Python Trending Weekly is a curated newsletter by Python猫 that selects the most valuable articles, tutorials, open-source projects, software tools, podcasts, videos, and hot topics from over 250 English and Chinese sources. The newsletter aims to help readers improve their Python skills and increase their income from both professional and side projects. It offers paid subscription options and is available on various platforms like GitHub, WeChat, blogs, email, Telegram, and Twitter. Each issue shares a collection of articles, open-source projects, videos, and books related to Python and technology.

This repository serves as a personal knowledge base for the owner's reference and use. It covers a wide range of topics including cloud-native operations, Kubernetes ecosystem, networking, cloud services, telemetry, CI/CD, electronic engineering, hardware projects, operating systems, homelab setups, high-performance computing applications, openwrt router usage, programming languages, music theory, blockchain, distributed systems principles, and various other knowledge domains. The content is periodically refined and published on the owner's blog for maintenance purposes.

Cherry Studio is a desktop client that supports multiple Large Language Model (LLM) providers, available on Windows, Mac, and Linux. It allows users to create multiple Assistants and topics, use multiple models to answer questions in the same conversation, and supports drag-and-drop sorting, code highlighting, and Mermaid chart. The tool is designed to enhance productivity and streamline the process of interacting with various language models.

This repository is a collection of free resources and a roadmap designed to help individuals learn Machine Learning and Artificial Intelligence concepts by providing key concepts, building blocks, roles, a learning roadmap, courses, certifications, books, tools & frameworks, research blogs, applied blogs, practice problems, communities, YouTube channels, newsletters, and must-read papers. It covers a wide range of topics from supervised learning to MLOps, offering guidance on learning paths, practical experience, and job interview preparation.

Shards is a high-performance, multi-platform, type-safe programming language designed for visual development. It is a dataflow visual programming language that enables building full-fledged apps and games without traditional coding. Shards features automatic type checking, optimized shard implementations for high performance, and an intuitive visual workflow for beginners. The language allows seamless round-trip engineering between code and visual models, empowering users to create multi-platform apps easily. Shards also powers an upcoming AI-powered game creation system, enabling real-time collaboration and game development in a low to no-code environment.

This repository contains course materials for T81 559: Applications of Generative Artificial Intelligence at Washington University in St. Louis. The course covers practical applications of Large Language Models (LLMs) and text-to-image networks using Python. Students learn about generative AI principles, LangChain, Retrieval-Augmented Generation (RAG) model, image generation techniques, fine-tuning neural networks, and prompt engineering. Ideal for students, researchers, and professionals in computer science, the course offers a transformative learning experience in the realm of Generative AI.

The TME-AIX repository is a collaborative workspace dedicated to exploring Telco Media Entertainment use-cases using open source AI capabilities and datasets. It focuses on projects like Revenue Assurance, Service Assurance Predictions, 5G Network Fault Predictions, Sustainability, SecOps-AI, SmartGrid, IoT Security, Customer Relation Management, Anomaly Detection, Starlink Quality Predictions, and NoC AI Augmentation for OSS.

Auto-MD is a Python tool that converts various file types and GitHub repositories into Markdown documents optimized for quick indexing via large language models. It supports multiple file types, processes zip files/folders/individual files and GitHub repositories, generates single or multiple Markdown files, and creates a table of contents and metadata for each processed file.

Databend is an open-source cloud data warehouse built in Rust, offering fast query execution and data ingestion for complex analysis of large datasets. It integrates with major cloud platforms, provides high performance with AI-powered analytics, supports multiple data formats, ensures data integrity with ACID transactions, offers flexible indexing options, and features community-driven development. Users can try Databend through a serverless cloud or Docker installation, and perform tasks such as data import/export, querying semi-structured data, managing users/databases/tables, and utilizing AI functions.

aiwechat-vercel is a tool that integrates AI capabilities into WeChat public accounts using Vercel functions. It requires minimal server setup, low entry barriers, and only needs a domain name that can be bound to Vercel, with almost zero cost. The tool supports various AI models, continuous Q&A sessions, chat functionality, system prompts, and custom commands. It aims to provide a platform for learning and experimentation with AI integration in WeChat public accounts.

LLM Cookbook is a developer-oriented comprehensive guide focusing on LLM for Chinese developers. It covers various aspects from Prompt Engineering to RAG development and model fine-tuning, providing guidance on how to learn and get started with LLM projects in a way suitable for Chinese learners. The project translates and reproduces 11 courses from Professor Andrew Ng's large model series, categorizing them for beginners to systematically learn essential skills and concepts before exploring specific interests. It encourages developers to contribute by replicating unreproduced courses following the format and submitting PRs for review and merging. The project aims to help developers grasp a wide range of skills and concepts related to LLM development, offering both online reading and PDF versions for easy access and learning.

A curated list of data science & AI guided projects to start building your portfolio. The repository contains guided projects covering various topics such as large language models, time series analysis, computer vision, natural language processing (NLP), and data science. Each project provides detailed instructions on how to implement specific tasks using different tools and technologies.

Streamline Analyst is a cutting-edge, open-source application powered by Large Language Models (LLMs) designed to revolutionize data analysis. This Data Analysis Agent effortlessly automates tasks such as data cleaning, preprocessing, and complex operations like identifying target objects, partitioning test sets, and selecting the best-fit models based on your data. With Streamline Analyst, results visualization and evaluation become seamless. It aims to expedite the data analysis process, making it accessible to all, regardless of their expertise in data analysis. The tool is built to empower users to process data and achieve high-quality visualizations with unparalleled efficiency, and to execute high-performance modeling with the best strategies. Future enhancements include Natural Language Processing (NLP), neural networks, and object detection utilizing YOLO, broadening its capabilities to meet diverse data analysis needs.

SciCode is a challenging benchmark designed to evaluate the capabilities of language models (LMs) in generating code for solving realistic scientific research problems. It contains 338 subproblems decomposed from 80 challenging main problems across 16 subdomains from 6 domains. The benchmark offers optional descriptions specifying useful scientific background information and scientist-annotated gold-standard solutions and test cases for evaluation. SciCode demonstrates a realistic workflow of identifying critical science concepts and facts and transforming them into computation and simulation code, aiming to help showcase LLMs' progress towards assisting scientists and contribute to the future building and evaluation of scientific AI.

Telco-AIX is a collaborative experimental workspace dedicated to exploring data-driven decision-making use-cases using open source AI capabilities and open datasets. The repository focuses on projects related to revenue assurance, fraud management, service assurance, latency predictions, 5G network operations, sustainability, energy efficiency, SecOps-AI for networking, AI-powered SmartGrid, IoT perimeter security, anomaly detection, root cause analysis, customer relationship management voice app, Starlink quality of experience predictions, and NoC AI augmentation for OSS.

fastRAG is a research framework designed to build and explore efficient retrieval-augmented generative models. It incorporates state-of-the-art Large Language Models (LLMs) and Information Retrieval to empower researchers and developers with a comprehensive tool-set for advancing retrieval augmented generation. The framework is optimized for Intel hardware, customizable, and includes key features such as optimized RAG pipelines, efficient components, and RAG-efficient components like ColBERT and Fusion-in-Decoder (FiD). fastRAG supports various unique components and backends for running LLMs, making it a versatile tool for research and development in the field of retrieval-augmented generation.

Python Trending Weekly is a curated newsletter by Python猫 that selects the most valuable articles, tutorials, open-source projects, software tools, podcasts, videos, and hot topics from over 250 English and Chinese sources. The newsletter aims to help readers improve their Python skills and increase their income from both professional and side projects. It offers paid subscription options and is available on various platforms like GitHub, WeChat, blogs, email, Telegram, and Twitter. Each issue shares a collection of articles, open-source projects, videos, and books related to Python and technology.

LF AI & Data Landscape is a map to explore open source projects in the AI & Data domains, highlighting companies that are members of LF AI & Data. It showcases members of the Foundation and is modelled after the Cloud Native Computing Foundation landscape. The landscape includes current version, interactive version, new entries, logos, proper SVGs, corrections, external data, best practices badge, non-updated items, license, formats, installation, vulnerability reporting, and adjusting the landscape view.

PythonAgentAI is a program designed to help individuals break into the tech industry and land entry-level software development roles. The program offers a self-paced learning experience with the potential for a starting salary of $70k+. It is an affordable alternative to expensive bootcamps or degrees, with a focus on preparing individuals for the 45,000+ job openings in the market. No prior experience is required, making it accessible to anyone determined to future-proof their career and unlock six-figure potential.

Free AI Tips is a GitHub repository that provides weekly tips on Generative AI and Machine Learning. Users can register to receive these tips for free. The repository aims to offer valuable insights and knowledge in the field of AI and ML to help individuals enhance their skills and stay updated with the latest trends and developments.

The AI Cookbook is a collection of examples and tutorials designed to assist developers in building AI systems. It provides ready-to-use code snippets that can be easily integrated into various projects. The content covers practical guidance on creating AI solutions that are functional in real-world scenarios. The repository aims to support learners, freelancers, and businesses seeking AI expertise by offering valuable resources and insights.

Awesome-Lists is a curated index of selected resources spanning various fields including programming languages and theories, web and frontend development, server-side development and infrastructure, cloud computing and big data, data science and artificial intelligence, product design, etc. It includes articles, books, courses, examples, open-source projects, and more. The repository categorizes resources according to the knowledge system of different domains, aiming to provide valuable and concise material indexes for readers. Users can explore and learn from a wide range of high-quality resources in a systematic way.

awesome-ml-blogs is a curated list of machine learning technical blogs covering a wide range of topics from research to deployment. It includes blogs from big corporations, MLOps startups, data labeling platforms, universities, community content, personal blogs, synthetic data providers, and more. The repository aims to help individuals stay updated with the latest research breakthroughs and practical tutorials in the field of machine learning.

The `Call-for-Reviewers` repository aims to collect the latest 'call for reviewers' links from various top CS/ML/AI conferences/journals. It provides an opportunity for individuals in the computer/ machine learning/ artificial intelligence fields to gain review experience for applying for NIW/H1B/EB1 or enhancing their CV. The repository helps users stay updated with the latest research trends and engage with the academic community.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.