#c_lang #ai #big_data #c #cloudberry #data_analysis #data_warehouse #database #distributed_database #greenplum #mpp #olap #postgres #postgresql #sql
Apache Cloudberry is a powerful, open-source database designed for large-scale data processing and analytics. It is built by the creators of Greenplum Database and uses a newer PostgreSQL kernel, making it suitable for data warehouses and AI/ML workloads. You can easily try it out using a Docker-based sandbox or build it from source on Linux or macOS. The community is active, with many channels for support, discussions, and contributions. This means you can get help quickly, share ideas, and even contribute to the project yourself. It's licensed under the Apache License, Version 2.0, making it free to use and modify. Overall, Apache Cloudberry offers advanced database capabilities and a supportive community, which can greatly benefit users needing robust data management solutions.
https://github.com/apache/cloudberry
Apache Cloudberry is a powerful, open-source database designed for large-scale data processing and analytics. It is built by the creators of Greenplum Database and uses a newer PostgreSQL kernel, making it suitable for data warehouses and AI/ML workloads. You can easily try it out using a Docker-based sandbox or build it from source on Linux or macOS. The community is active, with many channels for support, discussions, and contributions. This means you can get help quickly, share ideas, and even contribute to the project yourself. It's licensed under the Apache License, Version 2.0, making it free to use and modify. Overall, Apache Cloudberry offers advanced database capabilities and a supportive community, which can greatly benefit users needing robust data management solutions.
https://github.com/apache/cloudberry
GitHub
GitHub - apache/cloudberry: One advanced and mature open-source MPP (Massively Parallel Processing) database. Open source alternative…
One advanced and mature open-source MPP (Massively Parallel Processing) database. Open source alternative to Greenplum Database. - apache/cloudberry
#python
This project helps you improve language models by fine-tuning them with instructions and public datasets. It provides code for training, evaluating, and optimizing these models using the latest techniques. You can use pre-made scripts to fine-tune models on various datasets and evaluate their performance on different benchmarks. The project also includes tools for preference tuning and reinforcement learning, making your language models more accurate and responsive. By using this project, you can easily update and customize your language models to perform better in different tasks, saving you time and effort in developing advanced AI models.
https://github.com/allenai/open-instruct
This project helps you improve language models by fine-tuning them with instructions and public datasets. It provides code for training, evaluating, and optimizing these models using the latest techniques. You can use pre-made scripts to fine-tune models on various datasets and evaluate their performance on different benchmarks. The project also includes tools for preference tuning and reinforcement learning, making your language models more accurate and responsive. By using this project, you can easily update and customize your language models to perform better in different tasks, saving you time and effort in developing advanced AI models.
https://github.com/allenai/open-instruct
GitHub
GitHub - allenai/open-instruct: AllenAI's post-training codebase
AllenAI's post-training codebase. Contribute to allenai/open-instruct development by creating an account on GitHub.
#typescript
Ant Design X helps you create AI-driven interfaces easily. It offers flexible and diverse atomic components for building personalized AI interaction interfaces, especially for chatbots. You can quickly connect with AI model services like OpenAI and manage conversation data efficiently. The tool supports TypeScript, advanced theme customization, and comes with rich templates to get you started fast. Installing it is simple using npm, yarn, or pnpm, making it a powerful tool for developers to build robust and interactive AI applications.
https://github.com/ant-design/x
Ant Design X helps you create AI-driven interfaces easily. It offers flexible and diverse atomic components for building personalized AI interaction interfaces, especially for chatbots. You can quickly connect with AI model services like OpenAI and manage conversation data efficiently. The tool supports TypeScript, advanced theme customization, and comes with rich templates to get you started fast. Installing it is simple using npm, yarn, or pnpm, making it a powerful tool for developers to build robust and interactive AI applications.
https://github.com/ant-design/x
GitHub
GitHub - ant-design/x: Craft AI-driven interface effortlessly🤖
Craft AI-driven interface effortlessly🤖. Contribute to ant-design/x development by creating an account on GitHub.
#python #baselines #gsde #gym #machine_learning #openai #python #pytorch #reinforcement_learning #reinforcement_learning_algorithms #robotics #sb3 #sde #stable_baselines #toolbox
Stable Baselines3 (SB3) is a tool that makes it easy to use reinforcement learning algorithms with PyTorch. It provides reliable and tested implementations of these algorithms, which helps researchers and developers build projects quickly. SB3 offers many features like custom environments, policies, and integration with other tools like Tensorboard and Hugging Face. It also has detailed documentation and examples to help beginners get started. This tool assumes you have some knowledge of reinforcement learning but provides resources to learn more. Using SB3 can save time and effort by providing a stable base for your projects, allowing you to focus on new ideas and improvements.
https://github.com/DLR-RM/stable-baselines3
Stable Baselines3 (SB3) is a tool that makes it easy to use reinforcement learning algorithms with PyTorch. It provides reliable and tested implementations of these algorithms, which helps researchers and developers build projects quickly. SB3 offers many features like custom environments, policies, and integration with other tools like Tensorboard and Hugging Face. It also has detailed documentation and examples to help beginners get started. This tool assumes you have some knowledge of reinforcement learning but provides resources to learn more. Using SB3 can save time and effort by providing a stable base for your projects, allowing you to focus on new ideas and improvements.
https://github.com/DLR-RM/stable-baselines3
GitHub
GitHub - DLR-RM/stable-baselines3: PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms. - GitHub - DLR-RM/stable-baselines3: PyTorch version of Stable Baselines, reliable implementatio...
#python #artificial_intelligence #attention_mechanism #computer_vision #image_classification #transformers
This text describes a comprehensive implementation of Vision Transformers (ViT) in PyTorch, offering various models and techniques for image classification. Here’s the key information and benefits**
- The repository provides multiple ViT variants, including the original ViT, Simple ViT, NaViT, Deep ViT, CaiT, Token-to-Token ViT, CCT, Cross ViT, PiT, LeViT, CvT, Twins SVT, RegionViT, CrossFormer, ScalableViT, SepViT, MaxViT, NesT, MobileViT, XCiT, and others.
- Each variant introduces different architectural improvements such as efficient attention mechanisms, multi-scale processing, and innovative embedding techniques.
- The implementation includes pre-trained models and supports various tasks like masked image modeling, distillation, and self-supervised learning.
**Benefits** Users can choose from a wide range of ViT models tailored for different needs, such as efficiency, performance, or specific tasks.
- **Performance** Some models, like NaViT and ScalableViT, are designed to be more efficient in terms of computational resources and training time.
- **Ease of Use** The inclusion of various research ideas and techniques allows users to explore new approaches in vision transformer research.
Overall, this repository offers a powerful toolkit for anyone working with vision transformers, providing both practical solutions and cutting-edge research opportunities.
https://github.com/lucidrains/vit-pytorch
This text describes a comprehensive implementation of Vision Transformers (ViT) in PyTorch, offering various models and techniques for image classification. Here’s the key information and benefits**
- The repository provides multiple ViT variants, including the original ViT, Simple ViT, NaViT, Deep ViT, CaiT, Token-to-Token ViT, CCT, Cross ViT, PiT, LeViT, CvT, Twins SVT, RegionViT, CrossFormer, ScalableViT, SepViT, MaxViT, NesT, MobileViT, XCiT, and others.
- Each variant introduces different architectural improvements such as efficient attention mechanisms, multi-scale processing, and innovative embedding techniques.
- The implementation includes pre-trained models and supports various tasks like masked image modeling, distillation, and self-supervised learning.
**Benefits** Users can choose from a wide range of ViT models tailored for different needs, such as efficiency, performance, or specific tasks.
- **Performance** Some models, like NaViT and ScalableViT, are designed to be more efficient in terms of computational resources and training time.
- **Ease of Use** The inclusion of various research ideas and techniques allows users to explore new approaches in vision transformer research.
Overall, this repository offers a powerful toolkit for anyone working with vision transformers, providing both practical solutions and cutting-edge research opportunities.
https://github.com/lucidrains/vit-pytorch
GitHub
GitHub - lucidrains/vit-pytorch: Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with…
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch - lucidrains/vit-pytorch
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#java #api_library #java #payment #sdk #wechatpay #wechatpay_apiv3 #weixin #weixin_pay
This Java SDK is designed to help you integrate WeChat Pay APIv3 into your application. Here are the key benefits and how to use it The SDK handles automatic signing and verification of HTTP requests, making it easier to secure your transactions.
- **Business Services** The SDK can automatically update WeChat Pay platform certificates to ensure security.
- **Error Handling** The SDK supports encryption and decryption of sensitive data according to WeChat Pay's rules.
### How to Use
- **Installation**wechatpay-java Set up the configuration with your merchant ID, private key path, merchant serial number, and APIv3 key.
```java
Config config =
new RSAAutoCertificateConfig.Builder()
.merchantId(merchantId)
.privateKeyFromPath(privateKeyPath)
.merchantSerialNumber(merchantSerialNumber)
.apiV3Key(apiV3Key)
.build();
```
- **Making Requests**: Use the configured service to make payment requests, such as prepay for Native Pay.
```java
NativePayService service = new NativePayService.Builder().config(config).build();
PrepayRequest request = new PrepayRequest();
// Set request parameters
PrepayResponse response = service.prepay(request);
```
By using this SDK, you can simplify the integration of WeChat Pay into your application while ensuring security and compliance with WeChat Pay's API rules.
https://github.com/wechatpay-apiv3/wechatpay-java
This Java SDK is designed to help you integrate WeChat Pay APIv3 into your application. Here are the key benefits and how to use it The SDK handles automatic signing and verification of HTTP requests, making it easier to secure your transactions.
- **Business Services** The SDK can automatically update WeChat Pay platform certificates to ensure security.
- **Error Handling** The SDK supports encryption and decryption of sensitive data according to WeChat Pay's rules.
### How to Use
- **Installation**wechatpay-java Set up the configuration with your merchant ID, private key path, merchant serial number, and APIv3 key.
```java
Config config =
new RSAAutoCertificateConfig.Builder()
.merchantId(merchantId)
.privateKeyFromPath(privateKeyPath)
.merchantSerialNumber(merchantSerialNumber)
.apiV3Key(apiV3Key)
.build();
```
- **Making Requests**: Use the configured service to make payment requests, such as prepay for Native Pay.
```java
NativePayService service = new NativePayService.Builder().config(config).build();
PrepayRequest request = new PrepayRequest();
// Set request parameters
PrepayResponse response = service.prepay(request);
```
By using this SDK, you can simplify the integration of WeChat Pay into your application while ensuring security and compliance with WeChat Pay's API rules.
https://github.com/wechatpay-apiv3/wechatpay-java
GitHub
GitHub - wechatpay-apiv3/wechatpay-java: 微信支付 APIv3 的官方 Java Library
微信支付 APIv3 的官方 Java Library. Contribute to wechatpay-apiv3/wechatpay-java development by creating an account on GitHub.
#python #deep_learning #plate_recognition #pytorch #yolov5
This tool helps you detect and recognize car license plates from images and videos. It supports 12 different types of Chinese license plates, including blue, yellow, new energy, police, and more. You can use it with Python and PyTorch, and it provides demos for testing with images and videos. The benefit is that it makes it easy to automate the process of identifying car license plates accurately, which can be useful for various applications such as traffic management or security systems.
https://github.com/we0091234/Chinese_license_plate_detection_recognition
This tool helps you detect and recognize car license plates from images and videos. It supports 12 different types of Chinese license plates, including blue, yellow, new energy, police, and more. You can use it with Python and PyTorch, and it provides demos for testing with images and videos. The benefit is that it makes it easy to automate the process of identifying car license plates accurately, which can be useful for various applications such as traffic management or security systems.
https://github.com/we0091234/Chinese_license_plate_detection_recognition
GitHub
GitHub - we0091234/Chinese_license_plate_detection_recognition: yolov5 车牌检测 车牌识别 中文车牌识别 检测 支持12种中文车牌 支持双层车牌
yolov5 车牌检测 车牌识别 中文车牌识别 检测 支持12种中文车牌 支持双层车牌. Contribute to we0091234/Chinese_license_plate_detection_recognition development by creating an account on GitHub.
#python #gpt #gpt_4 #gpt4 #graphrag #llm #llms #rag
GraphRAG is a tool that helps extract useful, structured data from unstructured text using large language models (LLMs). It creates a data pipeline to make sense of your private data. To get started, you can use the Solution Accelerator package, which provides a simple way to use GraphRAG with Azure resources. The benefit to you is that GraphRAG can enhance your LLM's ability to understand and reason about your data, making it easier to extract valuable information from texts. However, be aware that using GraphRAG can be costly, so it's important to read the documentation carefully and start with small tests.
https://github.com/microsoft/graphrag
GraphRAG is a tool that helps extract useful, structured data from unstructured text using large language models (LLMs). It creates a data pipeline to make sense of your private data. To get started, you can use the Solution Accelerator package, which provides a simple way to use GraphRAG with Azure resources. The benefit to you is that GraphRAG can enhance your LLM's ability to understand and reason about your data, making it easier to extract valuable information from texts. However, be aware that using GraphRAG can be costly, so it's important to read the documentation carefully and start with small tests.
https://github.com/microsoft/graphrag
GitHub
GitHub - microsoft/graphrag: A modular graph-based Retrieval-Augmented Generation (RAG) system
A modular graph-based Retrieval-Augmented Generation (RAG) system - microsoft/graphrag
#python
`aisuite` is a tool that makes it easy for developers to use multiple Generative AI models from different providers like OpenAI, Google, and AWS through a single interface. This means you can compare responses from different AI models without changing your code. To use `aisuite`, you need to install it using `pip` and set up API keys for the providers you want to use. It simplifies the process of testing and comparing AI responses, making your development work more efficient and flexible.
https://github.com/andrewyng/aisuite
`aisuite` is a tool that makes it easy for developers to use multiple Generative AI models from different providers like OpenAI, Google, and AWS through a single interface. This means you can compare responses from different AI models without changing your code. To use `aisuite`, you need to install it using `pip` and set up API keys for the providers you want to use. It simplifies the process of testing and comparing AI responses, making your development work more efficient and flexible.
https://github.com/andrewyng/aisuite
GitHub
GitHub - andrewyng/aisuite: Simple, unified interface to multiple Generative AI providers
Simple, unified interface to multiple Generative AI providers - GitHub - andrewyng/aisuite: Simple, unified interface to multiple Generative AI providers
#go #cli #docker #docker_image #explorer #inspector #tui
Dive is a tool that helps you analyze and optimize Docker images. It shows you the contents of each layer in the image, indicates what has changed in each layer, and estimates how much space is wasted. You can use it to build and immediately analyze an image with one command. It also integrates with CI pipelines to ensure your images are efficient. Dive supports multiple container engines like Docker and Podman, and it has customizable key bindings and UI settings. This tool helps you make your Docker images smaller and more efficient, saving space and improving performance.
https://github.com/wagoodman/dive
Dive is a tool that helps you analyze and optimize Docker images. It shows you the contents of each layer in the image, indicates what has changed in each layer, and estimates how much space is wasted. You can use it to build and immediately analyze an image with one command. It also integrates with CI pipelines to ensure your images are efficient. Dive supports multiple container engines like Docker and Podman, and it has customizable key bindings and UI settings. This tool helps you make your Docker images smaller and more efficient, saving space and improving performance.
https://github.com/wagoodman/dive
GitHub
GitHub - wagoodman/dive: A tool for exploring each layer in a docker image
A tool for exploring each layer in a docker image. Contribute to wagoodman/dive development by creating an account on GitHub.
#swift #alamofire #carthage #certificate_pinning #cocoapods #httpurlresponse #networking #parameter_encoding #public_key_pinning #request #response #swift #swift_package_manager #urlrequest #urlsession #xcode
Alamofire is a powerful library for making HTTP requests in Swift. It makes networking easier with its simple and concise syntax. You can write complex requests with features like automatic retry, authentication, and response validation in just a few lines of code. Alamofire supports various platforms including iOS, macOS, tvOS, watchOS, and even Linux and Windows, though with some limitations on the latter. It also integrates well with tools like CocoaPods, Carthage, and the Swift Package Manager for easy installation. Using Alamofire helps you manage network requests efficiently and debug them easily, making your development process faster and more reliable.
https://github.com/Alamofire/Alamofire
Alamofire is a powerful library for making HTTP requests in Swift. It makes networking easier with its simple and concise syntax. You can write complex requests with features like automatic retry, authentication, and response validation in just a few lines of code. Alamofire supports various platforms including iOS, macOS, tvOS, watchOS, and even Linux and Windows, though with some limitations on the latter. It also integrates well with tools like CocoaPods, Carthage, and the Swift Package Manager for easy installation. Using Alamofire helps you manage network requests efficiently and debug them easily, making your development process faster and more reliable.
https://github.com/Alamofire/Alamofire
GitHub
GitHub - Alamofire/Alamofire: Elegant HTTP Networking in Swift
Elegant HTTP Networking in Swift. Contribute to Alamofire/Alamofire development by creating an account on GitHub.
#javascript #bugbounty #exploit_development #exploits #fingerprint #hacktoberfest #nuclei #nuclei_checks #nuclei_templates #security #vulnerability_detection
Nuclei Templates are pre-made scripts used by the Nuclei scanner to find security vulnerabilities in applications. These templates are created and shared by a community of users, making it easier for everyone to identify and fix security issues. You can contribute your own templates, report bugs, or request new features, which helps grow the library of available templates. This community-driven approach ensures that the scanner stays updated and effective, benefiting users by providing a robust tool for enhancing application security. You can also join discussions on GitHub or the Discord community to learn more and share ideas.
https://github.com/projectdiscovery/nuclei-templates
Nuclei Templates are pre-made scripts used by the Nuclei scanner to find security vulnerabilities in applications. These templates are created and shared by a community of users, making it easier for everyone to identify and fix security issues. You can contribute your own templates, report bugs, or request new features, which helps grow the library of available templates. This community-driven approach ensures that the scanner stays updated and effective, benefiting users by providing a robust tool for enhancing application security. You can also join discussions on GitHub or the Discord community to learn more and share ideas.
https://github.com/projectdiscovery/nuclei-templates
GitHub
GitHub - projectdiscovery/nuclei-templates: Community curated list of templates for the nuclei engine to find security vulnerabilities.
Community curated list of templates for the nuclei engine to find security vulnerabilities. - projectdiscovery/nuclei-templates
#html
This repository helps you build and contribute to the Kubernetes website and documentation. You can run the website locally using Hugo or a container runtime, which ensures consistency with the live site. To get started, you need to install tools like npm, Go, Hugo (Extended version), and a container runtime like Docker. You can then clone the repository, fetch necessary dependencies, and run the website locally. This setup allows you to make changes and see them reflected in real-time in your browser. Contributing to the docs is easy; you can fork the repository, make changes, and submit a pull request for review. This process helps ensure high-quality documentation for Kubernetes users.
https://github.com/kubernetes/website
This repository helps you build and contribute to the Kubernetes website and documentation. You can run the website locally using Hugo or a container runtime, which ensures consistency with the live site. To get started, you need to install tools like npm, Go, Hugo (Extended version), and a container runtime like Docker. You can then clone the repository, fetch necessary dependencies, and run the website locally. This setup allows you to make changes and see them reflected in real-time in your browser. Contributing to the docs is easy; you can fork the repository, make changes, and submit a pull request for review. This process helps ensure high-quality documentation for Kubernetes users.
https://github.com/kubernetes/website
GitHub
GitHub - kubernetes/website: Kubernetes website and documentation repo:
Kubernetes website and documentation repo: . Contribute to kubernetes/website development by creating an account on GitHub.
🤮1
#go #casaos #docker #golang #home_automation #home_cloud #home_server #iot #raspberry #self_hosted #vuejs
CasaOS is a personal cloud system that helps you manage your data and devices at home. It's designed to be easy to use, even if you're not tech-savvy. Here are the key benefits It reduces the cost of using cloud services by letting you host your own data center at home.
- **Data Control** It supports various hardware like ZimaBoard, Intel NUC, and Raspberry Pi, and can be installed with just one command.
- **User-Friendly Interface** There's a strong community behind it, offering help and sharing ideas through platforms like Discord.
Overall, CasaOS makes it easy to set up and manage your own personal cloud, giving you more control and savings.
https://github.com/IceWhaleTech/CasaOS
CasaOS is a personal cloud system that helps you manage your data and devices at home. It's designed to be easy to use, even if you're not tech-savvy. Here are the key benefits It reduces the cost of using cloud services by letting you host your own data center at home.
- **Data Control** It supports various hardware like ZimaBoard, Intel NUC, and Raspberry Pi, and can be installed with just one command.
- **User-Friendly Interface** There's a strong community behind it, offering help and sharing ideas through platforms like Discord.
Overall, CasaOS makes it easy to set up and manage your own personal cloud, giving you more control and savings.
https://github.com/IceWhaleTech/CasaOS
GitHub
GitHub - IceWhaleTech/CasaOS: CasaOS - A simple, easy-to-use, elegant open-source Personal Cloud system.
CasaOS - A simple, easy-to-use, elegant open-source Personal Cloud system. - IceWhaleTech/CasaOS
#java #jeepay #xxpay
Jeepay is an open-source payment system designed for internet companies. It supports multiple payment channels, including WeChat Pay, Alipay, and Cloud Flash Pay. Here are the key benefits Jeepay makes it simple to integrate various payment services into your system.
- **Security** The system is designed for high concurrency and distributed deployment.
- **User-Friendly** You can deploy the system quickly using a one-click installation script.
- **Extensive Documentation**: There are detailed guides and SDKs available for easy integration.
Overall, Jeepay simplifies the process of adding payment functionalities to your business, making it more convenient and secure.
https://github.com/jeequan/jeepay
Jeepay is an open-source payment system designed for internet companies. It supports multiple payment channels, including WeChat Pay, Alipay, and Cloud Flash Pay. Here are the key benefits Jeepay makes it simple to integrate various payment services into your system.
- **Security** The system is designed for high concurrency and distributed deployment.
- **User-Friendly** You can deploy the system quickly using a one-click installation script.
- **Extensive Documentation**: There are detailed guides and SDKs available for easy integration.
Overall, Jeepay simplifies the process of adding payment functionalities to your business, making it more convenient and secure.
https://github.com/jeequan/jeepay
GitHub
GitHub - jeequan/jeepay: Jeepay是一套适合互联网企业使用的开源支付系统,支持多渠道服务商和普通商户模式。已对接微信支付,支付宝,云闪付官方接口,支持聚合码支付。
Jeepay是一套适合互联网企业使用的开源支付系统,支持多渠道服务商和普通商户模式。已对接微信支付,支付宝,云闪付官方接口,支持聚合码支付。 - jeequan/jeepay
#jupyter_notebook #deep_learning #machine_learning #python #pytorch
This course, "深入浅出PyTorch" (Thorough PyTorch), is designed to help you learn PyTorch from basics to advanced levels. It covers everything from installing PyTorch, understanding tensors and automatic differentiation, to building and training models, and even deploying them. The course is divided into several chapters, each focusing on different aspects of PyTorch, such as data loading, model construction, loss functions, optimizers, and visualization.
The benefit to you is that you will gain a comprehensive understanding of PyTorch, which is a powerful tool for deep learning. You will learn through both theoretical explanations and practical exercises, including hands-on projects like fashion classification and fruit classification. This will help you develop your programming skills and ability to solve real-world problems using deep learning algorithms. Additionally, the course includes video tutorials and a community-driven approach to learning, making it easier and more engaging.
https://github.com/datawhalechina/thorough-pytorch
This course, "深入浅出PyTorch" (Thorough PyTorch), is designed to help you learn PyTorch from basics to advanced levels. It covers everything from installing PyTorch, understanding tensors and automatic differentiation, to building and training models, and even deploying them. The course is divided into several chapters, each focusing on different aspects of PyTorch, such as data loading, model construction, loss functions, optimizers, and visualization.
The benefit to you is that you will gain a comprehensive understanding of PyTorch, which is a powerful tool for deep learning. You will learn through both theoretical explanations and practical exercises, including hands-on projects like fashion classification and fruit classification. This will help you develop your programming skills and ability to solve real-world problems using deep learning algorithms. Additionally, the course includes video tutorials and a community-driven approach to learning, making it easier and more engaging.
https://github.com/datawhalechina/thorough-pytorch
GitHub
GitHub - datawhalechina/thorough-pytorch: PyTorch入门教程,在线阅读地址:https://datawhalechina.github.io/thorough-pytorch/
PyTorch入门教程,在线阅读地址:https://datawhalechina.github.io/thorough-pytorch/ - datawhalechina/thorough-pytorch
#python
AutoCut is a tool that helps you cut videos using subtitles. Here’s how it works and its benefits
- **Ease of Use** Faster transcription with supported models like `large-v3-turbo`.
- **Flexibility** Edit your subtitles in any Markdown editor, making it easy to select and cut video segments.
Overall, AutoCut simplifies the process of cutting videos by allowing you to work with text files instead of dealing with video editing software.
https://github.com/mli/autocut
AutoCut is a tool that helps you cut videos using subtitles. Here’s how it works and its benefits
- **Ease of Use** Faster transcription with supported models like `large-v3-turbo`.
- **Flexibility** Edit your subtitles in any Markdown editor, making it easy to select and cut video segments.
Overall, AutoCut simplifies the process of cutting videos by allowing you to work with text files instead of dealing with video editing software.
https://github.com/mli/autocut
GitHub
GitHub - mli/autocut: 用文本编辑器剪视频
用文本编辑器剪视频. Contribute to mli/autocut development by creating an account on GitHub.
👍2
#python #backtest #backtesting #broker_trading_platform #quantitative #quantitative_finance #stock #stocks #strategies #strategy
The InStock股票系统 is a powerful tool for stock market analysis and automated trading. Here’s the key information and its benefits
- **Comprehensive Data Analysis** Supports automatic trading with built-in strategies and logs transactions.
- **Efficient Performance** Offers a web-based interface for visualizing results and managing data.
- **Flexibility** Includes features to identify potential buy and sell signals based on technical indicators like MACD, RSI, and KDJ.
Overall, this system is a valuable tool for investors looking to streamline their stock analysis and trading processes.
https://github.com/myhhub/stock
The InStock股票系统 is a powerful tool for stock market analysis and automated trading. Here’s the key information and its benefits
- **Comprehensive Data Analysis** Supports automatic trading with built-in strategies and logs transactions.
- **Efficient Performance** Offers a web-based interface for visualizing results and managing data.
- **Flexibility** Includes features to identify potential buy and sell signals based on technical indicators like MACD, RSI, and KDJ.
Overall, this system is a valuable tool for investors looking to streamline their stock analysis and trading processes.
https://github.com/myhhub/stock
GitHub
GitHub - myhhub/stock: stock股票.获取股票数据,计算股票指标,筹码分布,识别股票形态,综合选股,选股策略,股票验证回测,股票自动交易,支持PC及移动设备。
stock股票.获取股票数据,计算股票指标,筹码分布,识别股票形态,综合选股,选股策略,股票验证回测,股票自动交易,支持PC及移动设备。 - myhhub/stock
#python #ollama #python
The Ollama Python library makes it easy to use Ollama models in your Python projects. To use it, you need to have Ollama installed and running, and then install the library with `pip install ollama`. You can then use simple code to ask questions or generate text using different models. For example, you can ask "Why is the sky blue?" using a specific model like `llama3.2`. The library also supports streaming responses and asynchronous requests, which can be useful for real-time applications. This makes it beneficial for users who want to integrate AI capabilities into their projects quickly and efficiently.
https://github.com/ollama/ollama-python
The Ollama Python library makes it easy to use Ollama models in your Python projects. To use it, you need to have Ollama installed and running, and then install the library with `pip install ollama`. You can then use simple code to ask questions or generate text using different models. For example, you can ask "Why is the sky blue?" using a specific model like `llama3.2`. The library also supports streaming responses and asynchronous requests, which can be useful for real-time applications. This makes it beneficial for users who want to integrate AI capabilities into their projects quickly and efficiently.
https://github.com/ollama/ollama-python
GitHub
GitHub - ollama/ollama-python: Ollama Python library
Ollama Python library. Contribute to ollama/ollama-python development by creating an account on GitHub.
#other #cc_by #collection #computer_science #educational #novice
Joining a community for software developers can greatly benefit your career and personal life. You gain access to a wealth of resources, including tutorials, books, and videos on various programming topics such as algorithms, data structures, security, and more. These resources help you improve your skills, stay updated with industry trends, and enhance your problem-solving abilities. Additionally, you can find guidance on career development, resume building, and remote work opportunities. This community support can give you more control over your life and career by providing the tools and knowledge you need to succeed.
https://github.com/mtdvio/every-programmer-should-know
Joining a community for software developers can greatly benefit your career and personal life. You gain access to a wealth of resources, including tutorials, books, and videos on various programming topics such as algorithms, data structures, security, and more. These resources help you improve your skills, stay updated with industry trends, and enhance your problem-solving abilities. Additionally, you can find guidance on career development, resume building, and remote work opportunities. This community support can give you more control over your life and career by providing the tools and knowledge you need to succeed.
https://github.com/mtdvio/every-programmer-should-know
GitHub
GitHub - mtdvio/every-programmer-should-know: A collection of (mostly) technical things every software developer should know about
A collection of (mostly) technical things every software developer should know about - mtdvio/every-programmer-should-know
#go #cloud #cloud_management #graph #infrastructure_as_code #terraform
Terraform is a tool that helps you build, change, and manage your computer infrastructure safely and efficiently. It uses a simple code-like language to describe your infrastructure, so you can version and share it like any other code. Terraform shows you a plan of what it will do before making changes, avoids surprises, and works efficiently by managing resources in parallel. This reduces human errors and makes complex changes easier. You can learn more through tutorials and guides on the Terraform website, and even get certified to show off your skills. This helps you manage your infrastructure reliably and efficiently.
https://github.com/hashicorp/terraform
Terraform is a tool that helps you build, change, and manage your computer infrastructure safely and efficiently. It uses a simple code-like language to describe your infrastructure, so you can version and share it like any other code. Terraform shows you a plan of what it will do before making changes, avoids surprises, and works efficiently by managing resources in parallel. This reduces human errors and makes complex changes easier. You can learn more through tutorials and guides on the Terraform website, and even get certified to show off your skills. This helps you manage your infrastructure reliably and efficiently.
https://github.com/hashicorp/terraform
GitHub
GitHub - hashicorp/terraform: Terraform enables you to safely and predictably create, change, and improve infrastructure. It is…
Terraform enables you to safely and predictably create, change, and improve infrastructure. It is a source-available tool that codifies APIs into declarative configuration files that can be shared ...