#other #automation #automation_templates #integration #n8n #n8n_automation #n8n_template #no_code_ai #no_code_automation #workflow_automation
You can use a large collection of ready-made automation templates for n8n, an open-source, low-code workflow automation tool that connects over 350 apps. These templates help automate tasks like email labeling, social media posting, document processing, chatbots, and data analysis without needing to build workflows from scratch. This saves you time and effort by letting you quickly implement smart automations for business, marketing, support, and more. n8nās visual editor and AI integrations make it easy to customize workflows, improving your productivity and operational efficiency with minimal coding.
https://github.com/enescingoz/awesome-n8n-templates
You can use a large collection of ready-made automation templates for n8n, an open-source, low-code workflow automation tool that connects over 350 apps. These templates help automate tasks like email labeling, social media posting, document processing, chatbots, and data analysis without needing to build workflows from scratch. This saves you time and effort by letting you quickly implement smart automations for business, marketing, support, and more. n8nās visual editor and AI integrations make it easy to customize workflows, improving your productivity and operational efficiency with minimal coding.
https://github.com/enescingoz/awesome-n8n-templates
GitHub
GitHub - enescingoz/awesome-n8n-templates: Supercharge your workflow automation with this curated collection of n8n templates!ā¦
Supercharge your workflow automation with this curated collection of n8n templates! Instantly connect your favorite apps-like Gmail, Telegram, Google Drive, Slack, and more-with ready-to-use, AI-po...
#other
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
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
GitHub
GitHub - DevCaress/guia-entrevistas-de-programacion
Contribute to DevCaress/guia-entrevistas-de-programacion development by creating an account on GitHub.
#other
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
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
GitHub
GitHub - zakirullin/cognitive-load: š§ Cognitive load is what matters
š§ Cognitive load is what matters. Contribute to zakirullin/cognitive-load development by creating an account on GitHub.
#other #ai #anthropic_claude #awesome #context #mcp #model_context_protocol #servers #tool_use #tools
Model Context Protocol (MCP) is an open standard that lets AI models securely connect to various data sources and tools, like files, databases, APIs, and cloud services, to get real-time, relevant information. This helps AI give more accurate, up-to-date, and context-aware answers, reducing repeated data processing and improving efficiency. MCP also supports automation of complex workflows and integration with many platforms, making AI more powerful and flexible. However, running MCP servers requires careful security measures to avoid risks like unauthorized code execution. Using MCP can save time, reduce costs, and enhance AI capabilities for tasks like chatbots, data analysis, and system control.
https://github.com/appcypher/awesome-mcp-servers
Model Context Protocol (MCP) is an open standard that lets AI models securely connect to various data sources and tools, like files, databases, APIs, and cloud services, to get real-time, relevant information. This helps AI give more accurate, up-to-date, and context-aware answers, reducing repeated data processing and improving efficiency. MCP also supports automation of complex workflows and integration with many platforms, making AI more powerful and flexible. However, running MCP servers requires careful security measures to avoid risks like unauthorized code execution. Using MCP can save time, reduce costs, and enhance AI capabilities for tasks like chatbots, data analysis, and system control.
https://github.com/appcypher/awesome-mcp-servers
GitHub
GitHub - appcypher/awesome-mcp-servers: Awesome MCP Servers - A curated list of Model Context Protocol servers
Awesome MCP Servers - A curated list of Model Context Protocol servers - appcypher/awesome-mcp-servers
#other
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
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
GitHub
GitHub - snarktank/ai-dev-tasks: A simple task management system for managing AI dev agents
A simple task management system for managing AI dev agents - snarktank/ai-dev-tasks
#other
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
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
GitHub
GitHub - linexjlin/GPTs: leaked prompts of GPTs
leaked prompts of GPTs. Contribute to linexjlin/GPTs development by creating an account on GitHub.
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#other #cti #cyberhunter #darkweb #deepweb #threat_intelligence
deepdarkCTI is a free project that collects and shares cyber threat intelligence (CTI) from the deep and dark web, helping you stay aware of hidden cyber threats like stolen data, ransomware, and hacker activities. It gathers information from places like Telegram, Discord, hacker forums, and ransomware sites to provide useful indicators and patterns of cyber attacks. You can join their Telegram group to discuss and suggest new sources or support the project with donations. Using deepdarkCTI helps you detect threats early, improve your cybersecurity decisions, and protect your organization from cyber attacks more effectively.
https://github.com/fastfire/deepdarkCTI
deepdarkCTI is a free project that collects and shares cyber threat intelligence (CTI) from the deep and dark web, helping you stay aware of hidden cyber threats like stolen data, ransomware, and hacker activities. It gathers information from places like Telegram, Discord, hacker forums, and ransomware sites to provide useful indicators and patterns of cyber attacks. You can join their Telegram group to discuss and suggest new sources or support the project with donations. Using deepdarkCTI helps you detect threats early, improve your cybersecurity decisions, and protect your organization from cyber attacks more effectively.
https://github.com/fastfire/deepdarkCTI
GitHub
GitHub - fastfire/deepdarkCTI: Collection of Cyber Threat Intelligence sources from the deep and dark web
Collection of Cyber Threat Intelligence sources from the deep and dark web - fastfire/deepdarkCTI
#other
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
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
GitHub
GitHub - github/copilot-cli: GitHub Copilot CLI brings the power of Copilot coding agent directly to your terminal.
GitHub Copilot CLI brings the power of Copilot coding agent directly to your terminal. - GitHub - github/copilot-cli: GitHub Copilot CLI brings the power of Copilot coding agent directly to your t...
#other
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
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
GitHub
GitHub - MoonshotAI/Kimi-K2: Kimi K2 is the large language model series developed by Moonshot AI team
Kimi K2 is the large language model series developed by Moonshot AI team - MoonshotAI/Kimi-K2
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#other
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
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
GitHub
GitHub - WICG/email-verification-protocol: verified autofill
verified autofill . Contribute to WICG/email-verification-protocol development by creating an account on GitHub.