sight. Our end-to-end platform[1] provides tooling for image collection, annotation, dataset exploration and curation, training, and deployment.
Over 100k engineers (including engineers from 2/3 Fortune 100 companies) build with Roboflow. And we now host the largest collection[2] of open source computer vision datasets and pre-trained models[3].
We have several openings available, but are primarily looking for strong technical generalists who want to help us democratize computer vision and like to wear many hats and have an outsized impact. (We especially love hiring past and future founders.)
We're hiring 3 full-stack engineers this quarter and we're also looking for an infrastructure engineer with Elasticsearch experience.
[1]: https://docs.roboflow.com
[2]: https://blog.roboflow.com/computer-vision-datasets-and-apis/
[3]: https://universe.roboflow.com
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When annotating an image, if a collection of an entity changes the nature of the entity, do you label them collectively or separately?
Based on what I do/use when I prepare models: A good framework for creating and improving this dataset faster is to use Roboflow Universe and search “flowers” and “bouquets of flowers” in the search bar (it’s like Google Images for CV Datasets). You can search images by subject, or metadata, and clone them directly into a free public workspace (they house up to 10k images without charge). * https://universe.roboflow.com/ * https://universe.roboflow.com/search?q=flowers * https://universe.roboflow.com/search?q=bouqets
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Need help on finding an area where machine learning is applicable on day-to-day life but not implemented already
Lots of ideas will come to mind if you look and search through open source datasets: https://universe.roboflow.com/
- Ask HN: Any good self-hosted image recognition software?
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SAAS for object detection?
Open source datasets: https://universe.roboflow.com/ Model training: https://docs.roboflow.com/train Model deployment: https://docs.roboflow.com/inference/hosted-api
What are some alternatives?
celo-monorepo - Official repository for core projects comprising the Celo platform
uxp-photoshop-plugin-samples - UXP Plugin samples for Photoshop 22 and higher.
hookdeck-cli - Manage your Hookdeck workspaces, connections, transformations, filters, and more with the Hookdeck CLI
process-google-dataset - Process Google Dataset is a tool to download and process images for neural networks from a Google Image Search using a Chrome extension and a simple Python code.
cost-model - Cross-cloud cost allocation models for Kubernetes workloads [Moved to: https://github.com/kubecost/opencost]
rollup-react-example - An example React application using Rollup with ES modules, dynamic imports, Service Workers, and Flow.
tesseract - Deterministic physics engine for Argo Navis
edenai-javascript - The best AI engines in one API: vision, text, speech, translation, OCR, machine learning, etc. SDK and examples for JavaScript developers.
Gravitational Teleport - The easiest, and most secure way to access and protect all of your infrastructure.
Speed-Coding-Games-in-JavaScript - Games Repository from Speed Coding channel
teleport - A WebXR teleport for three.js
Alpaca-API - The Alpaca API is a developer interface for trading operations and market data reception through the Alpaca platform.