Search Results for "cover"
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42 projects for "cover" with 2 filters applied:
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Qodo Cover
AI tool that generates tests to improve code coverage quickly
Qodo Cover is an open source developer tool designed to automate the creation of unit tests using generative AI, helping teams improve code coverage with minimal manual effort. It operates as a command-line interface and can also be integrated into continuous integration workflows, making it adaptable to different development environments. -
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ChatGPT Shortcut
Curated AI prompt manager with search, sharing, and browser access
ChatGPT-Shortcut, also known as AiShort, is an open source AI prompt management tool designed to help users quickly find and use effective prompts for large language models. It provides a curated collection of prompts that cover many different scenarios, making it easier for users to obtain useful results from AI systems. Prompts can be browsed, searched, and copied with a single click, allowing users to quickly insert them into AI conversations or workflows. ChatGPT-Shortcut includes tagging and filtering features that help users locate relevant prompts efficiently without manually browsing long lists. ... -
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davidondrej-skills
Access to david ondrej's personal agent skills
...It packages repeatable workflows into structured instructions that AI coding, research, and workflow agents can load when needed. The repository is organized under a skills/ folder, with each individual skill living in its own folder and beginning with a SKILL.md file. Its categories cover agent orchestration, skill authoring, research and web work, operations and setup, and thinking or documentation workflows. The skills are meant to act as practical building blocks for code improvement, content preparation, idea research, presentation review, transcript work, and agent coordination. It is useful for people building a personal or team agent stack that needs consistent reusable procedures. -
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DINOv2
PyTorch code and models for the DINOv2 self-supervised learning
...The repository includes code for training, evaluating, and feature extraction, with utilities to run k-NN or linear evaluation baselines to assess representation quality. Pretrained checkpoints cover multiple model sizes so practitioners can trade accuracy for speed and memory depending on their deployment constraints. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Generative AI Use Cases (GenU)
Application implementation with business use cases
...Each example typically includes infrastructure templates, backend services, and application code that show how to integrate generative AI capabilities with other AWS services. These examples cover tasks such as document analysis, conversational assistants, content generation, and knowledge retrieval systems. The repository is intended to serve as both a learning resource and a starting point for developers who want to deploy generative AI solutions using AWS infrastructure. -
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VIPER
AI-powered red team platform for adversary simulation toolkit
...Viper emphasizes ease of use through a graphical interface, allowing users to manage complex operations without relying solely on command-line tools. It includes a large collection of built-in modules that cover multiple stages of the MITRE ATT&CK framework, enabling realistic and structured attack simulations. Viper also incorporates automation features and workflow orchestration, helping teams streamline repetitive tasks and maintain continuous monitoring of target environments. A notable aspect of the project is its integration of a large language model agent, which assists with decision-making, parameter handling, and analysis during operations. -
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Guizang Social Card Skill
Claude Code / Codex skill — generate Xiaohongshu carousels
Guizang Social Card Skill is an AI-agent skill for generating polished social image packages in a Guizang-inspired visual style. It is designed for formats such as Xiaohongshu or Rednote carousels, WeChat Official Account covers, article covers, product update graphics, thumbnails, and screenshot-heavy posts. The skill turns articles, scripts, screenshots, product notes, subtitles, or photos into structured social card outputs. It supports editorial magazine layouts and Swiss-style visual... -
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Claude Code Skills & Plugins Hub
270+ Claude Code plugins with 739 agent skills
...The repository functions as a marketplace-style collection of hundreds of plugins and specialized skills that enable Claude Code to perform complex development, automation, and operational tasks. These plugins cover a wide range of domains including DevOps automation, security testing, API debugging, infrastructure management, and AI workflow orchestration. The project also includes orchestration patterns and best practices that guide how multiple AI agents or tools can collaborate effectively in software development workflows. Developers can install plugins through a package-style plugin system and integrate them with their Claude Code environment using standardized commands. -
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Wondel.ai Skills
Agent skills for Claude Code and other agentskills-compatible agents
Wondel.ai Skills is a repository that provides modular capability packages for AI agents, particularly those operating within Claude Code and similar ecosystems. Each skill represents a structured set of instructions, workflows, and tools that enable agents to perform specific tasks with expertise. These skills can cover a wide range of domains, including development, automation, and problem-solving, allowing agents to extend their capabilities dynamically. The system emphasizes modularity, enabling skills to be reused and combined into more complex workflows. It also supports compatibility across multiple AI tools and platforms, making it a versatile resource for developers. ... -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
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TorchCode
Practice implementing softmax, attention, GPT-2 and more
...It is structured similarly to competitive programming platforms like LeetCode but focuses specifically on tensor operations and deep learning concepts. The platform provides a collection of curated problems that cover fundamental topics such as activation functions, normalization layers, attention mechanisms, and full transformer architectures. It runs in a Jupyter-based environment, allowing users to write, test, and debug their code interactively while receiving immediate feedback. An automated judging system evaluates correctness, gradient flow, and numerical stability, helping users understand both functional and theoretical aspects of their implementations. -
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Python Programming Hub
Learn Python and Machine Learning from scratch
Python Programming Hub repository by Tanu-N-Prabhu is an educational resource designed to help programmers learn Python programming and data science concepts through practical examples and notebooks. The project contains a wide range of tutorials and exercises that cover Python fundamentals, programming concepts, and applied techniques for data analysis and machine learning. Many sections are implemented as Jupyter notebooks, allowing learners to run code interactively while reading explanations of the concepts involved. The repository emphasizes hands-on learning by demonstrating real programming tasks such as data manipulation, statistical analysis, visualization, and automation. ... -
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MLOps Zoomcamp
Free MLOps course from DataTalks.Club
...The course is designed to teach data scientists and engineers how to move machine learning models from experimentation environments into scalable production services. The repository provides lessons, code examples, and assignments that cover the entire MLOps lifecycle, including model training, experiment tracking, deployment, monitoring, and infrastructure management. Students learn to use widely adopted tools such as MLflow, orchestration frameworks, and cloud platforms to manage machine learning pipelines. The curriculum emphasizes hands-on projects so learners gain practical experience building automated ML pipelines and maintaining deployed models. -
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DecryptPrompt
Summarize Prompt & LLM papers, open source data & models
...It serves as a structured knowledge base where developers and researchers can quickly find key papers about topics such as chain-of-thought reasoning, prompt optimization, reasoning frameworks, and model training techniques. The repository organizes research into thematic sections that cover different prompting methodologies and reasoning paradigms used in LLM development. Many of the resources focus on understanding how prompts influence model behavior and how prompting strategies can improve reasoning or efficiency. -
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Deep Learning Is Nothing
Deep learning concepts in an approachable style
...Once the fundamentals are clear, the material extends to CNNs, RNNs, and attention mechanisms, explaining why each architecture suits particular tasks. Practical sections cover data pipelines, regularization, and evaluation, emphasizing reproducibility and debugging techniques. The goal is to replace buzzwords with intuition so learners can reason about architectures and training dynamics with confidence. -
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Machine Learning Foundations
Machine Learning Foundations: Linear Algebra, Calculus, Statistics
...The project focuses on explaining the fundamental mathematical and computational concepts that underpin modern machine learning and artificial intelligence systems. The materials cover essential topics such as linear algebra, calculus, statistics, and probability, which form the theoretical basis of many machine learning algorithms. The repository includes Jupyter notebooks with explanations and examples that demonstrate how these mathematical principles relate to real machine learning applications. Each section introduces theoretical concepts and then illustrates them through practical coding examples to reinforce understanding. ... -
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Data-Science-Interview-Questions-Answers
Curated list of data science interview questions and answers
...The repository focuses on core data science fundamentals rather than acting as a software framework, which makes it especially useful as a study and revision resource. Its content is organized into subject-specific documents that cover machine learning, deep learning, statistics, probability, Python, SQL and databases, and resume-based interview questions. That structure makes it practical for users who want to study by topic, strengthen weak areas, or simulate the range of questions they may encounter in interviews. -
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learning
A log of things I'm learning
...Rather than being a traditional software library, the repository acts as a structured knowledge base documenting the author’s ongoing learning journey across topics such as programming, system design, machine learning, and generative AI. The content is organized into categories that cover both core engineering skills and adjacent technologies, enabling readers to follow a practical roadmap for developing strong technical foundations. The repository emphasizes clear explanations, curated resources, and concise notes designed to help developers learn complex topics efficiently. Because it is updated regularly, it reflects evolving trends in software engineering and emerging technologies such as modern AI systems. -
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Data Science Interviews
Data science interview questions and answers
...The repository organizes questions into different categories including theoretical machine learning concepts, technical programming questions, and probability or statistics problems. Many of the questions cover fundamental machine learning topics such as linear models, decision trees, neural networks, and evaluation metrics. In addition to theoretical questions, the repository also includes practical interview topics related to coding challenges, SQL queries, and algorithmic thinking. -
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AI Engineering Transition Path
Research papers and blogs to transition to AI Engineering
AI Engineering Resources is an open educational repository that compiles research papers, tutorials, and learning materials for software engineers transitioning into artificial intelligence engineering roles. The project organizes resources that cover fundamental topics required to understand modern AI systems, including transformers, vector embeddings, tokenization, infrastructure design, and mixture-of-experts architectures. Instead of presenting isolated tutorials, the repository provides a structured pathway that guides engineers through the technical knowledge needed to build and deploy large language model systems. ... -
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Solon
Java enterprise application development framework
...It advertises large concurrency gains, lower memory use, much faster startup, and dramatically smaller packages while remaining compatible from Java 8 through Java 24. The framework focuses on restrained APIs and an open ecosystem, with modules that cover web, data, cloud, and microservice patterns. Its messaging emphasizes “replaceable Spring” ergonomics—keeping developer familiarity while reducing overhead and improving deployment characteristics. Releases and companion repositories present an active community and production-oriented guidance. The broader Solon org also maintains AI/agent extensions (e.g., MCP components) that complement application development. -
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TigerBot
TigerBot: A multi-language multi-task LLM
...The project focuses on building high-performance models capable of handling both English and Chinese tasks while maintaining strong reasoning and conversational abilities. TigerBot models are based on modern transformer architectures and are trained on large datasets that cover multiple domains and languages. The project provides both base models and chat-optimized variants that can be used for dialogue systems, question answering, and general language understanding tasks. In addition to model weights, the repository includes training scripts, inference tools, and configuration files that allow researchers and developers to reproduce experiments or fine-tune the models for specific applications. -
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AWS Agent Skills
AWS Skills for Agents
...Instead of streaming giant documentation sets or relying on episodic web search, this project compresses AWS best practices, usage patterns, edge cases, and real-world engineering guides into pre-structured skill definitions that are token-efficient and tailored for reasoning. The skills cover critical AWS services such as IAM, Lambda, DynamoDB, S3, API Gateway, EKS, and many more, letting agents offer actionable advice on infrastructure as code, debugging, security configurations, and architectural workflows. Skills are kept up to date with weekly documentation checks, ensuring they reflect current AWS patterns and service changes. -
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Easy-Vibe
Tutorial on Product Prototype, AI Capability Integration
...Instead of focusing only on theoretical concepts, Easy-Vibe emphasizes practical, step-by-step tutorials that demonstrate how to transform product ideas into working software using modern AI coding tools and development workflows. The learning path is divided into progressive stages that cover beginner concepts, full-stack development, and advanced multi-platform application development. Throughout the curriculum, learners explore topics such as prompt engineering, AI tool integration, product prototyping, and deployment strategies for AI-enabled applications. -
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AutoAgent
AutoAgent: Fully-Automated and Zero-Code LLM Agent Framework
AutoAgent is a fully automated, zero-code LLM agent framework that lets users create agents and workflows using natural language instead of manual coding and configuration. It is structured around modes that cover both “use” and “build” scenarios: a user mode for running a ready-made multi-agent research assistant, plus editors for creating individual agents or multi-agent workflows from conversational requirements. The framework emphasizes self-managing workflow generation, where it can infer steps, refine them, and adapt plans even when users cannot fully specify implementation details up front. ... -
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LLM Course
Course to get into Large Language Models (LLMs)
...Learners get exposure to multiple adaptation strategies—LoRA/QLoRA, instruction fine-tuning, and alignment techniques—so they can choose approaches that fit their hardware and budgets. The materials also cover inference optimization and quantization to make serving LLMs feasible on commodity GPUs or even CPUs, which is crucial for side projects and startups. Evaluation is treated as a first-class topic, with examples of automatic and human-in-the-loop methods to catch regressions and verify quality beyond simple loss values. By the end, students have a mental model and a practical toolkit for iterating on datasets, training configs, etc.