Top GitHub Projects of 06/09/2023: Discover the Most Popular Repositories of Today!
Projects that have already appeared in previous Github ranking lists will not display details, please refer to previous lists for information.
Rank #1 MCRcortex/nekodetector
https://github.com/MCRcortex/nekodetector
Nekoclient infection detector
Language: Java
Stars: 369(63 stars today) Forks:46
The Neko Detector is a tool designed to detect the presence of the fractureiser malware, which infects any jar file it can find. The tool scans every jar file in a computer and checks for signs of infection, helping users to determine if they have been infected. This tool can be applied in various fields, including cybersecurity and malware detection. It has commercial applications in industries such as finance, healthcare, and government, where data security is of utmost importance. The tool is easy to use and can be run on both Windows and Linux operating systems. By using the Neko Detector, users can protect their systems from the fractureiser malware and prevent potential data breaches.
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Rank #2 intel/intel-one-mono
https://github.com/intel/intel-one-mono
Intel One Mono font repository
Language:
Stars: Star(307 stars today) Forks:40
The Intel One Mono Typeface is a font family designed with the needs of developers in mind, with a focus on clarity and legibility. It was created by Frere-Jones Type in partnership with the Intel Brand Team and VMLY&R, with feedback from a panel of low-vision and legally blind developers at each stage of design. The font family includes four weights — Light, Regular, Medium, and Bold — with matching italics, and covers a wide range of over 200 languages using the Latin script. It is available for free under an open-source font license.
This project can be applied in various fields, including software development, web design, and graphic design. It has commercial applications in industries such as technology, advertising, and publishing, where the legibility of text is crucial. The font family is easy to use and can be installed on desktop and mobile devices, as well as used for web design. The Intel One Mono Typeface includes several additional features, such as raised colon, language support, and superior/superscript and inferior/subscript figures, making it a versatile and practical choice for developers and designers alike.
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Rank #3 xinyu1205/Recognize_Anything-Tag2Text
https://github.com/xinyu1205/Recognize_Anything-Tag2Text
Code for the Recognize Anything Model and Tag2Text Model
Language: Python
Stars: 508(105 stars today) Forks:41
The Recognize Anything project is an official PyTorch implementation of two models: the Recognize Anything Model (RAM) and the Tag2Text Model. RAM is a powerful image tagging model that can recognize any common category with high accuracy, while Tag2Text is an efficient and controllable vision-language model with tagging guidance. When combined with localization models, such as Grounded-SAM, Tag2Text and RAM form a strong and general pipeline for visual semantic analysis.
This project can be applied in various fields, including computer vision, machine learning, and natural language processing. It has commercial applications in industries such as e-commerce, advertising, and social media, where image recognition and tagging are crucial for effective marketing and user engagement. The models are easy to use and can be implemented in various applications, such as video question answering tools and multimodal large models.
The Recognize Anything project offers several helpful tutorials and resources, including a web demo for RAM and Tag2Text, access to the RAM and Tag2Text homepages, and arXiv papers for both models. The project highlights the importance of recognition and localization in computer vision tasks and demonstrates the exceptional recognition abilities of RAM and the superior image tag recognition ability of Tag2Text. With its open-set capability, RAM is feasible to recognize any common category, making it a valuable tool for a wide range of applications.
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Rank #4 TransformerOptimus/SuperAGI
https://github.com/TransformerOptimus/SuperAGI
<⚡️> SuperAGI — A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.
Language: Python
Stars: 6,428(606 stars today) Forks:579
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Rank #5 Hufe921/canvas-editor
https://github.com/Hufe921/canvas-editor
rich text editor by canvas/svg
Language: TypeScript
Stars: 1,003(117 stars today) Forks:129
The Canvas Editor project is a rich text editor that uses canvas/svg to render text. The project offers several features, including a render layer by svg that is under development, and an export pdf feature that is available now. The editor is easy to use and can be installed with npm.
This project can be applied in various fields, including web development, content creation, and document management. It has commercial applications in industries such as publishing, education, and legal services, where document creation and management are crucial. The editor is designed to improve list and title formatting, improve performance, and provide control rules and table paging. It also offers CRDT functionality, which enables collaborative editing in real-time.
The Canvas Editor project is still under development, with several next features planned, such as improving list and title formatting, improving performance, and providing table paging. The editor is highly customizable and can be integrated into various applications. The project offers a helpful documentation page, as well as a snapshot of the editor in action. With its rich text editing capabilities and customizable features, the Canvas Editor project is a valuable tool for various applications.
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Rank #6 IsaacMarovitz/Whisky
https://github.com/IsaacMarovitz/Whisky
A modern Wine wrapper for macOS built with SwiftUI
Language: Swift
Stars: 1,679(381 stars today) Forks:36
The Whisky project is a graphical wrapper for Wine built in native SwiftUI, providing a clean and easy-to-use interface for macOS users. The project allows users to create and manage bottles, install and run Windows apps and games, and unlock the full potential of their Mac with no technical knowledge required. Whisky is built on top of CrossOver 22.1.1 and Apple’s own Game Porting Toolkit, making it a powerful tool for running Windows apps and games on macOS.
This project can be applied in various fields, including software development, gaming, and multimedia production. It has commercial applications in industries such as game development, video editing, and graphic design, where cross-platform compatibility is essential. The Whisky project offers a familiar UI that integrates seamlessly with macOS, making it easy for users to manage bottles, debug and profile with ease.
The project is highly customizable and offers a helpful FAQ section to address common issues. It is built on top of CrossOver 22.1.1 and Apple’s own Game Porting Toolkit, ensuring high performance and reliability. With its clean interface and powerful functionality, the Whisky project is a valuable tool for macOS users who need to run Windows apps and games.
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Rank #7 apple/homebrew-apple
https://github.com/apple/homebrew-apple
Language: Ruby
Stars: 1,940(1,054 stars today) Forks:54
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Rank #8 fractureiser-investigation/fractureiser
https://github.com/fractureiser-investigation/fractureiser
Information about the fractureiser malware
Language: Java
Stars: 785(267 stars today) Forks:56
The Fractureiser project is a malware investigation and mitigation effort focused on a virus found in several Minecraft projects uploaded to CurseForge and CraftBukkit’s dev website. The malware, known as fractureiser, is embedded in multiple mods, some of which were added to highly popular modpacks. The malware is only known to target Windows and Linux systems and can be incredibly dangerous if left unchecked.
This project can be applied in the field of cybersecurity and malware investigation. It has commercial applications in industries such as software development, gaming, and content creation, where the security of digital assets is critical. The Fractureiser project provides surface-level information on the malware’s effects, steps to check if you have it and how to remove it, and a FAQ for mod players. For those who wish to dig deeper, the project offers an event timeline and technical breakdown of the malware.
The project is still in progress, with ongoing efforts to refine user-facing documentation and investigate preventive measures and solutions for future problems of this scale. The Fractureiser Mitigation Team also encourages anyone with files relevant to this malware to upload them to https://wormhole.app and email the URL to fractureiser.investigation@opayq.com.
Overall, the Fractureiser project is a valuable resource for those affected by the malware and those interested in cybersecurity and malware investigation.
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Rank #9 ruanyf/weekly
https://github.com/ruanyf/weekly
科技爱好者周刊，每周五发布
Language:
Stars: Star(115 stars today) Forks:2,270
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Rank #10 bregman-arie/devops-exercises
https://github.com/bregman-arie/devops-exercises
Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, NoSQL, Azure, GCP, DNS, Elastic, Network, Virtualization. DevOps Interview Questions
Language: Python
Stars: 43,832(77 stars today) Forks:9,787
The Technical Interview Questions and Exercises project is a collection of questions and exercises on various technical topics, sometimes related to DevOps and SRE. The repository currently contains over 2600 exercises and questions covering a wide range of topics, including DevOps, Git, Network, Hardware, Kubernetes, Software Development, Python, Go, Perl, Regex, Cloud, AWS, Azure, Google Cloud Platform, OpenStack, Operating System, Linux, Virtualization, DNS, Shell Scripting, Databases, SQL, Mongo, Testing, Big Data, CI/CD, Certificates, Containers, OpenShift, Storage, Terraform, Puppet, Distributed, Ansible, Observability, Prometheus, Circle CI, Grafana, Argo, Soft Skills, Security, System Design, Chaos Engineering, Misc, Elastic, and Kafka.
This project can be applied in various fields, including software development, IT operations, and network engineering. It has commercial applications in industries such as technology, finance, and healthcare, where technical expertise is critical. The Technical Interview Questions and Exercises project offers a helpful resource for those preparing for technical interviews or looking to expand their knowledge in various technical topics.
The project is highly customizable, and users can add more exercises by submitting pull requests. The repository also provides contribution guidelines to ensure that the added exercises meet the project’s standards. The Technical Interview Questions and Exercises project is a valuable resource for those looking to improve their technical skills and knowledge in various fields.
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Rank #11 hncboy/chatgpt-web-java
https://github.com/hncboy/chatgpt-web-java
Java 开发的 ChatGPT 的项目，基于 Spring Boot 3 和 JDK 17，支持 AccessToken 和 ApiKey 模式。
Language: Java
Stars: 1,102(144 stars today) Forks:379
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Rank #12 InternLM/InternLM-techreport
https://github.com/InternLM/InternLM-techreport
Language:
Stars: Star(51 stars today) Forks:15
The InternLM project is a multilingual large language model jointly developed by Shanghai AI Lab and SenseTime, in collaboration with several universities. The project presents a foundational language model with 104B parameters that are pre-trained on a large corpus with 1.6T tokens. The model is fine-tuned to align with human preferences and demonstrates outstanding performances on comprehensive exams, including MMLU, AGIEval, C-Eval, and GAOKAO-Bench, without resorting to external tools. The project achieves state-of-the-art performance in multiple aspects, including knowledge understanding, reading comprehension, mathematics, and coding.
This project can be applied in various fields, including natural language processing, machine learning, and artificial intelligence. It has commercial applications in industries such as technology, finance, and healthcare, where language processing and understanding are critical. The InternLM project offers a valuable resource for those looking to improve their language understanding and processing capabilities.
The project is highly customizable, and users can access the technical report and download the PDF file. The InternLM project is a significant contribution to the field of language processing and demonstrates the potential of large language models in various applications.
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Rank #13 BradyFU/Awesome-Multimodal-Large-Language-Models
https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models
Latest Papers and Datasets on Multimodal Large Language Models
Language:
Stars: Star(37 stars today) Forks:33
The Awesome-Multimodal-Large-Language-Models project is a curated list of Multimodal Large Language Models (MLLM), including datasets, multimodal instruction tuning, multimodal in-context learning, multimodal chain-of-thought, LLM-aided visual reasoning, foundation models, and others. The project provides a comprehensive list of papers, code, and demos related to MLLMs. The list is updated in real-time, and a survey paper on MLLM is being prepared and will be released soon.
This project can be applied in various fields, including natural language processing, computer vision, and machine learning. It has commercial applications in industries such as technology, finance, and healthcare, where language processing and understanding are critical. The Awesome-Multimodal-Large-Language-Models project offers a valuable resource for those looking to improve their understanding and processing capabilities of multimodal language models.
The project is highly customizable, and users can access the curated list of papers, code, and demos related to MLLMs. The Awesome-Multimodal-Large-Language-Models project is a significant contribution to the field of language processing and demonstrates the potential of multimodal language models in various applications.
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Rank #14 SysCV/sam-hq
https://github.com/SysCV/sam-hq
Segment Anything in High Quality
Language:
Stars: Star(206 stars today) Forks:32
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Rank #15 ansible-semaphore/semaphore
https://github.com/ansible-semaphore/semaphore
Modern UI for Ansible
Language: Go
Stars: 6,600(70 stars today) Forks:770
Ansible Semaphore is a modern UI for Ansible that lets users easily run Ansible playbooks, get notifications about fails, and control access to the deployment system. It offers a user-friendly interface for Ansible, a popular open-source automation tool, and is designed to simplify the deployment process. The project provides installation guides for Snap and Docker, making it easy for users to set up and use Ansible Semaphore.
This project can be applied in various fields, including IT operations, DevOps, and software development. It has commercial applications in industries such as technology, finance, and healthcare, where automation and deployment are critical. Ansible Semaphore offers a valuable resource for those looking to simplify their deployment process and improve their automation capabilities.
The project is highly customizable, and users can contribute to the project’s development by submitting pull requests and UX reviews. The Ansible Semaphore project is a significant contribution to the field of automation and deployment and demonstrates the potential of user-friendly interfaces for popular open-source tools.
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Rank #16 camenduru/text-to-video-synthesis-colab
https://github.com/camenduru/text-to-video-synthesis-colab
Text To Video Synthesis Colab
Language: Jupyter Notebook
Stars: 701(185 stars today) Forks:57
The text-to-video-synthesis-colab project provides a collection of Colab notebooks that allow users to generate videos from text using pre-trained models. The project includes various pre-trained models such as potat1, zeroscope_v1, ms_1_7b, and animov, which are available on the Hugging Face model hub. The notebooks provide a user-friendly interface for generating videos from text, making it easy for users to create videos without any prior knowledge of video synthesis.
This project can be applied in various fields, including video production, advertising, and entertainment. It has commercial applications in industries such as media, marketing, and film, where video production and content creation are critical. The text-to-video-synthesis-colab project offers a valuable resource for those looking to generate videos from text and improve their video synthesis capabilities.
The project is highly customizable, and users can use their own text and fine-tune the pre-trained models to generate videos that meet their specific requirements. The text-to-video-synthesis-colab project is a significant contribution to the field of video synthesis and demonstrates the potential of user-friendly interfaces for popular pre-trained models.
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Rank #17 Vahe1994/SpQR
https://github.com/Vahe1994/SpQR
Language: Python
Stars: 190(27 stars today) Forks:17
The SPQR model compression project provides a quantization algorithm and model evaluation code for a sparse-quantized representation method for near-lossless language model (LLM) weight compression. The project is based on the research paper “SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression.” The project offers a method for compressing LLMs, which are critical for natural language processing tasks such as text generation and sentiment analysis.
This project can be applied in various fields, including natural language processing, machine learning, and artificial intelligence. It has commercial applications in industries such as technology, finance, and healthcare, where language processing and understanding are critical. The SPQR model compression project offers a valuable resource for those looking to compress LLMs and improve their language processing capabilities.
The project is highly customizable, and users can adjust the compression parameters to achieve a balance between compression and loss. The SPQR model compression project is a significant contribution to the field of LLM compression and demonstrates the potential of sparse-quantized representations for near-lossless compression of LLM weights.
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Rank #18 fiatrete/OpenDAN-Personal-AI-OS
https://github.com/fiatrete/OpenDAN-Personal-AI-OS
OpenDAN is an open source Personal AI OS , which consolidates various AI modules in one place for your personal use.
Language: Python
Stars: 804(219 stars today) Forks:43
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Rank #19 pittcsc/Summer2024-Internships
https://github.com/pittcsc/Summer2024-Internships
Collection of Summer 2023 & Summer 2024 tech internships!
Language:
Stars: Star(170 stars today) Forks:1,868
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Rank #20 datawhalechina/prompt-engineering-for-developers
https://github.com/datawhalechina/prompt-engineering-for-developers
吴恩达大模型系列课程中文版，包括《Prompt Engineering》、《Building System》和《LangChain》
Language: Jupyter Notebook
Stars: 4,137(419 stars today) Forks:465
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Rank #21 ggerganov/ggml
https://github.com/ggerganov/ggml
Tensor library for machine learning
Language: C
Stars: 3,840(161 stars today) Forks:289
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Rank #22 reactive-python/reactpy
https://github.com/reactive-python/reactpy
It’s React, but in Python
Language: Python
Stars: 4,226(929 stars today) Forks:159
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Rank #23 Cyfrin/foundry-full-course-f23
https://github.com/Cyfrin/foundry-full-course-f23
Language:
Stars: Star(57 stars today) Forks:50
The Blockchain Developer, Smart Contract, & Solidity Course is a comprehensive course that covers the basics of blockchain technology, smart contracts, and Solidity programming language. The course is designed for beginners to experts and is powered by AI. The Foundry Edition 2023 includes 15 lessons, covering topics such as blockchain basics, smart contracts, Remix Simple Storage, Remix Storage Factory, Remix Fund Me, AI prompting, and Foundry Simple Storage.
This course can be applied in various fields, including blockchain development, smart contract development, and cryptocurrency. It has commercial applications in industries such as finance, healthcare, and supply chain management, where blockchain technology is critical. The Blockchain Developer, Smart Contract, & Solidity Course offers a valuable resource for those looking to learn blockchain development and improve their programming skills.
The course provides recommended tools, testnet faucets, and resources for the course, making it easy for users to get started with blockchain development. The course also offers bonus NFTs, which can be used for commercial applications such as digital asset management and ownership tracking.
The Blockchain Developer, Smart Contract, & Solidity Course is a significant contribution to the field of blockchain development and demonstrates the potential of AI-powered education for complex technical fields.
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Rank #24 jonasschmedtmann/ultimate-react-course
https://github.com/jonasschmedtmann/ultimate-react-course
Starter files, final projects, and FAQ for my Ultimate React course
Language: JavaScript
Stars: 428(95 stars today) Forks:102
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Rank #25 ciaochaos/qrbtf
https://github.com/ciaochaos/qrbtf
An art QR code (qrcode) beautifier. 艺术二维码生成器。https://qrbtf.com
Language: JavaScript
Stars: 3,556(501 stars today) Forks:314
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