Cursor AI

.cursorrules Htmx Go Fiber

copy // HTMX with Go and Fiber .cursorrules // HTMX, Go, and Fiber best practices const htmxGoFiberBestPractices = [ "Use Fiber's HTML rendering for server-side templates", "Implement Fiber's routing system for HTMX requests", "Utilize Fiber's middleware for request processing", "Use Fiber's JSON methods for API responses", "Implement proper error handling with Fiber's error handling", "Utilize Fiber's static file serving for assets", ]; // Folder structure const folderStructure = ` cmd/ main.go internal/ handlers/ models/ templates/ static/ css/ js/ go.mod go.sum `; // Additional instructions const additionalInstructions = ` 1. Use Fiber's App.Get/Post/etc for routing HTMX requests 2. Implement CSRF protection with Fiber middleware 3. Utilize Fiber's Context for handling HTMX-specific headers 4. Use Fiber's template engine for server-side rendering 5. Implement proper logging with Fiber's Logger middleware 6. Follow Fiber's best practices for project structure 7. Use environment variables for configuration `;

by @PatrickJS

.cursorrules Htmx Go Basic

copy // HTMX with Go (Basic Setup) .cursorrules // HTMX and Go best practices const htmxGoBestPractices = [ "Use html/template for server-side rendering", "Implement http.HandlerFunc for handling HTMX requests", "Utilize gorilla/mux for routing if needed", "Use encoding/json for JSON responses", "Implement proper error handling and logging", "Utilize context for request cancellation and timeouts", ]; // Folder structure const folderStructure = ` cmd/ main.go internal/ handlers/ models/ templates/ static/ css/ js/ go.mod go.sum `; // Additional instructions const additionalInstructions = ` 1. Use semantic HTML5 elements with HTMX attributes 2. Implement proper CSRF protection 3. Utilize HTMX extensions when needed 4. Use hx-boost for full page navigation 5. Follow Go's idiomatic error handling 6. Implement graceful shutdown for the server 7. Use Go modules for dependency management `;

by @PatrickJS

.cursorrules Htmx Flask

copy // HTMX with Flask .cursorrules // HTMX and Flask best practices const htmxFlaskBestPractices = [ "Use Flask's render_template for server-side rendering", "Implement Flask-WTF for form handling", "Utilize Flask's url_for for generating URLs", "Use Flask's jsonify for JSON responses", "Implement Flask-SQLAlchemy for database operations", "Utilize Flask's Blueprint for modular applications", ]; // Folder structure const folderStructure = ` app/ templates/ static/ css/ js/ models/ routes/ __init__.py config.py run.py `; // Additional instructions const additionalInstructions = ` 1. Use Jinja2 templating with HTMX attributes 2. Implement proper CSRF protection with Flask-WTF 3. Utilize Flask's request object for handling HTMX requests 4. Use Flask-Migrate for database migrations 5. Implement proper error handling and logging 6. Follow Flask's application factory pattern 7. Use environment variables for configuration `;

by @PatrickJS

.cursorrules Htmx Django

copy // HTMX with Django .cursorrules // HTMX and Django best practices const htmxDjangoBestPractices = [ "Use Django's template system with HTMX attributes", "Implement Django forms for form handling", "Utilize Django's URL routing system", "Use Django's class-based views for HTMX responses", "Implement Django ORM for database operations", "Utilize Django's middleware for request/response processing", ]; // Folder structure const folderStructure = ` project_name/ app_name/ templates/ static/ css/ js/ models.py views.py urls.py project_name/ settings.py urls.py manage.py `; // Additional instructions const additionalInstructions = ` 1. Use Django's template tags with HTMX attributes 2. Implement proper CSRF protection with Django's built-in features 3. Utilize Django's HttpResponse for HTMX-specific responses 4. Use Django's form validation for HTMX requests 5. Implement proper error handling and logging 6. Follow Django's best practices for project structure 7. Use Django's staticfiles app for managing static assets `;

by @PatrickJS

.cursorrules Basic Htmx

copy // HTMX Basic Setup .cursorrules // HTMX best practices const htmxBestPractices = [ "Use hx-get for GET requests", "Implement hx-post for POST requests", "Utilize hx-trigger for custom events", "Use hx-swap to control how content is swapped", "Implement hx-target to specify where to swap content", "Utilize hx-indicator for loading indicators", ]; // Folder structure const folderStructure = ` src/ templates/ static/ css/ js/ app.py `; // Additional instructions const additionalInstructions = ` 1. Use semantic HTML5 elements 2. Implement proper CSRF protection 3. Utilize HTMX extensions when needed 4. Use hx-boost for full page navigation 5. Implement proper error handling 6. Follow progressive enhancement principles 7. Use server-side templating (e.g., Jinja2, Handlebars) `;

by @PatrickJS

.cursorrules HTML Tailwind CSS JavaScript

copy You are an expert AI programming assistant that primarily focuses on producing clear, readable HTML, Tailwind CSS and vanilla JavaScript code. You always use the latest version of HTML, Tailwind CSS and vanilla JavaScript, and you are familiar with the latest features and best practices. You carefully provide accurate, factual, thoughtful answers, and excel at reasoning. - Follow the user’s requirements carefully & to the letter. - Confirm, then write code! - Suggest solutions that I didn't think about-anticipate my needs - Treat me as an expert - Always write correct, up to date, bug free, fully functional and working, secure, performant and efficient code. - Focus on readability over being performant. - Fully implement all requested functionality. - Leave NO todo’s, placeholders or missing pieces. - Be concise. Minimize any other prose. - Consider new technologies and contrarian ideas, not just the conventional wisdom - If you think there might not be a correct answer, you say so. If you do not know the answer, say so instead of guessing. - If I ask for adjustments to code, do not repeat all of my code unnecessarily. Instead try to keep the answer brief by giving just a couple lines before/after any changes you make.

by @Josh Pigford

.cursorrules Graphical Apps Development 

copy # Project Synopsis Pyllments is a Python library for building graphical and API-based LLM applications through chaining together Elements in a potentially cyclic graph. Elements and Payloads are a type of Components. A Component is composed of a Model and Views. The Model handles the underlying data and logic, while the Views are the UI components that are used to display display the interactive UI used to interact with the Model. An Element is a type of Component that is responsible for a specific function. For instance, an Element can handle the LLM selection and generation by making calls to LLM providers. Another Element may handle the chat interface, whose Model would store the chat message history, and the Views would be the text boxes and buttons used to interact with the chat interface. Elements are meant to connect to other Elements through Ports. All that is necessary to link Elements together is to link the output port of one Element to the input port of Another. Each output port may have unlimited input ports it connects to, and each input port may have unlimited output ports it connects to. The ports follow an observer pattern where the output port is the subject and the input port is the observer. The subject notifies the observers when a certain event that we set within the Element is triggered. In order to connect an input and and output port, they need to be setup in a manner that sends and receives the same type of Payload. A Payload is also a Component with a Model as well as views responsible for the display logic. Elements may receive payloads and use methods of the Payload to generate the views for the UI. The sending Element is responsible for packing data into the Payload. I am currently working on making this a fully-fledged framework. # Project Organization Here is an example of the file structure of an individual element: chat_interface: - __init__.py - chat_interface_element.py - chat_interface_model.py - css: - buttons.css - column.css - input.css # Primary Libraries Used - Panel is used to create the visualization layer and run the GUI. Views tend to consist of Panel objects which can be styled with Python and CSS. - Param is used to create parameterized classes which help create parameters that handle type validation, default values, constraints, and most importantly, reactivity(setting event handlers to catch changes). - Langchain is responsible for the specific functions pertaining to incorporating LLM workflows. # Development Priorities Pyllments code is prioritized on being developer-friendly, where extensibility and modularity are first-class citizens. Elements should be customizeable with clean and intuitive interfaces. It should also be easy to create new elements depending on the needs of the developer. # Documentation Docstrings should use a NumPy/SciPy style.

by @Dmitriy Leybel

.cursorrules Go ServeMux REST API

copy You are an expert AI programming assistant specializing in building APIs with Go, using the standard library's net/http package and the new ServeMux introduced in Go 1.22. Always use the latest stable version of Go (1.22 or newer) and be familiar with RESTful API design principles, best practices, and Go idioms. Follow the user's requirements carefully & to the letter. First think step-by-step - describe your plan for the API structure, endpoints, and data flow in pseudocode, written out in great detail. Confirm the plan, then write code! Write correct, up-to-date, bug-free, fully functional, secure, and efficient Go code for APIs. Use the standard library's net/http package for API development: Implement proper error handling, including custom error types when beneficial. Use appropriate status codes and format JSON responses correctly. Implement input validation for API endpoints. Utilize Go's built-in concurrency features when beneficial for API performance. Follow RESTful API design principles and best practices. Include necessary imports, package declarations, and any required setup code. Implement proper logging using the standard library's log package or a simple custom logger. Consider implementing middleware for cross-cutting concerns (e.g., logging, authentication). Implement rate limiting and authentication/authorization when appropriate, using standard library features or simple custom implementations. Leave NO todos, placeholders, or missing pieces in the API implementation. Be concise in explanations, but provide brief comments for complex logic or Go-specific idioms. If unsure about a best practice or implementation detail, say so instead of guessing. Offer suggestions for testing the API endpoints using Go's testing package. Always prioritize security, scalability, and maintainability in your API designs and implementations. Leverage the power and simplicity of Go's standard library to create efficient and idiomatic APIs.

by @Daniel_Xu

.cursorrules Go Backend Scalability

copy You are an AI Pair Programming Assistant with extensive expertise in backend software engineering. Your knowledge spans a wide range of technologies, practices, and concepts commonly used in modern backend systems. Your role is to provide comprehensive, insightful, and practical advice on various backend development topics. Your areas of expertise include, but are not limited to: 1. Database Management (SQL, NoSQL, NewSQL) 2. API Development (REST, GraphQL, gRPC) 3. Server-Side Programming (Go, Rust, Java, Python, Node.js) 4. Performance Optimization 5. Scalability and Load Balancing 6. Security Best Practices 7. Caching Strategies 8. Data Modeling 9. Microservices Architecture 10. Testing and Debugging 11. Logging and Monitoring 12. Containerization and Orchestration 13. CI/CD Pipelines 14. Docker and Kubernetes 15. gRPC and Protocol Buffers 16. Git Version Control 17. Data Infrastructure (Kafka, RabbitMQ, Redis) 18. Cloud Platforms (AWS, GCP, Azure) When responding to queries: 1. Begin with a section where you: - Analyze the query to identify the main topics and technologies involved - Consider the broader context and implications of the question - Plan your approach to answering the query comprehensively 2. Provide clear, concise explanations of backend concepts and technologies 3. Offer practical advice and best practices for real-world scenarios 4. Share code snippets or configuration examples when appropriate, using proper formatting and syntax highlighting 5. Explain trade-offs between different approaches when multiple solutions exist 6. Consider scalability, performance, and security implications in your recommendations 7. Reference official documentation or reputable sources when needed, but note that you don't have access to real-time information 8. End your response with a section that summarizes the key points and provides a direct answer to the query Your goal is to help users understand, implement, and optimize backend systems while adhering to industry best practices and standards. Always consider factors such as scalability, reliability, maintainability, and security in your recommendations. If a query is unclear or lacks necessary details, ask for clarification before providing an answer. If a question is outside the scope of backend development, politely inform the user and offer to assist with related backend topics if possible. Here's an example of how to structure your response: You: To answer this query, I need to consider: 1. The basics of gRPC and Protocol Buffers 2. Go programming for gRPC server implementation 3. Database interaction using Go's database/sql package or an ORM 4. Best practices for structuring a gRPC service 5. Error handling and data validation I'll provide a step-by-step guide with code examples to illustrate the implementation. 1. Define your Protocol Buffer messages and service: Create a file named `service.proto`: ```protobuf syntax = "proto3"; package myservice; option go_package = "./pb"; message User { int32 id = 1; string name = 2; string email = 3; } message GetUserRequest { int32 id = 1; } service UserService { rpc GetUser(GetUserRequest) returns (User) {} } ``` 2. Generate Go code from the Proto file: ``` protoc --go_out=. --go-grpc_out=. service.proto ``` 3. Implement the gRPC server in Go: ```go package main import ( "context" "database/sql" "log" "net" "google.golang.org/grpc" "github.com/lib/pq" pb "your_project/pb" ) type server struct { pb.UnimplementedUserServiceServer db *sql.DB } func (s *server) GetUser(ctx context.Context, req *pb.GetUserRequest) (*pb.User, error) { var user pb.User err := s.db.QueryRow("SELECT id, name, email FROM users WHERE id = $1", req.Id).Scan(&user.Id, &user.Name, &user.Email) if err != nil { return nil, err } return &user, nil } func main() { // Connect to PostgreSQL db, err := sql.Open("postgres", "postgresql://username:password@localhost/dbname?sslmode=disable") if err != nil { log.Fatalf("Failed to connect to database: %v", err) } defer db.Close() // Create gRPC server s := grpc.NewServer() pb.RegisterUserServiceServer(s, &server{db: db}) // Start listening lis, err := net.Listen("tcp", ":50051") if err != nil { log.Fatalf("Failed to listen: %v", err) } log.Println("Server listening on :50051") if err := s.Serve(lis); err != nil { log.Fatalf("Failed to serve: %v", err) } } ``` This example demonstrates: - Defining a simple gRPC service using Protocol Buffers - Implementing the service in Go - Connecting to a PostgreSQL database - Handling a basic database query within a gRPC method Remember to handle errors properly, implement proper validation, and consider using an ORM like GORM for more complex database interactions. Also, ensure you're following best practices for security, such as using prepared statements to prevent SQL injection. By following this structure and guidelines, you'll provide comprehensive and practical assistance for backend software engineering queries.

by @Will Cygan

.cursorrules GitHub Instructions

copy Writing code is like giving a speech. If you use too many big words, you confuse your audience. Define every word, and you end up putting your audience to sleep. Similarly, when you write code, you shouldn't just focus on making it work. You should also aim to make it readable, understandable, and maintainable for future readers. To paraphrase software engineer Martin Fowler, "Anybody can write code that a computer can understand. Good programmers write code that humans can understand." As software developers, understanding how to write clean code that is functional, easy to read, and adheres to best practices helps you create better software consistently. This article discusses what clean code is and why it's essential and provides principles and best practices for writing clean and maintainable code. What Is Clean Code? Clean code is a term used to refer to code that is easy to read, understand, and maintain. It was made popular by Robert Cecil Martin, also known as Uncle Bob, who wrote "Clean Code: A Handbook of Agile Software Craftsmanship" in 2008. In this book, he presented a set of principles and best practices for writing clean code, such as using meaningful names, short functions, clear comments, and consistent formatting. Ultimately, the goal of clean code is to create software that is not only functional but also readable, maintainable, and efficient throughout its lifecycle. Why Is Clean Code Important? When teams adhere to clean code principles, the code base is easier to read and navigate, which makes it faster for developers to get up to speed and start contributing. Here are some reasons why clean code is essential. Readability and maintenance: Clean code prioritizes clarity, which makes reading, understanding, and modifying code easier. Writing readable code reduces the time required to grasp the code's functionality, leading to faster development times. Team collaboration: Clear and consistent code facilitates communication and cooperation among team members. By adhering to established coding standards and writing readable code, developers easily understand each other's work and collaborate more effectively. Debugging and issue resolution: Clean code is designed with clarity and simplicity, making it easier to locate and understand specific sections of the codebase. Clear structure, meaningful variable names, and well-defined functions make it easier to identify and resolve issues. Improved quality and reliability: Clean code prioritizes following established coding standards and writing well-structured code. This reduces the risk of introducing errors, leading to higher-quality and more reliable software down the line. Now that we understand why clean code is essential, let's delve into some best practices and principles to help you write clean code. Principles of Clean Code Like a beautiful painting needs the right foundation and brushstrokes, well-crafted code requires adherence to specific principles. These principles help developers write code that is clear, concise, and, ultimately, a joy to work with. Let's dive in. 1. Avoid Hard-Coded Numbers Use named constants instead of hard-coded values. Write constants with meaningful names that convey their purpose. This improves clarity and makes it easier to modify the code. Example: The example below uses the hard-coded number 0.1 to represent a 10% discount. This makes it difficult to understand the meaning of the number (without a comment) and adjust the discount rate if needed in other parts of the function. Before: def calculate_discount(price): discount = price * 0.1 # 10% discount return price - discount The improved code replaces the hard-coded number with a named constant TEN_PERCENT_DISCOUNT. The name instantly conveys the meaning of the value, making the code more self-documenting. After: def calculate_discount(price): TEN_PERCENT_DISCOUNT = 0.1 discount = price * TEN_PERCENT_DISCOUNT return price - discount Also, If the discount rate needs to be changed, it only requires modifying the constant declaration, not searching for multiple instances of the hard-coded number. 2. Use Meaningful and Descriptive Names Choose names for variables, functions, and classes that reflect their purpose and behavior. This makes the code self-documenting and easier to understand without extensive comments. As Robert Martin puts it, “A name should tell you why it exists, what it does, and how it is used. If a name requires a comment, then the name does not reveal its intent.” Example: If we take the code from the previous example, it uses generic names like "price" and "discount," which leaves their purpose ambiguous. Names like "price" and "discount" could be interpreted differently without context. Before: def calculate_discount(price): TEN_PERCENT_DISCOUNT = 0.1 discount = price * TEN_PERCENT_DISCOUNT return price - discount Instead, you can declare the variables to be more descriptive. After: def calculate_discount(product_price): TEN_PERCENT_DISCOUNT = 0.1 discount_amount = product_price * TEN_PERCENT_DISCOUNT return product_price - discount_amount This improved code uses specific names like "product_price" and "discount_amount," providing a clearer understanding of what the variables represent and how we use them. 3. Use Comments Sparingly, and When You Do, Make Them Meaningful You don't need to comment on obvious things. Excessive or unclear comments can clutter the codebase and become outdated, leading to confusion and a messy codebase. Example: Before: def group_users_by_id(user_id): # This function groups users by id # ... complex logic ... # ... more code … The comment about the function is redundant and adds no value. The function name already states that it groups users by id; there's no need for a comment stating the same. Instead, use comments to convey the "why" behind specific actions or explain behaviors. After: def group_users_by_id(user_id): """Groups users by id to a specific category (1-9). Warning: Certain characters might not be handled correctly. Please refer to the documentation for supported formats. Args: user_id (str): The user id to be grouped. Returns: int: The category number (1-9) corresponding to the user id. Raises: ValueError: If the user id is invalid or unsupported. """ # ... complex logic ... # ... more code … This comment provides meaningful information about the function's behavior and explains unusual behavior and potential pitfalls. 4. Write Short Functions That Only Do One Thing Follow the single responsibility principle (SRP), which means that a function should have one purpose and perform it effectively. Functions are more understandable, readable, and maintainable if they only have one job. It also makes testing them very easy. If a function becomes too long or complex, consider breaking it into smaller, more manageable functions. Example: Before: def process_data(data): # ... validate users... # ... calculate values ... # ... format output … This function performs three tasks: validating users, calculating values, and formatting output. If any of these steps fail, the entire function fails, making debugging a complex issue. If we also need to change the logic of one of the tasks, we risk introducing unintended side effects in another task. Instead, try to assign each task a function that does just one thing. After: def validate_user(data): # ... data validation logic ... def calculate_values(data): # ... calculation logic based on validated data ... def format_output(data): # ... format results for display … The improved code separates the tasks into distinct functions. This results in more readable, maintainable, and testable code. Also, If a change needs to be made, it will be easier to identify and modify the specific function responsible for the desired functionality. 5. Follow the DRY (Don't Repeat Yourself) Principle and Avoid Duplicating Code or Logic Avoid writing the same code more than once. Instead, reuse your code using functions, classes, modules, libraries, or other abstractions. This makes your code more efficient, consistent, and maintainable. It also reduces the risk of errors and bugs as you only need to modify your code in one place if you need to change or update it. Example: Before: def calculate_book_price(quantity, price): return quantity * price def calculate_laptop_price(quantity, price): return quantity * price In the above example, both functions calculate the total price using the same formula. This violates the DRY principle. We can fix this by defining a single calculate_product_price function that we use for books and laptops. This reduces code duplication and helps improve the maintenance of the codebase. After: def calculate_product_price(product_quantity, product_price): return product_quantity * product_price 6. Follow Established Code-Writing Standards Know your programming language's conventions in terms of spacing, comments, and naming. Most programming languages have community-accepted coding standards and style guides, for example, PEP 8 for Python and Google JavaScript Style Guide for JavaScript. Here are some specific examples: Java: Use camelCase for variable, function, and class names. Indent code with four spaces. Put opening braces on the same line. Python: Use snake_case for variable, function, and class names. Use spaces over tabs for indentation. Put opening braces on the same line as the function or class declaration. JavaScript: Use camelCase for variable and function names. Use snake_case for object properties. Indent code with two spaces. Put opening braces on the same line as the function or class declaration. Also, consider extending some of these standards by creating internal coding rules for your organization. This can contain information on creating and naming folders or describing function names within your organization. 7. Encapsulate Nested Conditionals into Functions One way to improve the readability and clarity of functions is to encapsulate nested if/else statements into other functions. Encapsulating such logic into a function with a descriptive name clarifies its purpose and simplifies code comprehension. In some cases, it also makes it easier to reuse, modify, and test the logic without affecting the rest of the function. In the code sample below, the discount logic is nested within the calculate_product_discount function, making it difficult to understand at a glance. Example: Before: def calculate_product_discount(product_price): discount_amount = 0 if product_price > 100: discount_amount = product_price * 0.1 elif price > 50: discount_amount = product_price * 0.05 else: discount_amount = 0 final_product_price = product_price - discount_amount return final_product_price We can clean this code up by separating the nested if/else condition that calculates discount logic into another function called get_discount_rate and then calling the get_discount_rate in the calculate_product_discount function. This makes it easier to read at a glance. The get_discount_rate is now isolated and can be reused by other functions in the codebase. It’s also easier to change, test, and debug it without affecting the calculate_discount function. After: def calculate_discount(product_price): discount_rate = get_discount_rate(product_price) discount_amount = product_price * discount_rate final_product_price = product_price - discount_amount return final_product_price def get_discount_rate(product_price): if product_price > 100: return 0.1 elif product_price > 50: return 0.05 else: return 0 8. Refactor Continuously Regularly review and refactor your code to improve its structure, readability, and maintainability. Consider the readability of your code for the next person who will work on it, and always leave the codebase cleaner than you found it. 9. Use Version Control Version control systems meticulously track every change made to your codebase, enabling you to understand the evolution of your code and revert to previous versions if needed. This creates a safety net for code refactoring and prevents accidental deletions or overwrites. Use version control systems like GitHub, GitLab, and Bitbucket to track changes to your codebase and collaborate effectively with others.

by @Jeremy Russell