## **1. What is AI and Vibe Coding?** AI-powered coding is revolutionizing software development by shifting how developers write, debug, and optimize code. **AI Coding** refers to any form of software development where artificial intelligence assists in writing, debugging, optimizing, or generating code. It includes a range of tools, from basic autocomplete systems to fully autonomous AI coding agents. **Vibe Coding** is a more recent term that refers to a frictionless, AI-powered coding experience where up to 90% of the code is AI-generated. Instead of merely assisting, AI becomes the primary driver of the coding process, allowing developers to work at a higher level of abstraction. **Types of AI Coding Tools:** - Coding Assistants: GitHub Copilot, Codeium (suggesting code snippets) - Autonomous Coding Agents: Cline, Cursor (generating structured, functional code) - Full AI Application Builders: Vercel V0, Reweb (creating apps without much coding) > [!info] > AI coding tools can still require significant human input, especially when dealing with complex logic, debugging, or deployment. **Key Benefits of AI Coding:** - Faster development time - Reduced human errors - Enhanced productivity for both technical and non-technical users - Better code consistency and maintainability - AI-assisted debugging and optimization ![[Vibe Coding Cheat Sheet.jpeg]] ## **2. Different Levels of AI Coding** AI coding tools fall into different levels based on their autonomy: **1. AI Coding Assistants** - These tools provide suggestions, autocompletions, and bug fixes but still require human oversight. **2. Autonomous AI Coding Tools** - These can generate, modify, and debug code with minimal human intervention. **3. Fully Automated Code Generation** - These tools generate entire applications with minimal user input. **4. AI-Powered DevOps and Deployment**: AI tools that manage full application lifecycles, from development to deployment. ![[AI_Coding_Cursor.jpeg]] *Source: Sacra* ## **3. Different Types of AI Coding Tools** AI coding tools are available in different formats, depending on their integration level and functionality. **1. IDE Extensions** - These integrate directly into IDEs (e.g., VS Code, JetBrains) to assist developers while coding. Examples: GitHub Copilot, Codeium, Cline. **2. IDE Wrappers** - Standalone AI-powered IDEs with advanced AI features for coding. Examples: Replit, StackBlitz Bolt, Cursor. **3. SaaS-Based AI Coding Platforms** - These services are purpose-built for AI-assisted software development, offering features such as code generation, debugging, UI design, and full-stack development. **4. General LLM Models That Can Recommend Code** - These models are designed for general AI tasks, including code generation. They are not specifically optimized for coding but can still be used effectively for various programming needs. ![[Coding Tools.png]] *Source: [Henry Shi](https://x.com/henrythe9ths/status/1889381891373146421)* ## **4. List of AI coding tools** | **Tool Name (URL)** | **Description** | **AI Coding Level** | **Type** | | ------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------ | ----------------------------------- | ------------------- | | [**Aider**](https://github.com/aider-dev/aider) | AI-powered terminal coding tool for power users. | Autonomous AI Coding Tool | IDE Extension | | [**Amazon Q Developer**](https://aws.amazon.com/q/) | AI assistant for AWS cloud development, infrastructure, and security. | AI Coding Assistant | SaaS-Based Platform | | [**Anthropic Claude 3.5**](https://www.anthropic.com/) & [Claude Code](https://docs.anthropic.com/en/docs/claude-code/overview) | AI model for rapid prototyping and reasoning in software development. | General LLM for Code Recommendation | LLM-Based AI Model | | [**Cline**](https://cline.dev/) | Open-source AI-powered coding assistant for VS Code. | Autonomous AI Coding Tool | IDE Extension | | [**Codeium**](https://www.codeium.com/) | Free AI code assistant providing autocomplete and debugging support. | AI Coding Assistant | IDE Extension | | [**Codeium Windsurf**](https://codeium.com/) | AI-powered agentic coding for full-stack development. | Autonomous AI Coding Tool | IDE Extension | | [**Cursor**](https://cursor.so/) | AI-enhanced IDE with deep agentic coding capabilities. | Autonomous AI Coding Tool | IDE Wrapper | | [**DeepSeek AI R1**](https://www.deepseek.com/) | AI model with strong reasoning chains for software development. | General LLM for Code Recommendation | LLM-Based AI Model | | [**GitHub Copilot**](https://github.com/features/copilot) | AI-powered code completion and inline suggestions for developers. | AI Coding Assistant | IDE Extension | | [**GitHub Spark**](https://github.com/spark-ai) | AI-powered coding assistant focusing on automation in GitHub workflows. | AI Coding Assistant | IDE Extension | | [**Google Gemini Code Assist**](https://gemini.google.com/) | AI-powered code suggestion and completion tool by Google. | AI Coding Assistant | LLM-Based AI Model | | [**Google Gemini 2**](https://gemini.google.com/) | Multi-modal AI model with some code-generation capabilities. | General LLM for Code Recommendation | LLM-Based AI Model | | [**Intellicode**](https://visualstudio.microsoft.com/services/intellicode/) | AI-powered code completion tool by Microsoft for Visual Studio. | AI Coding Assistant | IDE Extension | | [**Lovable**](https://www.lovable.dev/) | AI-powered cloud-based development environment with auto-generated code. | Autonomous AI Coding Tool | SaaS-Based Platform | | [**OpenAI GPT-4 (o3-mini-high)**](https://openai.com/) | AI-powered code generation and logic reasoning. | General LLM for Code Recommendation | LLM-Based AI Model | | [**Onlook**](https://onlook.design/) | AI-powered UI-first design workflow for frontend code generation. | Fully Automated Code Generation | SaaS-Based Platform | | [**OpenHands**](https://github.com/OpenHandsAI) | Open-source AI coding assistant with deep automation. | Autonomous AI Coding Tool | IDE Extension | | [**Replit**](https://replit.com/) | Cloud-based AI-powered IDE for full-stack development. | Autonomous AI Coding Tool | IDE Wrapper | | **[Sourcegraph Cody](https://sourcegraph.com/cody)** | AI-powered search and code completion assistant for large repositories. | AI Coding Assistant | IDE Extension | | [**StackBlitz Bolt**](https://stackblitz.com/) | Cloud-based IDE with AI code generation and live collaboration. | Autonomous AI Coding Tool | IDE Wrapper | | [**Tabnine**](https://www.tabnine.com/) | AI-based code suggestion tool optimized for privacy and speed. | AI Coding Assistant | IDE Extension | | [**Tempo (YC S23)**](https://www.tempo.dev/) | AI-powered DevOps tool for full application lifecycle automation. | AI-Powered DevOps & Deployment | SaaS-Based Platform | | **[Vercel v0](https://v0.dev/)** | No-code/low-code AI-powered platform for building web applications. | Fully Automated Code Generation | SaaS-Based Platform | AI coding is rapidly evolving, and developers must decide how much autonomy they want AI to have in their workflow. Whether you're a beginner looking for a no-code solution or an advanced programmer leveraging AI for deeper automation, there’s a tool designed for your needs. Read more: - [Best AI for coding in 2025: 25 developer tools to use (or avoid)](https://www.pragmaticcoders.com/resources/ai-developer-tools) - [AI-Driven Prototyping: v0, Bolt, and Lovable Compared](https://addyo.substack.com/p/ai-driven-prototyping-v0-bolt-and) ## **5. Best Practices for AI-Assisted Coding** While AI can boost efficiency, it also introduces new challenges in maintainability, security, and accountability. Thus let's take a look at the best practices for AI-assisted coding, **emphasizing quality control, documentation, security, and collaborative development.** Here is a collection of best practices for AI-Assisted coding in organizations: ### **1. Pair AI Code with Human Code Reviews** AI-generated code should always be reviewed by human engineers before being merged into production. While AI can produce syntactically correct code, it may introduce logical errors, inefficiencies, or security vulnerabilities. To implement this practice, organizations should: - Require at least one human review before merging AI-generated code. - Use AI-assisted code review tools while ensuring human oversight. - Maintain a log of AI-generated contributions for transparency. - Assign domain experts to review AI-generated logic in complex areas. ### **2. Implement Static and Dynamic Analysis for AI Code** Automated analysis is crucial for detecting errors in AI-generated code. Static analysis checks for syntax errors, type mismatches, and unused variables, while dynamic analysis evaluates runtime behavior. To integrate this practice: - Use tools like ESLint, SonarQube, and TypeScript type checking for static analysis. - Conduct dynamic tests such as fuzz testing and runtime monitoring. - Implement automated test pipelines to catch AI errors early. ### **3. Manage Dependencies Automatically in AI Code** AI-generated code often introduces dependencies that must be monitored to prevent security vulnerabilities. Dependency management ensures compatibility with the latest frameworks and libraries. Best practices include: - Using tools like Dependabot, Snyk, or Renovate to scan dependencies. - Flagging and upgrading dependencies in pull requests. - Enforcing security checks on newly added dependencies. ### **4. Enforce Auto-Generated Documentation** AI-generated code should include clear documentation to facilitate understanding and maintenance. AI can assist in generating documentation, but human engineers must validate it. To ensure proper documentation: - Require AI to generate docstrings for each function/class. - Use AI to summarize complex code into human-readable explanations. - Mandate human validation of AI-generated documentation. ### **5. Conduct Privacy Audits on AI Code Contributions** AI-generated code must be scrutinized for potential exposure of sensitive data. AI may inadvertently introduce hardcoded credentials, API keys, or personal data. To mitigate risks: - Use tools like GitGuardian and truffleHog to scan for secrets. - Implement automated checks to detect sensitive data exposure. - Limit AI’s access to sensitive internal codebases. ### **6. Implement AI-Powered Threat Modeling** AI-generated code may introduce security risks such as SQL injection, privilege escalation, or buffer overflows. Automated security analysis helps detect vulnerabilities. To strengthen security: - Use tools like OWASP Dependency-Check and CodeQL for vulnerability analysis. - Require AI-generated code to pass security scans before merging. - Maintain a database of common AI-generated security issues. ### **7. Set Higher Unit Test Coverage for AI Code** AI-generated code must meet higher testing standards to catch potential errors that may arise due to its probabilistic nature. To enforce this: - Set a minimum test coverage percentage (e.g., 80%) for AI-generated code. - Require AI-generated unit tests but mandate human validation. - Implement continuous integration (CI) pipelines to verify AI-generated tests. ### **8. Establish AI Feedback Loops for Continuous Improvement** AI-generated code should be continuously evaluated to refine AI’s performance over time. AI feedback loops help improve coding suggestions. Best practices include: - Allowing developers to rate AI-generated suggestions (e.g., thumbs up/down). - Periodically reviewing and retraining AI using feedback data. - Implementing A/B testing for AI-generated vs. human-written code. ### **9. Maintain Accountability for AI-Generated Code** Developers should be responsible for AI-generated code that they approve and merge. AI code failures should be owned and addressed by the engineers overseeing them. To implement accountability: - Establish a policy: “Your AI code breaks, you own it.” - Require AI-generated pull requests to be linked to a responsible engineer. - Use AI attribution logs for debugging and compliance. ### **10. Require AI-Generated Roadmaps and PRDs for Features** Before building any feature, developers should define its purpose, functionality, and limitations. AI can generate roadmaps to ensure adherence to requirements. To implement this: - Describe the feature and what it should not do in a roadmap.md file. - Use AI-generated Product Requirements Documents (PRD.md) to maintain clarity. - Reference PRDs in AI-generated code for consistency. ### **11. Require AI-Generated Code to Pass Clarifying Questions** AI should ask relevant questions when processing prompts to prevent misinterpretations. This minimizes rework due to incorrect execution. Best practices include: - Adding prompt refinements such as: “Do you need any clarifying questions from what I just requested?” - Training AI to request clarification before making changes. ### **12. Allow Developers to Stop Incorrect AI Execution** If AI starts executing code incorrectly, developers should be empowered to stop the process and refine their prompts before allowing further execution. To implement this: - Train developers to stop AI execution when necessary. - Refine prompts iteratively to ensure the AI doesn’t take incorrect routes repeatedly. ### **13. Require Visual Documentation for AI Code Contributions** Understanding AI-generated code can be challenging without visual aids. Diagrams help illustrate complex logic. Best practices include: - Requiring a Mermaid diagram for each pull request. - Using AI to auto-generate visual representations of code structures. - Validating diagrams with human review before merging code. ### **14. Mandate AI-Written Unit Tests for AI Code Contributions** AI should generate unit tests for AI-generated code to ensure correctness and coverage. To implement this: - Enforce the generation of AI-written unit tests. - Require human engineers to validate and refine AI-generated tests. - Use AI-powered test suites for continuous validation. ### **15. Encourage Small, Stacked Pull Requests for AI Code** Large pull requests make reviewing AI-generated code difficult. Encouraging smaller, stacked PRs improves maintainability. Best practices include: - Limiting PRs to incremental, manageable changes. - Using AI to generate summaries of stacked PRs for easier reviews. - Automating PR approvals based on passing AI-generated tests. AI-assisted coding offers significant advantages in efficiency, but it also introduces risks that require careful management. By implementing structured best practices—ranging from human oversight and rigorous testing to dependency management and AI accountability—teams can harness AI’s power while ensuring code quality, security, and maintainability.