AI Coding Tools

By Tom Berg

August 28, 2025

About this collection

## AI Coding Tools One field where the impact of AI is *clearly*, *already* enormous is in software, where AI coding assistants have already changed everything and are still improving rapidly. Here's a collection of sources about the different agents, their approaches, and their capabilities. The space is characterized by intense competition and rapid innovation, with tools differentiating through **model access** (Claude dominance?), **context awareness** (codebase understanding vs. local context), and **autonomy levels** (human-in-the-loop vs. fully autonomous). Major players include established tech giants (Google's Gemini Code Assist, Amazon Q Developer, GitHub (i.e. Microsoft) Copilot), the LLM powerhouses (Anthropic's Claude Code and OpenAI's Codex), smaller startups (Cognition's Devin, Windsurf, Tabnine), and open source projects (Aider, Cline).

Curated Sources

Coding Agents 101: The Art of Actually Getting Things Done

The document provides a comprehensive guide on effectively utilizing coding agents in software development workflows. It covers the evolution of developer tooling, from autocomplete and intellisense to autonomous agents capable of handling tasks end-to-end. The guide is divided into sections, starting with 'Getting Started,' which introduces the basics of interacting with coding agents, such as clear prompting, providing context, and leveraging feedback loops. It then delves into 'Using Agents in Your Workflow,' offering practical advice on delegating tasks, code reviews, and automating repetitive work. The 'Intermediate' section focuses on delegating larger tasks, automating first drafts, and co-developing detailed plans with agents. 'Advanced' topics include automating workflows, creating custom CLI tools, and improving agent performance through environment setup and knowledge base customization. The document also addresses the limitations of autonomous agents, such as limited debugging skills and knowledge cutoffs, and provides strategies for managing time, minimizing losses, and ensuring security.

Key Takeaways

Effective use of coding agents requires clear communication of tasks and expectations, leveraging their ability to handle repetitive and complex tasks while maintaining human oversight for critical verification and decision-making.
The key to successfully delegating larger tasks to coding agents lies in breaking down the task into manageable chunks, setting clear checkpoints, and teaching the agent to verify its own work through feedback and testing.
While autonomous agents offer significant productivity gains, their limitations, such as poor fine-grained visual reasoning and limited debugging skills, necessitate careful task selection and human intervention when needed.
Customization and continuous improvement of coding agents through environment setup, knowledge base updates, and feedback integration are crucial for maximizing their potential and adapting to evolving project requirements.
As coding agents become more prevalent, the role of software engineers will evolve to focus more on oversight, strategic decision-making, and high-level problem-solving, making it essential to adapt workflows and skill sets accordingly.

Best practices for using GitHub Copilot - GitHub Docs

This document provides best practices for using GitHub Copilot, an AI coding assistant that helps developers write code faster and with less effort. It covers understanding Copilot's strengths and weaknesses, choosing the right Copilot tool for the job, creating thoughtful prompts, checking Copilot's work, guiding Copilot towards helpful outputs, and staying up-to-date on Copilot's features. The document emphasizes the importance of understanding Copilot's capabilities and limitations, providing context, and validating the code it suggests. It also highlights the different use cases for Copilot code completion and Copilot Chat, and provides tips for effective prompt engineering and code review.

Key Takeaways

To get the most out of GitHub Copilot, it's essential to understand its strengths and weaknesses, and use it for tasks it's well-suited for, such as writing tests and repetitive code, debugging, and explaining code.
Effective prompt engineering is critical to getting valuable responses from Copilot, and involves breaking down complex tasks, being specific about requirements, and providing examples.
Validating Copilot's suggestions is crucial to ensuring accuracy and security, and involves understanding the suggested code, reviewing it carefully, and using automated tests and tooling to check its work.

Tips | aider

The document provides tips for using Aider, a tool that assists with code editing, to maximize efficiency. It suggests adding only relevant files to the chat to avoid distracting the Large Language Model (LLM). Users are advised to break down complex tasks into smaller steps and adjust the files in the chat accordingly. For complicated changes, discussing a plan with Aider before proceeding is recommended. The document also covers how to handle situations where Aider gets stuck, such as clearing the chat history, dropping unnecessary files, or switching to a different model. Additionally, it explains how to create new files, fix bugs, and provide documentation to Aider. Tips on interrupting Aider and sending multiline messages are also included.

Key Takeaways

To optimize Aider's performance, it's crucial to be selective about which files are added to the chat, as too much irrelevant code can confuse the LLM.
Breaking down complex tasks into smaller, manageable steps and adjusting the files in the chat as needed can significantly improve the efficiency of the code editing process.
When Aider encounters difficulties, strategies such as clearing the chat history, dropping unnecessary files, or switching models can help resolve the issue and improve productivity.

How I use Cursor (+ my best tips)

The document discusses how to effectively use Cursor, an AI tool, to enhance productivity in software development. The author shares personal experiences and tips on utilizing various features such as YOLO mode, handling complex tasks, visual editing, and debugging. The article emphasizes that while AI tools can automate basic coding tasks, they do not replace the need for coding skills and problem-solving abilities. It highlights the importance of balancing AI assistance with manual coding skills to achieve efficiency and deliver high-quality products.

Key Takeaways

AI tools like Cursor can significantly enhance developer productivity by automating routine tasks, but they require a balance with manual coding skills to tackle complex problems.
Features like YOLO mode and visual editing in Cursor can streamline the development process, allowing developers to focus on higher-level tasks.
The effective use of AI tools in coding requires understanding their capabilities and limitations, as well as maintaining and developing one's own coding skills.

Claude Code Best Practices \ Anthropic

This document provides tips and best practices for using Claude Code, a command line tool for agentic coding, across various codebases, languages, and environments. It covers customization, tool usage, workflows, and optimization techniques to improve the effectiveness of Claude Code. The document is based on the experiences of Anthropic engineers and researchers who developed Claude Code.

Key Takeaways

Customizing Claude Code with CLAUDE.md files and allowed tools can significantly improve its performance and safety.
Using Claude Code with various tools and workflows, such as bash tools, MCP, and custom slash commands, can enhance its capabilities.
Optimizing workflows by being specific in instructions, giving Claude images, and using checklists and scratchpads can lead to better results.

Prompt Engineering Guide - Cline

The document is a comprehensive guide to Cline, an AI-powered coding assistant. It covers various aspects of using Cline, including installation, task management, context management, and prompt engineering. The guide provides detailed information on Cline's features, such as auto-approve, checkpoints, and workflows, as well as its integration with various cloud providers and AI models. It also includes troubleshooting tips and best practices for using Cline effectively. The document highlights the importance of clear context, specific questioning, and feedback in getting the most out of Cline. Additionally, it showcases advanced prompting techniques, such as constraint stuffing and confidence checks, to improve the quality of Cline's responses. The guide also includes examples of effective prompts for various tasks, such as debugging, refactoring, and feature development.

Key Takeaways

Effective use of Cline requires clear context and specific questioning to guide the AI assistant towards the desired outcome.
Advanced prompting techniques, such as constraint stuffing and confidence checks, can significantly improve the quality and reliability of Cline's responses.
Cline's capabilities can be extended through integration with various cloud providers and AI models, making it a versatile tool for a wide range of development tasks.
The use of .clineignore files can help optimize Cline's performance by excluding non-essential content and protecting sensitive data.
Cline's community has developed a range of best practices and favorite prompts that can be used to improve the effectiveness of the tool.

Augment Code Reviews, Ratings & Features 2025 | Gartner Peer Insights

Augment Code is a Developer AI tool that provides personalized code suggestions and insights by analyzing a large codebase and development ecosystem. Founded in 2022, it offers a free plan for OSS projects and paid plans ranging from $30 to $60 per month. Users praise its ability to accelerate productivity, improve code iterations, and reduce issues in SDLC. Reviewers highlight its context awareness, IDE integration, and responsiveness. However, some note limitations such as lack of model selection transparency and auto-documentation. Overall, Augment Code is seen as a powerful coding assistant that enhances AI usability in software engineering.

Key Takeaways

Augment Code significantly improves developer productivity by providing personalized code suggestions and insights.
The tool's context awareness and integration with IDEs and Slack enhance its usability and effectiveness.
While praised for its responsiveness and support, some users note areas for improvement such as model selection transparency and auto-documentation.

Anysphere Cursor Reviews, Ratings & Features 2025 | Gartner Peer Insights

The document provides an overview of Cursor, an AI-powered code editor developed by Anysphere, a private company based in the USA. Cursor aims to automate coding tasks, enhancing developer productivity and efficiency. The document includes reviews from various users across different industries and company sizes, highlighting Cursor's strengths in code optimization, debugging, and code completion. Users praise Cursor for its ability to speed up development processes, provide intelligent code suggestions, and reduce repetitive tasks. However, some users mention limitations, such as struggles with complex projects and occasional crashes. The reviews are sourced from Gartner Peer Insights, a platform that aggregates user feedback on various products and services.

Key Takeaways

Cursor significantly enhances developer productivity by automating coding tasks and providing intelligent code suggestions.
The AI-powered code editor is particularly effective for simple prototypes and repetitive tasks, but may struggle with larger, more complex projects.
Users across various industries and company sizes praise Cursor for its code optimization, debugging capabilities, and user support.
While Cursor shows promise, it is not without limitations, including occasional crashes and potential shortcomings in its large language model (LLM) capabilities.

Best AI Code Assistants Reviews 2025 | Gartner Peer Insights

This document provides an overview of AI code assistants, their features, and reviews from Gartner Peer Insights. AI code assistants are tools that use foundation models like large language models (LLMs) to generate, analyze, debug, and refactor code. They integrate into developer tools and can be customized to an organization's codebase. The document lists various AI code assistant products, their vendors, and ratings. It also outlines mandatory features for AI code assistants, including code completion, conversational chat interfaces, and the ability to work across multiple vendor ecosystems without training on customer code. The products reviewed include GitHub Copilot, Amazon Q Developer, Gemini, and others, with ratings based on user reviews and feedback.

Key Takeaways

The use of AI code assistants can significantly enhance developer productivity by automating routine coding tasks and providing real-time assistance.
Customization to an organization's specific codebase and documentation is a key feature for many AI code assistants, allowing for more accurate and relevant code suggestions.
The document highlights a range of AI code assistant products with varying ratings, indicating a diverse market with different strengths and weaknesses.
Mandatory features for AI code assistants include code completion, conversational interfaces, and the ability to operate across multiple ecosystems without compromising customer code security.

Minusx | What makes Claude Code so damn good (and how to recreate that magic in your agent)!?

The document discusses the features that make Claude Code a delightful AI agent and provides guidance on how to recreate its success in other chat-based LLM agents. Claude Code's effectiveness is attributed to its simplicity, well-designed control loop, elaborate prompts, and effective tool usage. The author analyzed Claude Code's functionality by logging network requests and identified key factors contributing to its performance. The document outlines the importance of maintaining a simple control loop, using smaller models for various tasks, and crafting prompts with heuristics and examples. It also highlights the benefits of using LLM search over RAG-based search and designing tools that cater to the model's strengths. Additionally, the document touches on the importance of steerability, tone, and style in LLM agents.

Key Takeaways

To build effective LLM agents, prioritize simplicity in design and avoid over-engineering, as seen in Claude Code's single main loop and minimal branching.
Using smaller models for tasks like reading large files and summarizing conversations can significantly reduce costs without compromising performance.
Crafting elaborate prompts with heuristics, examples, and special XML tags can improve the agent's understanding and adherence to instructions.
LLM search is preferred over RAG-based search due to its ability to understand code and reduce hidden failure modes.
Designing tools that cater to the model's strengths and weaknesses, such as low-level and high-level tools, can enhance the agent's overall performance.

Qodo Documentation | Qodo Documentation

The Qodo documentation provides an overview of Qodo's products and services, including Qodo Gen for generating quality code and tests within an Integrated Development Environment (IDE), Qodo Command for running and managing AI agents from the terminal, and Qodo Merge for simplifying pull requests. The Management Portal offers insights and management capabilities for accounts, teams, and organizations. Additionally, the documentation covers On Prem environments with how-to guides. The site is updated regularly, with the last update occurring 8 days prior to the current date, and includes links to Terms of Use, Privacy Policy, and Data Processing information.

Key Takeaways

The Qodo suite of products is designed to enhance developer productivity through AI-driven tools like code generation and AI agent management.
Qodo's products are tailored to work within existing development workflows, including IDEs and terminals, indicating a focus on integration and ease of use.
The inclusion of a Management Portal suggests that Qodo's products are designed with team and organizational use cases in mind, offering administrative and analytical capabilities.

Qodo (formerly Codium) | AI Agents for Code, Review & Workflows

Qodo is an agentic code integrity platform that integrates AI across development workflows to enhance code quality. It offers tools like Qodo Command, Qodo Merge, and Qodo Gen for automating code reviews, generating tests, and improving code quality. Qodo's agents are context-aware, understanding organizational best practices and codebase specifics. The platform supports all major programming languages and integrates with popular IDEs and git providers. Qodo provides a free plan for individual developers and enterprise features for teams, focusing on security, privacy, and compliance with SOC2 certification and SSL encryption.

Key Takeaways

Qodo's agentic platform uses AI to understand codebase specifics and organizational best practices, enabling context-aware code suggestions and reviews.
The platform's tools, such as Qodo Merge and Qodo Gen, automate code reviews and test generation, improving code quality and developer productivity.
Qodo prioritizes security, privacy, and compliance, being SOC2 certified and using SSL encryption to protect user data.

20 Best AI Code Assistants Reviewed and Tested [August 2025]

The document provides a comprehensive list of 20 AI coding assistant tools that can significantly enhance developers' capabilities and efficiency. These tools offer features like code suggestions, explanations, test generation, and collaboration. They support various programming languages and IDEs, catering to both individuals and teams with pricing options ranging from free to subscription-based plans. The tools are evaluated based on their ability to address common challenges faced by developers, such as syntax complexity, debugging, code efficiency, and security.

Key Takeaways

AI coding assistants can significantly enhance developer productivity and code quality by providing features like code suggestions, explanations, and test generation.
The future of AI coding assistants lies in multi-agent systems that can handle distinct tasks under safe guardrails, automating a significant portion of the development workflow.
The choice of AI coding assistant depends on factors like programming languages, codebase size, and specific tasks, with popular options including Qodo, GitHub Copilot, and CodeGPT.
Many AI coding assistants offer free versions or trials, allowing developers to experiment and find the best fit for their needs.

Personal Copilot: Train Your Own Coding Assistant

This blog post demonstrates how to create a personalized coding assistant, HugCoder, by fine-tuning the Starcoder model on the Hugging Face codebase. It covers data collection, fine-tuning using QLoRA and full fine-tuning, and deployment using Inference Endpoints and VS Code extension. The post also explores combining different LoRAs for code completion and chatting/QA tasks, and transferring LoRAs to different base models. The authors provide detailed steps for local deployment on consumer hardware and discuss the performance of the fine-tuned models.

Key Takeaways

Fine-tuning large language models like Starcoder on specific codebases can create personalized coding assistants with improved performance.
QLoRA and full fine-tuning have comparable performance, but QLoRA is more cost-effective and requires less GPU memory.
Combining different LoRAs can enable a single model to perform multiple tasks, such as code completion and chatting/QA.
Transferring LoRAs to different base models can leverage the strengths of both the original model and the new base model.

Cline Memory Bank - Cline

The Cline Memory Bank is a documentation system designed to preserve project context across AI-assisted development sessions. It consists of core files (projectbrief.md, productContext.md, activeContext.md, systemPatterns.md, techContext.md, progress.md) that maintain project knowledge and support consistent development. The Memory Bank helps manage context window limitations, creates self-documenting projects, and scales to projects of any size. Key workflows include Plan Mode and Act Mode, with commands like 'initialize memory bank' and 'update memory bank' to manage documentation updates. The system is flexible, allowing additional context files for complex features, integration specs, and testing strategies. Best practices include starting with a basic project brief, letting patterns emerge naturally, and trusting the documentation process to compound value over time.

Key Takeaways

The Cline Memory Bank provides context preservation across development sessions, ensuring consistent AI-assisted development.
The system's core files create a hierarchical structure that maintains comprehensive project knowledge.
Regular updates to the Memory Bank, triggered by commands or significant changes, maintain project documentation accuracy.
The Memory Bank concept can be applied beyond coding projects, offering versatility in managing complex tasks.

What is Cline? - Cline

Cline is an open-source AI-powered development assistant integrated with VS Code, offering features like model selection, task management, context management, and MCP server integration. It provides complete transparency, control over models, and is built for real engineering tasks. Key features include Focus Chain, Auto Compact, Deep Planning, and .clinerules. Cline allows reading and writing files, executing terminal commands, planning complex features, and connecting to external systems. It supports various cloud providers and AI models from providers like Anthropic, AWS Bedrock, and OpenAI. The tool is designed with zero trust by design and offers customization options through custom instructions and MCP servers.

Key Takeaways

Cline's integration with VS Code and support for various AI models make it a versatile tool for developers.
The emphasis on transparency, control, and zero-trust design addresses security concerns in AI-powered development tools.
Cline's features like Deep Planning and MCP Integration enable complex feature development and external system connections.

Cline - AI Coding, Open Source and Uncompromised

Cline is an open-source AI coding agent that provides transparent, model-agnostic, and secure coding assistance. It allows developers to choose any AI model, control costs, and maintain complete visibility into the coding process. Cline runs entirely client-side, ensuring code never touches their servers, making it ideal for enterprises with strict security requirements. Key features include Plan Mode for complex tasks, .clinerules for project-specific instructions, MCP Integration for connecting to external systems, Context Intelligence for transparent resource usage, and Terminal Mastery for executing commands and debugging. Cline is free and open source, with users only paying for the AI models they use. It's designed to amplify human capability, not obscure it, with a commitment to transparency and audibility in every design decision.

Key Takeaways

Cline's client-side architecture and open-source nature make it an attractive option for enterprises with strict security requirements, as it ensures code never touches their servers and allows for complete auditability.
The platform's model-agnostic approach enables users to choose the best AI model for their needs, switch between models instantly, and avoid vendor lock-in, making it future-proof by design.
Cline's transparent and visible AI decision-making process, including features like Context Intelligence and True Visibility, builds trust with users and allows them to understand exactly how the AI is making decisions and using resources.

Gemini Code Assist · GitHub Marketplace

GitHub's Gemini Code Assist is an AI-powered tool that accelerates code reviews and improves code quality by providing automatic pull request summaries, ready-to-commit code suggestions, and the ability to invoke Gemini for assistance. It is integrated into GitHub and can be customized with a configuration file and/or a custom code review style guide. The tool provides an initial review within five minutes of a new pull request being opened and allows users to request assistance via issue comments using specific commands. Users can provide feedback on the tool's suggestions and comments.

Key Takeaways

The integration of Gemini Code Assist into GitHub represents a significant step towards leveraging AI for improving code quality and streamlining development workflows.
The ability to customize the tool with specific code review style guides (e.g., PEP-8 for Python) allows for tailored feedback that aligns with project-specific standards.
The tool's capacity to provide immediate, actionable feedback on pull requests has the potential to reduce review times and improve overall development efficiency.

Use agentic chat as a pair programmer | Gemini Code Assist | Google for Developers

This document describes how to configure and use Gemini Code Assist agent mode as a pair programmer in an integrated development environment (IDE). Agent mode allows users to ask questions about their code, use context and built-in tools to improve generated content, and configure MCP servers to extend the agent's abilities. The document outlines the limitations of agent mode, how to set it up, and how to configure tools, MCP servers, and context files. It also provides information on using commands, always allowing agent actions, and additional prompts to try. The document is intended for developers who want to use Gemini Code Assist to improve their coding productivity.

Key Takeaways

The Gemini Code Assist agent mode provides a powerful tool for developers to improve their coding productivity by allowing them to ask questions about their code and generate content using context and built-in tools.
Configuring MCP servers and context files can significantly enhance the capabilities of the agent mode, allowing it to access additional information and perform more complex tasks.
The use of yolo mode and auto-approving changes can streamline the development process, but also requires careful consideration of the potential risks and limitations.

google-gemini/gemini-cli: An open-source AI agent that brings the power of Gemini directly into your terminal.

Gemini CLI is an open-source AI agent that brings Gemini's capabilities directly into the terminal. It offers a free tier with 60 requests/min and 1,000 requests/day using a personal Google account, and supports powerful features like code understanding, generation, and automation. The tool is extensible through MCP (Model Context Protocol) for custom integrations and is designed for developers who work primarily in the command line. Gemini CLI supports various authentication methods including OAuth login, Gemini API Key, and Vertex AI for enterprise use. It provides advanced features such as conversation checkpointing, custom context files, and integration with GitHub workflows. The project is fully open-source under the Apache 2.0 license and welcomes community contributions.

Key Takeaways

Gemini CLI provides a powerful terminal interface for interacting with Gemini AI, enabling features like code generation and automation.
The tool supports multiple authentication methods, including OAuth, API keys, and Vertex AI, catering to different user needs and enterprise requirements.
Gemini CLI is highly extensible through MCP servers, allowing users to integrate custom tools and capabilities.
The project is actively maintained with regular releases, including preview, stable, and nightly builds, ensuring continuous improvement and new feature additions.

Gemini Code Assist | AI coding assistant

Gemini Code Assist is an AI-powered coding assistance tool that provides developers with a range of features to improve productivity and code quality. It is available in three versions: for individuals at no cost, Standard, and Enterprise. The tool integrates with popular IDEs, provides code completion and generation, and offers AI-assisted coding assistance in various Google Cloud services such as Firebase and Colab Enterprise. Gemini Code Assist also includes features like code review, code explanation, and AI-powered automation flow generation. The Enterprise version offers customized code suggestions based on private source code repositories and integration with multiple Google Cloud services.

Key Takeaways

Gemini Code Assist offers a range of AI-powered features to improve developer productivity and code quality, including code completion, code review, and AI-assisted coding assistance.
The tool is available in three versions: for individuals at no cost, Standard ($19-$22.80/user/month), and Enterprise ($45-$54/user/month), each with varying levels of features and support.
Gemini Code Assist integrates with multiple Google Cloud services, including Firebase, Colab Enterprise, and Apigee, to provide a comprehensive AI-powered development solution.

AI coding and agents, which is best? : ChatGPTCoding

The document is a discussion thread on Reddit about various AI coding tools and their effectiveness. Users share their experiences with different tools such as Cursor, Windsurf, Cline, and Aider, discussing their features, benefits, and limitations. The conversation revolves around the capabilities of these tools in automating coding tasks, their integration with IDEs, and their performance with different models like GPT-4 and Claude. Some users highlight the importance of prompt skills when using these tools, while others discuss the cost and value proposition of premium services. The thread also touches on the idea of creating a leaderboard or comparison guide for these AI coding tools to help users choose the best one for their needs.

Key Takeaways

The effectiveness of AI coding tools depends on the user's prompt skills and specific use cases.
Cline is praised for its performance and open-source nature, despite being potentially expensive due to token usage.
Aider and Cursor are also popular choices, with some users preferring them for specific tasks or workflows.
The discussion highlights the need for a comprehensive comparison or leaderboard of AI coding tools to help users make informed decisions.
The cost and value proposition of premium services are important considerations for users, with some opting for free or low-cost alternatives.

Install | Tabnine: The AI code assistant that you control

Tabnine is an AI code assistant that provides highly personalized code suggestions in various Integrated Development Environments (IDEs) such as VS Code, IntelliJ, and Visual Studio. It supports a wide range of programming languages, libraries, and frameworks including JavaScript, Python, Java, C++, and React. Tabnine aims to accelerate and simplify software development while maintaining code privacy, security, and compliance. The platform offers features like AI code generation, code refactoring, code debugging, and code documentation. Users can explore Tabnine's capabilities through its supported IDEs and languages, and request support for additional IDEs if needed.

Key Takeaways

Tabnine's AI code assistant can significantly accelerate software development by providing personalized code suggestions in popular IDEs.
The platform's support for a wide range of programming languages and frameworks makes it a versatile tool for diverse development needs.
Tabnine prioritizes code privacy, security, and compliance, addressing concerns that are critical in software development.

Tabnine AI Code Assistant | private, personalized, protected

Tabnine is an AI-powered software development platform that helps mature engineering teams accelerate and simplify their development process. It offers a range of AI agents for code creation, explanation, testing, documentation, and bug fixing, with a focus on code privacy and security. Tabnine's AI models are context-aware and can be fine-tuned on a team's codebase. The platform supports popular languages, libraries, and IDEs, and offers various deployment options, including on-premises, VPC, and secure SaaS. Tabnine has been recognized by leading analyst firms, including Gartner, and is trusted by millions of developers and thousands of companies. User reviews praise Tabnine for its ability to improve productivity, reduce redundant typing, and provide accurate code suggestions.

Key Takeaways

Tabnine's AI-powered code assistant can improve developer productivity by up to 11% by providing accurate code suggestions that are accepted 90% of the time.
The platform's focus on code privacy and security includes features like zero data retention, bespoke models trained on a team's codebase, and protection from IP liability.
Tabnine's AI agents can be used for a range of tasks, including code creation, explanation, testing, documentation, and bug fixing, making it a comprehensive tool for software development teams.

sourcegraph/cody-public-snapshot: Type less, code more: Cody is an AI code assistant that uses advanced search and codebase context to help you write and fix code.

Cody is an open-source AI coding assistant that helps developers understand, write, and fix code faster using advanced search and codebase context. It is available for VS Code, JetBrains, and on the web, supporting various large language models like Claude Sonnet 4 and GPT-4o. Cody features include chat, autocomplete, inline edit, and customizable prompts. The repository is a public snapshot of the original Cody repository before it was made private, containing 6,150 commits and various development files. The code is licensed under Apache 2.

Key Takeaways

Cody's capabilities include chat, autocomplete, and inline edit features that leverage codebase context to assist developers.
The project supports multiple LLMs and is available across different IDEs and platforms.
The public snapshot repository provides a historical record of Cody's development under an open-source license.

Cody - Sourcegraph docs

Cody is an AI coding assistant that leverages the latest Large Language Models (LLMs) and development context to enhance coding efficiency. It integrates with code hosts like GitHub and GitLab, and IDEs such as VS Code, JetBrains, and Visual Studio. Cody's capabilities include developer chat, code completions, code edits, and customizable prompts. It uses Sourcegraph's advanced Search API to pull context from local and remote codebases, providing accurate results. Cody is available on multiple platforms, including VS Code, JetBrains, Visual Studio, and the web app. The tool collects prompts and responses to provide the service and improve user experience, but does not use user data to train models for individuals using Cody via Sourcegraph.com. Cody is compatible with other Sourcegraph products like Code Search, allowing users to ask questions about their codebase directly in Cody's chat interface.

Key Takeaways

Cody's integration with Sourcegraph's advanced Search API enables it to provide context-aware coding assistance by pulling information from both local and remote codebases.
The tool's compatibility with multiple IDEs and code hosts makes it versatile and widely applicable across different development environments.
Cody's ability to ignore selected repositories from chat and autocomplete results through Context Filters allows for more controlled and relevant coding assistance.

Open sourcing Cody | Sourcegraph Blog

Sourcegraph has open-sourced Cody, its AI-enabled editor assistant, under the Apache 2.0 license. Cody uses Large Language Models (LLMs) and Sourcegraph's code graph to provide context-aware code generation and answers. The decision to open-source Cody is driven by the preferences of developers for open tools, the potential for community contributions, and the alignment with Sourcegraph's business model of selling enterprise software. Cody's capabilities are enhanced by its connection to Sourcegraph's search and code intelligence APIs, but it can function independently. The open-sourcing of Cody is also motivated by a sense of contributing back to the open-source ecosystem that has influenced the development of LLMs.

Key Takeaways

The open-sourcing of Cody reflects Sourcegraph's understanding of developers' preferences for transparency and customizability in their tools.
Cody's performance is significantly enhanced by its ability to fetch context from the broader codebase and Sourcegraph's code graph, differentiating it from other AI coding assistants.
The decision to open-source Cody aligns with Sourcegraph's business strategy of focusing on enterprise sales rather than individual licenses.
The development of Cody's capabilities is deeply connected to the open-source ecosystem, as LLMs' reasoning abilities are influenced by training on open-source code.

Sourcegraph | Pricing

Sourcegraph offers various pricing plans for its Amp and Code Search products, catering to different user needs and organization sizes. Amp is an agentic coding tool with at-cost pricing, available in three plans: Amp for individuals, Amp Teams for small development teams, and Amp Enterprise for large development teams with additional features like Single Sign-on and enterprise-grade support. Code Search has two enterprise plans: Enterprise Starter for growing organizations with cloud-hosted search experience, and Enterprise Search for organizations requiring comprehensive search, batch changes, and enterprise-level security. Pricing for Amp is based on prepaid credits, with usage costs varying depending on the plan and features used. Enterprise plans have different pricing tiers, with Enterprise Starter costing $19 per user monthly and Enterprise Search costing $49 per user monthly.

Key Takeaways

Sourcegraph's pricing strategy is tailored to different customer segments, from individual developers to large enterprises, with varying feature sets and support levels.
The company's at-cost pricing model for Amp aims to provide transparency and cost-effectiveness for users, with the potential for cost savings through prepaid credits.
Enterprise customers have access to advanced features like Single Sign-on, enterprise-grade support, and comprehensive search capabilities, justifying higher pricing tiers.
The distinction between Enterprise Starter and Enterprise Search plans highlights Sourcegraph's focus on scalability and security for larger organizations, with corresponding differences in pricing and feature availability.

Cody | AI coding assistant from Sourcegraph

Sourcegraph introduces Cody, an enterprise AI code assistant designed to enhance development speed, quality, and consistency in complex codebases. Cody is part of Sourcegraph's enterprise plan and is trusted by major organizations, including top US banks and government agencies. It integrates with various code hosts and editors, offers access to latest-generation language models with strict security controls, and is engineered to handle large codebases. According to Principal Engineer Roderick Randolph at Coinbase, engineers using AI code assistant tools like Cody save around 5-6 hours per week and write code twice as fast.

Key Takeaways

Cody enables enterprises to achieve a balance between development speed, code quality, and consistency by automating tasks and promoting best practices.
The tool's ability to integrate with various code hosts and editors, along with its access to latest-generation language models, makes it versatile and powerful for enterprise development needs.
Sourcegraph's emphasis on enterprise-grade security, including full data isolation and zero retention, addresses critical concerns for large organizations adopting AI code assistants.

What is Amazon Q Developer? - Amazon Q Developer

Amazon Q Developer is a generative AI-powered conversational assistant that helps users understand, build, extend, and operate AWS applications. It provides software development assistance in integrated development environments (IDEs) and is powered by Amazon Bedrock. The service is available through a Free tier and a Pro subscription, offering features such as code completion, security vulnerability scanning, and code upgrades. Amazon Q Developer integrates with various AWS services and third-party tools, and provides detailed guidance on getting started, using the service, and troubleshooting.

Key Takeaways

Amazon Q Developer is a powerful tool for AWS application development and management, leveraging generative AI to provide conversational assistance.
The service offers a range of features, including code completion and security vulnerability scanning, making it a valuable resource for developers.
Amazon Q Developer's integration with Amazon Bedrock and various AWS services enhances its capabilities and provides users with a comprehensive development environment.

Amazon CodeWhisperer Documentation

The document contains information about Amazon CodeWhisperer, a machine learning-powered service that generates code recommendations for developers based on their comments and code in the integrated development environment (IDE). It also includes details about cookie preferences for the Amazon Web Services (AWS) site, categorizing cookies into essential, performance, functional, and advertising cookies. The document provides a user guide for Amazon CodeWhisperer, detailing its usage with various AWS tools and services such as Visual Studio Code, JetBrains, and AWS Lambda. Additionally, it mentions the inability to save cookie preferences at the time of access.

Key Takeaways

Amazon CodeWhisperer enhances developer productivity using machine learning to generate code recommendations based on natural language comments and code in the IDE.
The service is compatible with multiple development environments and AWS tools, including Visual Studio Code, JetBrains, and AWS Lambda.
Understanding and managing cookie preferences is crucial for user experience and data privacy on the AWS site, with different categories of cookies serving distinct purposes.

Codex | OpenAI

Codex is an AI-powered coding assistant integrated with ChatGPT, available in various plans including Plus, Pro, Business, Edu, and Enterprise. It enables developers to work seamlessly across different environments such as terminals, IDEs, and the cloud. Codex can navigate repositories, edit files, run commands, execute tests, and facilitate code reviews directly from GitHub. It operates in an isolated sandbox with access to the user's repository and environment, allowing for secure code generation that can be reviewed and merged. The tool is accessible via CLI, IDE extensions (available for VSCode, Cursor, and Windsurf), and the ChatGPT mobile app, enabling tasks to be initiated and managed across different platforms. Codex aims to accelerate software development by automating tasks, enhancing code review processes, and allowing developers to stay focused on critical tasks.

Key Takeaways

Codex represents a significant advancement in AI-assisted software development by integrating coding capabilities directly into developer workflows across multiple environments.
The tool's ability to operate in an isolated sandbox with access to the user's repository and environment enhances security and trust in the generated code.
By automating tasks such as code reviews and providing seamless integration with GitHub, Codex has the potential to significantly accelerate the software development lifecycle.
The accessibility of Codex across different platforms, including mobile devices, allows developers to initiate and manage tasks on the go, potentially increasing productivity.
The integration of Codex with ChatGPT and the recommendation to use GPT-5 as the default model indicate a reliance on advanced AI models for coding tasks, suggesting a high level of sophistication in code generation and task execution.

Pricing | Devin

Nubank, a large FinServ organization, undertook a massive refactoring project to migrate their 8-year-old, multi-million lines of code ETL monolith to sub-modules. With the help of Devin, an AI tool, they achieved a 12x efficiency improvement in engineering hours and over 20x cost savings. The project involved over 1,000 engineers and required migrating ~100,000 data class implementations. Devin was fine-tuned for the task, doubling its task completion scores and improving task speed by 4x. The tool autonomously completed migration sub-tasks, with human oversight for project management and approval of changes. Devin's learning and improvement over time significantly contributed to the project's success.

Key Takeaways

The use of Devin for ETL refactoring resulted in significant efficiency gains and cost savings for Nubank, demonstrating the potential of AI tools in large-scale code migration projects.
Devin's ability to learn and improve over time was crucial in achieving the desired outcomes, as it adapted to the task and reduced errors and edge cases.
The project's success highlights the importance of leveraging AI and automation in software development to tackle complex, repetitive tasks and free up engineering resources for more strategic work.

Customers | Devin

Nubank, a FinServ company, refactored their 8-year-old monolithic ETL containing millions of lines of code into smaller sub-modules using Devin, an AI tool. The migration involved over 1,000 engineers and was initially expected to take 18 months. With Devin's assistance, Nubank achieved a 12x efficiency improvement in engineering hours and over 20x cost savings. Devin was fine-tuned on previous manual migrations and demonstrated a doubling of task completion scores and a 4x improvement in task speed. The tool autonomously completed migrations, with human oversight for project management and approval of changes. Devin's capabilities included building classical tools and scripts to improve migration speed and reliability. The collaboration resulted in an 8x engineering time efficiency gain and enabled Nubank to deliver outcomes in months instead of years, supporting their rapid growth.

Key Takeaways

Devin's integration into Nubank's ETL refactoring process led to significant efficiency gains, demonstrating the potential for AI tools to accelerate large-scale software migrations.
The fine-tuning of Devin on previous manual migrations substantially improved its performance, highlighting the importance of contextual learning in AI-assisted development.
Nubank's experience shows that AI-assisted refactoring can reduce technical debt and enable companies to focus on new business value creation rather than being bogged down by legacy code maintenance.

Devin | The AI Software Engineer

Nubank, a large FinServ organization, undertook a massive refactoring project to migrate their 8-year-old, multi-million lines of code ETL monolith to sub-modules. The task involved over 1,000 engineers and was initially expected to take 18 months. By leveraging Devin, an AI software engineer, Nubank achieved a 12x efficiency improvement in engineering hours and over 20x cost savings. Devin handled repetitive refactoring tasks, allowing engineers to focus on reviewing and approving changes. The project demonstrated significant speed and reliability improvements as Devin learned and adapted to the task. Nubank's experience showcases the potential of AI-assisted code migration and refactoring in large-scale software modernization projects.

Key Takeaways

The use of Devin AI in Nubank's ETL refactoring project resulted in significant efficiency gains and cost savings, demonstrating the potential of AI in large-scale software modernization.
Devin's ability to learn and adapt to the task improved its performance over time, reducing errors and increasing speed.
The project highlights the importance of human oversight in AI-assisted refactoring, with engineers reviewing and approving Devin's changes to ensure accuracy and quality.

Object Generation from Structured Data | GitHub Copilot Patterns & Exercises

This document provides guidance on using GitHub Copilot to generate objects from structured data, such as JSON. It includes a Python example that demonstrates how to convert JSON data into a list of user objects. The document also offers exercises to practice generating objects from different JSON structures and handling edge cases like missing data. Additionally, it provides a checklist for further learning on adapting the pattern to complex data structures, validating data, and applying the pattern to various programming languages or frameworks.

Key Takeaways

Using GitHub Copilot can simplify the process of transforming structured data into objects, making code more robust and maintainable.
The pattern demonstrated in the document can be adapted to different JSON structures and edge cases, such as missing data.
Validating data before transformation and adapting the pattern to various programming languages or frameworks are key areas for further exploration.

Regular Expression | GitHub Copilot Patterns & Exercises

The document discusses GitHub Copilot's capabilities and limitations in generating regular expressions. It explains how Copilot can create drafts of regular expression patterns using input-output patterns and natural language descriptions. Two examples illustrate how Copilot generates regular expressions: one using input and output strings, and another using natural language descriptions. The document also highlights the limitations of Large Language Models (LLMs) like Copilot in representing complex regular expressions, suggesting that human input is necessary for complex cases. It provides exercises and checklists for further learning, including extracting specific patterns from strings and leveraging Copilot for complex regular expressions.

Key Takeaways

GitHub Copilot can generate simple regular expressions using input-output patterns or natural language descriptions, but struggles with complex ones.
The limitations of LLMs in representing complex regular expressions necessitate human input and oversight for accurate results.
Users can leverage Copilot to support building complex regular expressions by breaking them down into simpler components or using it as a starting point for further refinement.

Quick Q&A | GitHub Copilot Patterns & Exercises

The document discusses the 'Quick Q&A' technique for interacting with GitHub Copilot, a method that enables developers to rapidly engage with Copilot in their code editor for brief answers and insights. It serves as a lightweight alternative to 'GitHub Copilot Chat' for more structured conversations. The technique involves posing questions directly in the code using comments and receiving brief responses from Copilot. Examples are provided to illustrate how to use this technique, including defining roles for more context-based dialogue. Exercises are suggested to compare Quick Q&A with 'copilot chat', use it for one-liner answers, and observe the effect of defining roles on Copilot's responses. A checklist for further learning is also provided, questioning the differences between Quick Q&A and 'copilot chat', its integration into the development process, and potential drawbacks.

Key Takeaways

The Quick Q&A technique offers a lightweight and agile method for developers to interact with GitHub Copilot directly within their code editor, enhancing on-the-spot guidance and coding efficiency.
Defining roles within Quick Q&A can significantly affect the contextual relevance and usefulness of Copilot's responses, making it a valuable approach for more complex coding queries.
While Quick Q&A is not a replacement for more structured conversation tools like 'GitHub Copilot Chat', it provides a handy alternative for brief interactions, and understanding when to use each can optimize the development workflow.

Code to Comment | GitHub Copilot Patterns & Exercises

This document explores GitHub Copilot's ability to generate comments from code, enhancing code readability and understandability. It demonstrates how Copilot can automatically add comments to explain code functionality using the Sieve of Eratosthenes algorithm as an example. The document provides a step-by-step guide on using Copilot for comment generation, including an exercise to generate comments for a given function and a checklist to evaluate the quality and accuracy of the generated comments. It discusses the potential benefits and limitations of using AI for code commenting, highlighting its utility in improving code comprehension for developers.

Key Takeaways

The use of GitHub Copilot for code commenting can significantly enhance code readability and maintainability by automatically generating explanatory comments.
Evaluating the accuracy and helpfulness of AI-generated comments is crucial to ensure they provide meaningful insights into the code's functionality.
The quality of generated comments may vary based on the complexity and context of the code, suggesting a need for human oversight and review.
GitHub Copilot's comment generation capability has the potential to streamline the documentation process, allowing developers to focus on more complex tasks.
The effectiveness of AI-assisted coding tools like GitHub Copilot depends on their integration into existing development workflows and their ability to adapt to specific coding styles and requirements.

Comment to Code | GitHub Copilot Patterns & Exercises

This document provides a comprehensive guide to using GitHub Copilot, an AI-powered code generation tool. It covers various patterns and exercises to effectively utilize Copilot for tasks such as code completion, comment to code generation, and refactoring. The guide includes specific examples and exercises, like generating a function to calculate the maximum number in an array based on comments. It also discusses design patterns, collaboration techniques, and testing strategies to maximize the benefits of using Copilot. The document is structured into sections including General, Client Side Tips, Design Patterns, Collaboration, Test, and Refactoring, offering a wide range of tips and best practices for developers.

Key Takeaways

The document highlights the potential of GitHub Copilot to significantly enhance developer productivity by generating code based on comments and context, but also emphasizes the need for careful review and testing of generated code.
Effective use of Copilot requires adopting specific design patterns and practices, such as AI-readable naming conventions and high-level architecture planning, to ensure that the generated code is maintainable and aligns with project requirements.
The guide suggests that Copilot can be particularly useful for tasks like unit test generation and code refactoring, but notes that human oversight is crucial to ensure the quality and relevance of the generated code.
By providing exercises and checklists for further learning, the document encourages developers to experiment with Copilot and develop their skills in using this tool effectively.
The document implies that the successful integration of Copilot into development workflows may require adjustments in how developers work, including changes in documentation practices and collaboration strategies.

Code Completion | GitHub Copilot Patterns & Exercises

This document provides a comprehensive guide to using GitHub Copilot, an AI-powered code completion tool, including various patterns and exercises to enhance developer productivity. It covers general features such as code completion, comment to code, and code to comment, as well as more advanced topics like design patterns, collaboration, testing, and refactoring. The guide includes practical exercises and checklists for further learning, aiming to help developers effectively utilize GitHub Copilot in their workflow. It discusses how to improve code quality, write more precise code, and leverage Copilot's capabilities for tasks beyond simple code completion, such as generating unit tests, documentation, and even entire code snippets from natural language descriptions.

Key Takeaways

The document highlights the potential of GitHub Copilot to significantly enhance developer productivity through AI-assisted code completion and other features.
Effective use of GitHub Copilot requires understanding how to craft appropriate prompts and provide context to get precise and robust code suggestions.
The guide emphasizes the importance of testing and refactoring when using AI-generated code, suggesting that developers should write test code before refactoring and focus on making calculation parts independent.
GitHub Copilot can be used for a variety of tasks beyond code completion, including generating documentation, unit tests, and even helping with code readability through AI-friendly naming conventions.
To maximize the benefits of GitHub Copilot, developers need to be aware of its limitations, such as the need for error handling and robustness in generated code, and take steps to address these limitations.

GitHub Copilot · Your AI pair programmer

GitHub Copilot is an AI-powered coding assistant that provides contextualized assistance throughout the software development lifecycle. It offers various pricing plans, including Free, Pro, Business, and Enterprise, each with different features and limitations. The tool is trained on publicly available code and natural language text, and its suggestions are generated using probabilistic determination. GitHub Copilot integrates with leading editors and IDEs, and its features include code completion, chat assistance, and code explanations. The tool also includes filters to block offensive language and detect vulnerable code patterns. GitHub Copilot is designed to make developers more efficient, but it is not intended to replace them. The tool's data retention policies vary depending on the plan and usage, and GitHub does not use Business or Enterprise data to train its models.

Key Takeaways

GitHub Copilot's pricing plans cater to different user needs, from individual developers to large enterprises.
The tool's AI model is trained on publicly available code and natural language text, raising intellectual property considerations.
GitHub Copilot includes features to mitigate risks, such as code referencing and duplication detection filters.
The tool is designed to augment developer productivity, not replace it, and should be used with the same safeguards as third-party code.
GitHub Copilot's data retention policies and usage vary depending on the plan and user configuration.

GitHub Copilot · Your AI pair programmer

GitHub Copilot is an AI-powered coding assistant that provides contextualized assistance throughout the software development lifecycle. It integrates with leading editors and is natively built into GitHub, offering features such as code completions, chat assistance, and code explanations. GitHub Copilot is available in various plans, including Free, Pro, Business, and Enterprise, each with different features and pricing. The tool is designed to improve developer productivity and satisfaction, with users reporting up to 75% higher job satisfaction and 55% increased productivity. GitHub Copilot is trained on publicly available code and natural language text, and includes filters to block offensive language and detect vulnerable code. The tool also provides features such as code referencing and duplication detection to mitigate copyright risks.

Key Takeaways

GitHub Copilot improves developer productivity and satisfaction by providing AI-powered coding assistance throughout the software development lifecycle.
The tool includes features such as code referencing and duplication detection to mitigate copyright risks associated with AI-generated code suggestions.
GitHub Copilot is available in various plans, including Free, Pro, Business, and Enterprise, each with different features and pricing to suit different user needs.

Tab - Windsurf Docs

This document provides an overview of the features and configuration options for an AI-powered editor. It covers various aspects such as keyboard shortcuts, tab navigation, settings, and advanced features like Fill In The Middle (FIM) and Terminal Context Awareness. The editor is designed to provide a powerful next-intent prediction experience with a single keystroke. It includes features like Tab to Jump and Tab to Import, which enhance navigation and code completion. The document also touches upon other related topics like models, command, code lenses, terminal, previews, and security. Additionally, it references other components such as Cascade, Browser, Accounts, and Context Awareness, indicating a comprehensive ecosystem around the editor.

Key Takeaways

The AI-powered editor is designed to streamline coding with features like next-intent prediction and context awareness.
Keyboard shortcuts and tab navigation are crucial for efficient use of the editor, with customizable settings.
Advanced features like FIM and Terminal Context Awareness suggest a deep integration of AI in coding tasks, potentially improving productivity.

Windsurf Tab | Windsurf

Windsurf Tab is an AI-powered coding tool that provides context-aware autocomplete, multi-file inline edits, and file navigation. It integrates with the Windsurf Editor and IDE plugins to offer features like Supercomplete for multiline edits and Cascade for advanced AI-assisted coding. The tool accesses context from the terminal and clipboard to make smarter completions. Windsurf Tab is designed to keep developers in a 'flow state' by providing seamless and intelligent coding assistance. The company behind Windsurf, Exafunction, Inc., offers various products and resources, including the Windsurf Editor, plugins, and documentation.

Key Takeaways

Windsurf Tab's AI-powered coding capabilities have the potential to significantly improve developer productivity by providing context-aware suggestions and automating complex coding tasks.
The integration of Windsurf Tab with the terminal and clipboard allows for more accurate and relevant code completions, setting it apart from other coding assistance tools.
The company's focus on maintaining a 'flow state' for developers suggests that Windsurf Tab is designed to minimize distractions and maximize coding efficiency, which could have implications for software development workflows and productivity.

The Next Chapter

Windsurf, a company specializing in agentic IDE for software development, has been acquired by Cognition, the creator of Devin, the world's first autonomous software engineering agent. The acquisition aims to combine Windsurf's agentic IDE with Cognition's autonomous agents to create a breakthrough developer experience. Windsurf's team and IP will be integrated into Cognition, enhancing AI coding capabilities. The deal brings together two world-class teams to shape the next era of AI coding, with a focus on human-AI collaboration and amplification. Windsurf's customers and users will benefit from better product innovation, and the joint team will continue to push the frontier of AI coding capabilities. Cognition has raised over $300M and was valued at $4B, demonstrating its strong financial backing and customer pipeline.

Key Takeaways

The acquisition combines Windsurf's agentic IDE with Cognition's autonomous agent Devin, potentially revolutionizing AI-assisted coding by enabling seamless task delegation and execution within a single environment.
The integration is expected to create a 'fearsome combo' that will significantly enhance developer experiences and push the boundaries of AI coding capabilities.
The deal highlights the growing importance of human-AI collaboration in software development, moving beyond mere automation towards true amplification of developer capabilities.

Windsurf Editor | Windsurf

The Windsurf Editor is an AI-native IDE that combines advanced tools and real-time awareness to create a seamless coding experience. It features Cascade, a powerful tool that provides deep contextual understanding and generates accurate code. The Editor includes features like autocomplete, Windsurf Previews, linter integration, and Model Context Protocol (MCP) for enhanced AI workflows. With over 1 million active users and backing from major companies like JPMorgan Chase and Mercado Libre, Windsurf aims to turbocharge developer productivity and output. The Editor is designed to understand software development, customize to unique workflows, and become a perfect pair programmer. It has been adopted by 59% of Fortune 500 companies and has written over 70 million lines of code daily.

Key Takeaways

The Windsurf Editor represents a significant evolution in AI-assisted coding by integrating deep codebase understanding and real-time awareness, potentially revolutionizing developer productivity.
By providing features like Cascade and Windsurf Previews, the Editor enables developers to see, shape, and ship code instantly, streamlining the development process.
The adoption of Windsurf by major companies and 59% of Fortune 500 companies underscores its potential to accelerate software development for mission-critical systems.

Windsurf - The most powerful AI Code Editor

The Windsurf Editor is a new AI-powered IDE that combines deep codebase understanding, advanced tools, and real-time awareness to create a seamless and collaborative coding experience. Its key features include Cascade, an AI agent that codes, fixes, and thinks ahead; Windsurf Tab, a single keystroke that enables multiple actions; and Memories, which remembers important codebase and workflow details. The IDE also includes features like lint fixing, MCP support, and drag-and-drop images. With over 1 million users and 4,000 enterprise customers, Windsurf Editor is trusted by developers and enterprises worldwide, with 94% of code written by AI. The IDE is available on JetBrains and offers solutions for both individual developers and organizations.

Key Takeaways

The Windsurf Editor's AI-powered features, such as Cascade and Windsurf Tab, significantly enhance developer productivity by automating complex tasks and providing a seamless coding experience.
The IDE's ability to remember important codebase and workflow details through Memories and its support for MCPs and various plugins make it a versatile tool for developers.
With its strong user adoption and enterprise customer base, Windsurf Editor is poised to become a leading AI coding assistant, as praised by industry experts and users alike.

codex/docs/getting-started.md at main · openai/codex

The document provides a comprehensive guide to getting started with Codex CLI, a command-line interface for interacting with Codex. It covers basic CLI usage, including commands and flags, as well as more advanced features such as using AGENTS.md files for extra instructions and guidance. The guide also includes example prompts and tips for using the CLI effectively. Codex CLI allows users to perform various tasks such as refactoring code, generating SQL migrations, writing unit tests, and explaining complex concepts. It also supports features like image input, shell completions, and customizable working directories.

Key Takeaways

Codex CLI offers a range of features for automating and streamlining development tasks, including code refactoring, test generation, and security vulnerability detection.
The use of AGENTS.md files allows users to provide additional context and guidance to Codex, enhancing its ability to understand and respond to specific project needs.
The CLI's support for image input and customizable working directories adds to its flexibility and usability in various development environments.

openai/codex: Lightweight coding agent that runs in your terminal

The OpenAI Codex CLI is a lightweight coding agent that runs locally on a user's computer. It can be installed globally using npm or brew and supports various configuration options stored in ~/.codex/config.toml. The repository is licensed under the Apache-2.0 License and is primarily written in Rust. The Codex CLI supports MCP servers and has features like zero data retention and non-interactive/CI mode. Users can sign in with their ChatGPT account or use an API key. The repository contains documentation for getting started, CLI usage, and advanced features.

Key Takeaways

The Codex CLI offers flexible configuration options through ~/.codex/config.toml, allowing users to customize their experience.
The repository is primarily written in Rust, indicating a focus on performance and reliability.
The Codex CLI supports integration with ChatGPT plans, enabling users to leverage their existing subscriptions.

Codex CLI

The OpenAI Codex CLI is a coding agent that can read, modify, and run code on a local machine to help build features faster, squash bugs, and understand unfamiliar code. It is open source and available on GitHub. The Codex CLI can be installed using a package manager like npm or Homebrew. It runs in an interactive terminal UI, allowing users to ask Codex to perform tasks, and it can read the codebase, make edits, and run commands. Codex has different approval modes, including Auto, Read Only, and Full Access, which determine its level of access to the file system and network. The tool can be used with different models, including GPT-5, and allows for scripting using the exec command. The Codex CLI officially supports macOS and Linux, with experimental support for Windows through WSL.

Key Takeaways

The Codex CLI offers flexible approval modes to balance automation and security, allowing users to switch between Auto, Read Only, and Full Access modes depending on their needs.
Using GPT-5 with Codex is recommended for complex tasks, and users can adjust the reasoning level to suit their requirements.
Codex can be scripted using the exec command, enabling non-interactive usage and automation of tasks.
While the Codex CLI is open source and rapidly improving, users should be cautious when using Full Access mode, as it allows Codex to make changes without approval.

Features | Cursor - The AI Code Editor

Cursor is an AI-powered code editing tool that offers various features to enhance developer productivity. It includes a powerful autocomplete function that suggests edits across multiple lines, taking into account recent changes. The tool also features multi-line edits, smart rewrites, and an agent mode that completes tasks end-to-end while keeping programmers in the loop. Cursor can understand a codebase using custom retrieval models, reducing the need for manual context addition. It can automatically write and run terminal commands, detect lint errors, and apply fixes. The tool also allows referencing code with @ symbols, using images, and searching the web for up-to-date information. Additionally, Cursor enables inline editing and writing code with AI using ⌘ K, and supports referencing popular libraries and documentation.

Key Takeaways

Cursor's AI-powered features significantly enhance developer productivity by automating tasks such as code completion, error detection, and terminal command generation.
The tool's ability to understand a codebase using custom retrieval models reduces manual effort and improves accuracy.
Cursor's integration with various features like image inclusion, web search, and documentation reference expands its capabilities beyond traditional code editing.

Cursor - The AI Code Editor

Cursor is an AI-powered code editor designed to significantly enhance developer productivity. It offers features such as predictive code completion, natural language editing, and intelligent code suggestions. Trusted by engineers at top companies, Cursor is described as a revolutionary tool that streamlines coding processes. Key features include tab completion, codebase awareness, and the ability to edit code using natural language instructions. The tool is powered by a mix of purpose-built and frontier AI models, making it both intelligent and fast. Cursor also prioritizes user familiarity by allowing the import of extensions, themes, and keybindings from other editors. It is SOC 2 certified and offers privacy options, including a Privacy Mode that ensures code is not stored remotely without consent. Users praise Cursor for its seamless integration of AI capabilities, describing it as a significant improvement over existing tools like Copilot.

Key Takeaways

Cursor represents a substantial advancement in AI-assisted coding tools, offering features like predictive code completion and natural language editing that significantly enhance developer productivity.
The tool's ability to understand and interact with the user's codebase, combined with its AI-driven suggestions, positions it as a valuable asset for software development teams.
Cursor's emphasis on privacy and security, including SOC 2 certification and optional Privacy Mode, addresses critical concerns for developers and organizations handling sensitive codebases.

Usage | aider

The document provides a comprehensive guide on using aider, a tool that assists in editing source code files using Large Language Models (LLMs). It explains how to run aider with specific files, use in-chat commands for various operations, and switch between different LLMs. The guide also covers best practices for adding files to the chat session, understanding how aider pulls in relevant context from the codebase, and utilizing features like git commit for change tracking. Additionally, it lists supported LLMs and provides instructions for specifying coding conventions, linting, and testing. The document is structured with a table of contents that includes topics such as tips, in-chat commands, chat modes, and integrating aider with IDEs and browsers.

Key Takeaways

The effectiveness of aider depends on carefully selecting which files to add to the chat session to avoid overwhelming the LLM.
Aider's ability to pull in relevant context from the rest of the codebase enhances its capability to make accurate edits.
Switching between different LLMs during a chat session allows for flexibility in handling various coding tasks.
Using aider with git commit for change tracking facilitates easy undoing of changes and maintaining a clear history of edits.
The guide highlights the importance of specifying coding conventions and utilizing linting and testing to ensure high-quality code edits.

Aider - AI Pair Programming in Your Terminal

Aider is an AI pair programming tool that integrates with various Large Language Models (LLMs) to assist in code development. It supports over 100 programming languages, maps codebases, and automatically commits changes with sensible commit messages. Aider can be used within IDEs, allows for voice-to-code functionality, and includes features like linting and testing. It is compatible with cloud and local LLMs, including Claude 3.7 Sonnet, DeepSeek R1 & Chat V3, and OpenAI models. Users can get started by installing Aider and connecting it to their preferred LLM using API keys. The tool has received positive feedback from users, with many reporting significant productivity gains.

Key Takeaways

Aider's integration with multiple LLMs and support for over 100 programming languages makes it a versatile tool for diverse development projects.
The tool's ability to map codebases and automatically commit changes enhances its utility in larger, complex projects.
Aider's voice-to-code feature and compatibility with IDEs offer a more streamlined and interactive coding experience, potentially revolutionizing the way developers work.

Claude Code: Deep coding at terminal velocity \ Anthropic

Claude Code is an AI-powered coding assistant that integrates directly into a developer's terminal, providing advanced code understanding and generation capabilities. It uses the Claude Opus 4.1 model to analyze entire codebases, make coordinated changes across multiple files, and interact with various development tools. Claude Code supports various IDEs, including VS Code and JetBrains, and can be used for tasks such as code onboarding, issue triage, refactoring, and feature implementation. It is designed to work alongside existing development workflows without requiring significant changes. The tool is available through various pricing plans, including individual, team, and enterprise options, and can be integrated with Anthropic's API for custom usage.

Key Takeaways

Claude Code revolutionizes coding by integrating AI directly into the terminal, enhancing developer productivity and efficiency.
The tool's ability to understand entire codebases and make complex, multi-file edits sets it apart from other AI coding assistants.
Claude Code's flexibility in working with various IDEs and development tools makes it adaptable to different development environments and workflows.

Frequently Asked Questions

How do the context management approaches of Cline Memory Bank, Windsurf Cascade, and Aider's codebase mapping compare in terms of effectiveness for large-scale software projects?
What are the key architectural differences between cloud-based tools like Cursor and Windsurf versus terminal-based solutions like Aider and Claude Code, and how do these impact developer adoption?
How do the enterprise security and compliance features of Gemini Code Assist Enterprise and Amazon Q Developer compare with the open-source transparency offered by Cline and Aider?
What evidence exists for Claude 3.5 Sonnet's superiority in coding tasks across different tools, and how does this model dominance affect competitive dynamics in the AI coding space?
How do the autonomous capabilities of Devin compare with the agentic features of Windsurf Cascade and Cursor's Agent mode in terms of actual software engineering task completion?
What are the implications of the shift from subscription-based pricing (GitHub Copilot) to usage-based models (Claude Code, OpenAI Codex) for developer adoption and tool economics?
How do the memory and learning systems across different tools (AGENTS.md files, Memory Bank, MCP servers) enable persistent context and knowledge retention?
What role do integrations with existing developer tools (VS Code, JetBrains, GitHub, terminal) play in determining the success of different AI coding assistants?
How do the fine-tuning and customization capabilities mentioned in the Personal Copilot document compare with the enterprise customization features of tools like Tabnine and Gemini Code Assist?
What are the trade-offs between the 'human-in-the-loop' approach of most current tools versus the fully autonomous approach demonstrated by Devin in real-world software development scenarios?