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Use Karpathy-inspired guidelines in a single CLAUDE.md file to fix Claude's coding flaws like wrong assumptions, overcomplicated code, unnecessary edits, and poor goal-setting. Follow four rules: think explicitly before coding, prioritize simplicity, make only required changes, and use tests for verifiable success. Install via Claude plugin or curl command. You benefit with cleaner, minimal code, fewer errors, proactive questions, and self-correcting AI that delivers precise results faster.
https://github.com/forrestchang/andrej-karpathy-skills
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The Agency offers 51 specialized AI agents across engineering, design, marketing, product, testing, and more, each with unique personalities, workflows, and code examples for tasks like building apps or running campaigns. Copy them to your Claude setup for instant use. This transforms your work by automating repetitive tasks, boosting efficiency by 30-50%, cutting errors and costs, and letting you scale projects fast without hiring—freeing time for creative, high-value goals.
https://github.com/msitarzewski/agency-agents
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Email verification without sending emails streamlines how you confirm your email address online. Instead of clicking links or entering codes sent to your inbox, this new protocol lets your browser handle verification directly while you stay on the website. Your email provider verifies you're the rightful owner through existing login credentials, then sends a secure token back to the site. This eliminates delays, reduces frustration from dropped verification attempts, and protects your privacy by preventing email services from tracking which websites you're using. You get faster, smoother verification while maintaining better control over your personal information.[1][2]
https://github.com/WICG/email-verification-protocol
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Kimi K2 is a powerful AI language model with 1 trillion parameters, designed to understand very long texts and perform complex tasks like coding, reasoning, and using tools autonomously. It excels at writing and debugging code, solving math and science problems, and managing multi-step workflows by calling external tools or APIs automatically. You can access it via an easy-to-use API or deploy it on popular platforms. This means you get a smart assistant that not only answers questions but also acts to complete tasks, making your work faster and more efficient, especially for coding and research projects.
https://github.com/MoonshotAI/Kimi-K2
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GitHub Copilot CLI lets you use AI coding help right in your terminal, so you can build, edit, debug, and understand code by just talking to it naturally. It works with your GitHub account to access your repos, issues, and pull requests without leaving the command line. You stay in full control by approving every action before it runs. It supports Linux, macOS, and Windows, and you install it easily with npm. This tool speeds up coding by bringing AI assistance directly where you work, reducing context switching and making development faster and smoother.
https://github.com/github/copilot-cli
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This collection of leaked GPT prompts offers a wide range of tools and ideas for interacting with AI models. It includes prompts for tasks like writing, coding, humor, and education, which can help users understand how GPT models work and improve their interactions with AI. By using these prompts, users can create more effective and personalized AI experiences, benefiting from the diverse contributions and insights shared by the community. This resource is valuable for both developers and users looking to enhance their AI interactions.
https://github.com/linexjlin/GPTs
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You can use a set of markdown files to guide AI coding assistants step-by-step in building software features. This method breaks down your feature idea into a clear Product Requirement Document (PRD), then into detailed tasks, and finally lets the AI work on each task one at a time while you review and approve progress. This structured workflow helps you keep control, avoid errors, and track progress visually, making AI-assisted development more reliable and manageable. It works with many AI tools and improves the quality and clarity of AI-generated code, saving you time and reducing frustration during complex feature development.
https://github.com/snarktank/ai-dev-tasks
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Cognitive load is the mental effort needed to understand and work with code. Since our brain can only hold about four pieces of information at once, complex code with many conditions, deep inheritance, or too many small modules increases this load, making it harder to understand and maintain. To reduce cognitive load, use clear, meaningful variable names, prefer composition over inheritance, avoid too many tiny modules, and keep interfaces simple. Also, avoid excessive abstractions, tight coupling with frameworks, and overly complex architectures. Lower cognitive load helps you and your team understand code faster, reduce bugs, and be more productive.
https://github.com/zakirullin/cognitive-load
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This guide helps you prepare for software engineering technical interviews by covering key topics like good coding practices (SOLID principles, DRY, Clean Code, Clean Architecture), algorithms and data structures, design patterns, system design, databases, version control, CI/CD, containers, and AI tools. It offers practical resources and examples for many programming languages and frameworks, plus common interview questions for frontend and backend roles. Using this guide improves your coding skills, helps you understand important concepts, and boosts your confidence to perform well in interviews and real projects. It saves you time by gathering essential knowledge and practice materials in one place.
https://github.com/DevCaress/guia-entrevistas-de-programacion
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The FFmpeg School of Assembly Language teaches you how assembly code is written in FFmpeg, helping you understand what happens inside your computer. To join, you should know C programming (especially pointers) and basic high school math. The lessons include assignments and a Discord server for questions. By completing them, you can contribute to FFmpeg, a powerful video processing tool that uses assembly to speed up tasks dramatically—sometimes up to 94 times faster with special instructions like AVX-512. Learning this helps you write highly efficient code for video and multimedia processing, improving performance in real-world applications.
https://github.com/FFmpeg/asm-lessons
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This resource is a huge, well-organized collection of computer vision materials including books, courses, papers, software, datasets, tutorials, and tools. It covers everything from beginner to advanced topics like image processing, object detection, 3D vision, deep learning, and more. You can find free and paid courses from top universities, open-source libraries like OpenCV, pre-trained models, and datasets for practice. This helps you learn computer vision efficiently, find the right tools, and stay updated with the latest research and applications, saving you time and effort in your learning or project development. It’s great for students, researchers, and developers.
https://github.com/jbhuang0604/awesome-computer-vision
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Contains Company Wise Questions sorted based on Frequency and all time
https://github.com/krishnadey30/LeetCode-Questions-CompanyWise