freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming, and computer science for free.

FCC China open source codebase and curriculum. Learn to code and help nonprofits.

Learn, design or document codebase by putting breadcrumbs in source code. Live updates, multi-language support and more.

RSpec cheatsheet & Rails app: Learn how to expertly test Rails apps from a model codebase

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An import picker that learns from your codebase

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The w3develops.org open source codebase - Learn, build, and meetup with other developers on DISCORD https://discord.gg/WphGvTT and YOUTUBE http://bit.ly/codingyt

Real World Sinatra apps and their open source codebases for developers to learn from

Codebase for my book "Python DeepLearning Projects" | Learn applied deep learning for various use-cases on NLP, CV and ASR using TensorFlow and Keras. Book link.

Codebase for the Let's Learn AdonisJS 6 series

The self-evolving coding agent that learns from your entire team and codebase. Less syncing. Less waiting. Deliver at the speed of thought.

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[ACL2024] A Codebase for Incremental Learning with Large Language Models; Official released code for "Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models (ACL 2024)", "Incremental Sequence Labeling: A Tale of Two Shifts (ACL 2024 Findings)", and "Concept-1K: A Novel Benchmark for Instance Incremental Learning (arxiv)"

A local-first AI engineering agent that learns from your codebase using DSPy.

Real World Elixir apps and their open source codebases for developers to learn from

DevContext is a cutting-edge Model Context Protocol (MCP) server designed to provide developers with continuous, project-centric context awareness. Unlike traditional context systems, DevContext continuously learns from and adapts to your development patterns and delivers highly relevant context providing a deeper understanding of your codebase.

This codebase has been moved to a monorepo. Please see the documentation to learn more. 🍻

The power of machine learning allows us to change long-standing computing paradigms. One of these is the age-old password-based authentication system common to most apps. With fast real-time facial recognition, we can easily dispense with text-based verification and allow users to log in just by showing their faces to a webcam. In this session, we’ll show how to do this in Flutter, Google’s popular open-source UI toolkit for developing apps for web, Android, iOS, Fuchsia, and many other platforms with a single codebase. We’ll first build a simple authentication-based Android app, and then deploy the Firebase ML Vision model for face ID & image processing; as well as the MobileFaceNet CNN model through TensorFlow Lite for structured verification. Once all these parts are in place, our solution will work seamlessly and can easily be ported to other apps. Pre-requisites: ✅ Android Studio (https://developer.android.com/studio) — you can also use other IDEs/platforms if you’d rather not use Android - Flutter documentation below guides on the same. ✅ Flutter SDK (https://flutter.dev/docs/get-started/install) ----------------------------------------- To learn more about The Assembly’s workshops, visit our website, social media or email us at workshops@theassembly.ae Our website: http://theassembly.ae Instagram: http://instagram.com/makesmartthings Facebook: http://fb.com/makesmartthings Twitter: http://twitter.com/makesmartthings #TensorFlow #Flutter #MachineLearning

An open source codebase for sharing programming solutions. This repository is in development phase and will soon provide you with python code of various data structures and algorithms . as we all know that there are not much resources which is available to learn data structures and algorithms in python.

A comprehensive guide and codebase for building interactive storytelling dashboards with Python, Streamlit, and Plotly. Learn how to transform static analytics into dynamic, user-driven data experiences that engage and inspire, featuring RFM segmentation, cohort analysis, and real-world insights.

# Contributing to this repository <!-- omit in toc --> ## Getting started <!-- omit in toc --> Before you begin: - This site is powered by Node.js. Check to see if you're on the [version of node we support](contributing/development.md). - Have you read the [code of conduct](CODE_OF_CONDUCT.md)? - Check out the [existing issues](https://github.com/github/docs/issues) & see if we [accept contributions](#types-of-contributions-memo) for your type of issue. ### Use the 'make a contribution' button  Navigating a new codebase can be challenging, so we're making that a little easier. As you're using docs.github.com, you may come across an article that you want to make an update to. You can click on the **make a contribution** button right on that article, which will take you to the file in this repo where you'll make your changes. Before you make your changes, check to see if an [issue exists](https://github.com/github/docs/issues/) already for the change you want to make. ### Don't see your issue? Open one If you spot something new, open an issue using a [template](https://github.com/github/docs/issues/new/choose). We'll use the issue to have a conversation about the problem you want to fix. ### Ready to make a change? Fork the repo Fork using GitHub Desktop: - [Getting started with GitHub Desktop](https://docs.github.com/en/desktop/installing-and-configuring-github-desktop/getting-started-with-github-desktop) will guide you through setting up Desktop. - Once Desktop is set up, you can use it to [fork the repo](https://docs.github.com/en/desktop/contributing-and-collaborating-using-github-desktop/cloning-and-forking-repositories-from-github-desktop)! Fork using the command line: - [Fork the repo](https://docs.github.com/en/github/getting-started-with-github/fork-a-repo#fork-an-example-repository) so that you can make your changes without affecting the original project until you're ready to merge them. Fork with [GitHub Codespaces](https://github.com/features/codespaces): - [Fork, edit, and preview](https://docs.github.com/en/free-pro-team@latest/github/developing-online-with-codespaces/creating-a-codespace) using [GitHub Codespaces](https://github.com/features/codespaces) without having to install and run the project locally. ### Make your update: Make your changes to the file(s) you'd like to update. Here are some tips and tricks for [using the docs codebase](#working-in-the-githubdocs-repository). - Are you making changes to the application code? You'll need **Node.js v14** to run the site locally. See [contributing/development.md](contributing/development.md). - Are you contributing to markdown? We use [GitHub Markdown](contributing/content-markup-reference.md). ### Open a pull request When you're done making changes and you'd like to propose them for review, use the [pull request template](#pull-request-template) to open your PR (pull request). ### Submit your PR & get it reviewed - Once you submit your PR, others from the Docs community will review it with you. The first thing you're going to want to do is a [self review](#self-review). - After that, we may have questions, check back on your PR to keep up with the conversation. - Did you have an issue, like a merge conflict? Check out our [git tutorial](https://lab.github.com/githubtraining/managing-merge-conflicts) on how to resolve merge conflicts and other issues. ### Your PR is merged! Congratulations! The whole GitHub community thanks you. :sparkles: Once your PR is merged, you will be proudly listed as a contributor in the [contributor chart](https://github.com/github/docs/graphs/contributors). ### Keep contributing as you use GitHub Docs Now that you're a part of the GitHub Docs community, you can keep participating in many ways. **Learn more about contributing:** - [Types of contributions :memo:](#types-of-contributions-memo) - [:mega: Discussions](#mega-discussions) - [:beetle: Issues](#beetle-issues) - [:hammer_and_wrench: Pull requests](#hammer_and_wrench-pull-requests) - [:question: Support](#question-support) - [:earth_asia: Translations](#earth_asia-translations) - [:balance_scale: Site Policy](#balance_scale-site-policy) - [Starting with an issue](#starting-with-an-issue) - [Labels](#labels) - [Opening a pull request](#opening-a-pull-request) - [Working in the github/docs repository](#working-in-the-githubdocs-repository) - [Reviewing](#reviewing) - [Self review](#self-review) - [Pull request template](#pull-request-template) - [Suggested changes](#suggested-changes) - [Windows](#windows) ## Types of contributions :memo: You can contribute to the GitHub Docs content and site in several ways. This repo is a place to discuss and collaborate on docs.github.com! Our small, but mighty :muscle: docs team is maintaining this repo, to preserve our bandwidth, off topic conversations will be closed. ### :mega: Discussions Discussions are where we have conversations. If you'd like help troubleshooting a docs PR you're working on, have a great new idea, or want to share something amazing you've learned in our docs, join us in [discussions](https://github.com/github/docs/discussions). ### :beetle: Issues [Issues](https://docs.github.com/en/github/managing-your-work-on-github/about-issues) are used to track tasks that contributors can help with. If an issue has a triage label, we haven't reviewed it yet and you shouldn't begin work on it. If you've found something in the content or the website that should be updated, search open issues to see if someone else has reported the same thing. If it's something new, open an issue using a [template](https://github.com/github/docs/issues/new/choose). We'll use the issue to have a conversation about the problem you want to fix. ### :hammer_and_wrench: Pull requests A [pull request](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/about-pull-requests) is a way to suggest changes in our repository. When we merge those changes, they should be deployed to the live site within 24 hours. :earth_africa: To learn more about opening a pull request in this repo, see [Opening a pull request](#opening-a-pull-request) below. ### :question: Support We are a small team working hard to keep up with the documentation demands of a continuously changing product. Unfortunately, we just can't help with support questions in this repository. If you are experiencing a problem with GitHub, unrelated to our documentation, please [contact GitHub Support directly](https://support.github.com/contact). Any issues, discussions, or pull requests opened here requesting support will be given information about how to contact GitHub Support, then closed and locked. If you're having trouble with your GitHub account, contact [Support](https://support.github.com/contact). ### :earth_asia: Translations This website is internationalized and available in multiple languages. The source content in this repository is written in English. We integrate with an external localization platform called [Crowdin](https://crowdin.com) and work with professional translators to localize the English content. **We do not currently accept contributions for translated content**, but we hope to in the future. ### :balance_scale: Site Policy GitHub's site policies are published on docs.github.com, too! If you find a typo in the site policy section, you can open a pull request to fix it. For anything else, see [the CONTRIBUTING guide in the site-policy repo](https://github.com/github/site-policy/blob/main/CONTRIBUTING.md). ## Starting with an issue You can browse existing issues to find something that needs help! ### Labels Labels can help you find an issue you'd like to help with. - The [`help wanted` label](https://github.com/github/docs/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22) is for problems or updates that anyone in the community can start working on. - The [`good first issue` label](https://github.com/github/docs/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) is for problems or updates we think are ideal for beginners. - The [`content` label](https://github.com/github/docs/issues?q=is%3Aopen+is%3Aissue+label%3Acontent) is for problems or updates in the content on docs.github.com. These will usually require some knowledge of Markdown. - The [`engineering` label](https://github.com/github/docs/issues?q=is%3Aopen+is%3Aissue+label%3Aengineering) is for problems or updates in the docs.github.com website. These will usually require some knowledge of JavaScript/Node.js or YAML to fix. ## Opening a pull request You can use the GitHub user interface :pencil2: for some small changes, like fixing a typo or updating a readme. You can also fork the repo and then clone it locally, to view changes and run your tests on your machine. ## Working in the github/docs repository Here's some information that might be helpful while working on a Docs PR: - [Development](/contributing/development.md) - This short guide describes how to get this app running on your local machine. - [Content markup reference](/contributing/content-markup-reference.md) - All of our content is written in GitHub-flavored Markdown, with some additional enhancements. - [Content style guide for GitHub Docs](/contributing/content-style-guide.md) - This guide covers GitHub-specific information about how we style our content and images. It also links to the resources we use for general style guidelines. - [Reusables](/data/reusables/README.md) - We use reusables to help us keep content up to date. Instead of writing the same long string of information in several articles, we create a reusable, then call it from the individual articles. - [Variables](/data/variables/README.md) - We use variables the same way we use reusables. Variables are for short strings of reusable text. - [Liquid](/contributing/liquid-helpers.md) - We use liquid helpers to create different versions of our content. - [Scripts](/script/README.md) - The scripts directory is the home for all of the scripts you can run locally. - [Tests](/tests/README.md) - We use tests to ensure content will render correctly on the site. Tests run automatically in your PR, and sometimes it's also helpful to run them locally. ## Reviewing We (usually the docs team, but sometimes GitHub product managers, engineers, or supportocats too!) review every single PR. The purpose of reviews is to create the best content we can for people who use GitHub. :yellow_heart: Reviews are always respectful, acknowledging that everyone did the best possible job with the knowledge they had at the time. :yellow_heart: Reviews discuss content, not the person who created it. :yellow_heart: Reviews are constructive and start conversation around feedback. ### Self review You should always review your own PR first. For content changes, make sure that you: - [ ] Confirm that the changes address every part of the content design plan from your issue (if there are differences, explain them). - [ ] Review the content for technical accuracy. - [ ] Review the entire pull request using the [localization checklist](contributing/localization-checklist.md). - [ ] Copy-edit the changes for grammar, spelling, and adherence to the style guide. - [ ] Check new or updated Liquid statements to confirm that versioning is correct. - [ ] Check that all of your changes render correctly in staging. Remember, that lists and tables can be tricky. - [ ] If there are any failing checks in your PR, troubleshoot them until they're all passing. ### Pull request template When you open a pull request, you must fill out the "Ready for review" template before we can review your PR. This template helps reviewers understand your changes and the purpose of your pull request. ### Suggested changes We may ask for changes to be made before a PR can be merged, either using [suggested changes](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/incorporating-feedback-in-your-pull-request) or pull request comments. You can apply suggested changes directly through the UI. You can make any other changes in your fork, then commit them to your branch. As you update your PR and apply changes, mark each conversation as [resolved](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/commenting-on-a-pull-request#resolving-conversations). ## Windows This site can be developed on Windows, however a few potential gotchas need to be kept in mind: 1. Regular Expressions: Windows uses `\r\n` for line endings, while Unix based systems use `\n`. Therefore when working on Regular Expressions, use `\r?\n` instead of `\n` in order to support both environments. The Node.js [`os.EOL`](https://nodejs.org/api/os.html#os_os_eol) property can be used to get an OS-specific end-of-line marker. 1. Paths: Windows systems use `\` for the path separator, which would be returned by `path.join` and others. You could use `path.posix`, `path.posix.join` etc and the [slash](https://ghub.io/slash) module, if you need forward slashes - like for constructing URLs - or ensure your code works with either. 1. Bash: Not every Windows developer has a terminal that fully supports Bash, so it's generally preferred to write [scripts](/script) in JavaScript instead of Bash.

A complete team of specialized AI agents for Ruby on Rails development. Drop into any Rails project and they'll analyze your codebase, learn your patterns, and help build features.

Official codebase for "Sampling For Learnability", published at NeurIPS 2024

This code is no longer maintained. The codebase has been moved to https://github.com/scikit-learn-contrib/skglm. This repository only serves to reproduce the results of the AISTATS 2021 paper "Anderson acceleration of coordinate descent" by Quentin Bertrand and Mathurin Massias.

Ethereum has brought us tools like Smart Contract, Dapp and DAO creation, deployment, and management. We can easily pay someone without ever hitting the send button, access decentralized applications that cannot be censored or shut down and we can be part of Decentralized Autonomous Organizations. Mistakes were made, bugs were found, and recently, millions were lost. Some are calling The DAO hack the most expensive bug bounty ever held, but whoever said this certainly didn’t have his Ether invested in The DAO, as the situation regarding the seizure of the stolen funds doesn’t seem to be improving. The DAO happened, it failed, all we can do now is move on and learn from our mistakes. The problem is that if we keep learning from $50m errors, we’ll be the wisest and poorest people on the planet. That’s why it’s good to have training wheels sometimes. Ethereum is the perfect playground for skilled developers, but with its 700% value increase since creation, it has made Solidity, one of the programming languages in Ethereum, a very expensive toy. That’s why Krypton has launched an open invitation to all developers to poke around the Krypton blockchain and see what it has to offer. Krypton (KR) is an Ethereum-based cryptocurrency that allows users all the same features and perks (Smart Contracts, Dapps, DAOs, DACs) but for a lower “price.” Ethereum transaction fees, which are known as “Gas” are spent according to computational costs, which means that the higher the price of Ether, the higher those costs will be. covertress, the Krypton founder, and project manager said: We’ve contacted several faculties at major universities and invited them to use the KR chain for this purpose. All of this means that developers have a testbed for smart contracts and Dapps, which are less expensive to deploy in the KR blockchain, before moving on to a more mainstream environment like Ethereum. Krypton can now be considered as a “gateway” into Ethereum. The team isn’t planning to stay humble forever but will, however, take their time before deploying anything and becoming a direct competitor to Ethereum, allowing them to tighten up security and functionality before moving on to providing smart contracts and Dapp solutions for companies. If you liked this article follow us on Twitter @themerklenews and make sure to subscribe to our newsletter to receive the latest bitcoin and altcoin price analysis and the latest cryptocurrency news. Krypton – Smart Contracts and DAPPs Development for Business Systems & IoT Ticker: KR Algorithm: Dagger-Hashimoto Block Reward: 0.25 KR Block Target: 15 Seconds Listen Port: 17171 RPC Port: 8888 Total KR: ~2.669 Million Real-Time Total KR Ethereum-Based: Utilizes Smart Contracts, DAOs, DACs and DAPPs Block Explorer: http://explorer.krypton.rocks After years in the tech sector, for engineering, entertainment, travel & finance companies, I’ve turned my focus to blockchain and building a startup, Krypton, to help companies realize their distributed applications. $KR is my vision for an ultra-fast blockchain that can realize all of the features of Ethereum with fewer initial coins, faster speed and lower inflation. Krypton can do the same things as Ethereum. However, with Ethereum’s codebase being updated to safely deploy DAOs, DACs, and DAPPs, there will be an explosion of practical-use cases, especially in the Internet of Things field. Companies will be actively seeking experienced developers. KR is an alternative platform on which to deploy these new technologies and Krypton developers are ready to build these systems. Join me in connecting Ðapps devs with real-life applications. Let’s code the future. — covertress, Founder & Project Manager Krypton is now hiring Smart Contracts and Ðapps developers with experience in Solidity, JS, and node.js. Please join Krypton’s Slack to apply: http://slack.krypton.rocks

 Read the technical deep dive: https://www.dessa.com/post/deepfake-detection-that-actually-works # Visual DeepFake Detection In our recent [article](https://www.dessa.com/post/deepfake-detection-that-actually-works), we make the following contributions: * We show that the model proposed in current state of the art in video manipulation (FaceForensics++) does not generalize to real-life videos randomly collected from Youtube. * We show the need for the detector to be constantly updated with real-world data, and propose an initial solution in hopes of solving deepfake video detection. Our Pytorch implementation, conducts extensive experiments to demonstrate that the datasets produced by Google and detailed in the FaceForensics++ paper are not sufficient for making neural networks generalize to detect real-life face manipulation techniques. It also provides a current solution for such behavior which relies on adding more data. Our Pytorch model is based on a pre-trained ResNet18 on Imagenet, that we finetune to solve the deepfake detection problem. We also conduct large scale experiments using Dessa's open source scheduler + experiment manger [Atlas](https://github.com/dessa-research/atlas). ## Setup ## Prerequisities To run the code, your system should meet the following requirements: RAM >= 32GB , GPUs >=1 ## Steps 0. Install [nvidia-docker](https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-2.0)) 00. Install [ffmpeg](https://www.ffmpeg.org/download.html) or `sudo apt install ffmpeg` 1. Git Clone this repository. 2. If you haven't already, install [Atlas](https://github.com/dessa-research/atlas). 3. Once you've installed Atlas, activate your environment if you haven't already, and navigate to your project folder. That's it, You're ready to go! ## Datasets Half of the dataset used in this project is from the [FaceForensics](https://github.com/ondyari/FaceForensics/tree/master/dataset) deepfake detection dataset. . To download this data, please make sure to fill out the [google form](https://github.com/ondyari/FaceForensics/#access) to request access to the data. For the dataset that we collected from Youtube, it is accessible on [S3](ttps://deepfake-detection.s3.amazonaws.com/augment_deepfake.tar.gz) for download. To automatically download and restructure both datasets, please execute: ``` bash restructure_data.sh faceforensics_download.py ``` Note: You need to have received the download script from FaceForensics++ people before executing the restructure script. Note2: We created the `restructure_data.sh` to do a split that replicates our exact experiments avaiable in the UI above, please feel free to change the splits as you wish. ## Walkthrough Before starting to train/evaluate models, we should first create the docker image that we will be running our experiments with. To do so, we already prepared a dockerfile to do that inside `custom_docker_image`. To create the docker image, execute the following commands in terminal: ``` cd custom_docker_image nvidia-docker build . -t atlas_ff ``` Note: if you change the image name, please make sure you also modify line 16 of `job.config.yaml` to match the docker image name. Inside `job.config.yaml`, please modify the data path on host from `/media/biggie2/FaceForensics/datasets/` to the absolute path of your `datasets` folder. The folder containing your datasets should have the following structure: ``` datasets ├── augment_deepfake (2) │   ├── fake │   │   └── frames │   ├── real │   │   └── frames │   └── val │   ├── fake │   └── real ├── base_deepfake (1) │   ├── fake │   │   └── frames │   ├── real │   │   └── frames │   └── val │   ├── fake │   └── real ├── both_deepfake (3) │   ├── fake │   │   └── frames │   ├── real │   │   └── frames │   └── val │   ├── fake │   └── real ├── precomputed (4) └── T_deepfake (0) ├── manipulated_sequences │   ├── DeepFakeDetection │   ├── Deepfakes │   ├── Face2Face │   ├── FaceSwap │   └── NeuralTextures └── original_sequences ├── actors └── youtube ``` Notes: * (0) is the dataset downloaded using the FaceForensics repo scripts * (1) is a reshaped version of FaceForensics data to match the expected structure by the codebase. subfolders called `frames` contain frames collected using `ffmpeg` * (2) is the augmented dataset, collected from youtube, available on s3. * (3) is the combination of both base and augmented datasets. * (4) precomputed will be automatically created during training. It holds cashed cropped frames. Then, to run all the experiments we will show in the article to come, you can launch the script `hparams_search.py` using: ```bash python hparams_search.py ``` ## Results In the following pictures, the title for each subplot is in the form `real_prob, fake_prob | prediction | label`. #### Model trained on FaceForensics++ dataset For models trained on the paper dataset alone, we notice that the model only learns to detect the manipulation techniques mentioned in the paper and misses all the manipulations in real world data (from data)   #### Model trained on Youtube dataset Models trained on the youtube data alone learn to detect real world deepfakes, but also learn to detect easy deepfakes in the paper dataset as well. These models however fail to detect any other type of manipulation (such as NeuralTextures).   #### Model trained on Paper + Youtube dataset Finally, models trained on the combination of both datasets together, learns to detect both real world manipulation techniques as well as the other methods mentioned in FaceForensics++ paper.   for a more in depth explanation of these results, please refer to the [article](https://www.dessa.com/post/deepfake-detection-that-actually-works) we published. More results can be seen in the [interactive UI](http://deepfake-detection.dessa.com/projects) ## Help improve this technology Please feel free to fork this work and keep pushing on it. If you also want to help improving the deepfake detection datasets, please share your real/forged samples at foundations@dessa.com. ## LICENSE © 2020 Square, Inc. ATLAS, DESSA, the Dessa Logo, and others are trademarks of Square, Inc. All third party names and trademarks are properties of their respective owners and are used for identification purposes only.

In this codebase we establish a benchmark for egocentric user adaptation based on Ego4d.First, we start from a population model which has data from many users to learn user-agnostic representations.As the user gains more experience over its lifetime, we aim to tailor the general model to user-specific expert models.

This is the codebase for a series on building an LMS app with NextJS and SupaBase for Youtube called Code with Andrew Ford. See my blog link below to learn more.