
Replit has rapidly emerged as a go-to platform for coders of all levels, offering a powerful, browser-based IDE, seamless collaboration, and integrated AI tools that aim to democratize software creation. But this an innovative platform isn't just built *for* developers; it's significantly shaped *by* developers who are its own most active and critical users. Replit's own teams—from engineering and product to IT and operations—rely extensively on their own platform, creating a potent "dogfooding" culture that directly fuels innovation, refines user experience, and stress-tests new features in a real-world environment.

### The Replit on Replit Philosophy: Building Tools They Need

At its core, Replit's mission is to make programming more accessible, collaborative, and powerful. This vision, championed by CEO Amjad Masad, translates into an internal culture where using Replit for daily tasks isn't just encouraged, it's often the most efficient way to get things done.

* **Developing Internal Tools:** Replit's platform is designed to be versatile, and its own IT and operations teams leverage this by building custom internal tools directly on Replit. As highlighted on their [use cases page for IT teams](https://replit.com/usecases/it), Replit enables the rapid creation and deployment of mission-critical internal apps without complex setups. This internal need for custom solutions provides a constant stream of real-world use cases that push the boundaries of what the platform can do, leading to improvements that benefit all users creating their own applications. A [customer story about Zinus](https://blog.replit.com/zinus-customer-story) saving over $140,000 by using Replit for in-house tool development mirrors the kind of efficiency and direct feedback loop that Replit's own teams likely experience and foster.
* **Streamlining Software Development:** For Replit's own software engineers, the platform offers a way to "eliminate the overhead that slows development—from environment setup to deployment," as stated on their [page for software engineers](https://replit.com/usecases/software-engineers). By using their own cloud IDE, collaborative features, and integrated deployment pipelines, Replit's engineering teams can experience firsthand the benefits and identify any friction points in the development lifecycle they offer to their users. This intimate understanding helps them build a more "fast and frictionless" engineering cycle for everyone.

### Replit AI: An Internal Coding Partner Driving Innovation

A cornerstone of Replit's recent innovation is **Replit AI**, encompassing tools like Replit Agent and Replit Assistant. These AI-powered coding partners are designed to help developers write, debug, and refactor code more efficiently. It's clear that Replit's own engineering teams are among the first and most intensive users of these AI capabilities.

The [Replit documentation on efficient prompting with Replit AI](https://docs.replit.com/tutorials/effective-prompting) demonstrates a deep, practical understanding of how to interact with these AI tools – an understanding likely honed through extensive internal use. Furthermore, a [Google Cloud case study on Replit](https://cloud.google.com/customers/replit) reveals that advancements in AI models (like Anthropic's Claude on Vertex AI) actually "accelerated its product roadmap for Replit Agent." This indicates a dynamic where internal experimentation with cutting-edge AI, using their own platform as the testbed, directly shapes the AI features offered to users. Replit's team effectively "vibe codes" with their own AI, pushing its capabilities and refining its usefulness for a multitude of development tasks.

### Collaboration as a Core Tenet, Tested Internally

Replit heavily emphasizes real-time, multiplayer collaboration, likening it to Google Docs for code. For a distributed company like Replit, these collaborative features are not just a product offering but a necessity for their own internal teamwork. Engineers, designers, and product managers using Replit to build Replit itself would rely on these tools for:

* **Pair Programming and Code Reviews:** Working together in shared workspaces, regardless of physical location.
* **Real-time Debugging:** Collectively troubleshooting issues within the same environment.
* **Seamless Handoffs:** Ensuring projects and context are easily shared across team members.

This constant internal stress-testing of collaborative functionalities ensures they are robust, intuitive, and genuinely enhance team productivity – lessons that directly translate into a better experience for teams using Replit externally. The [launch of Replit Teams](https://blog.replit.com/teams) was explicitly aimed at making software creation more of a team sport, addressing the needs they observed from users bringing Replit to work, a trend their own teams would exemplify.

### Benefits of Coding Their Own Reality

This deep immersion in their own product offers Replit several key advantages:

* **Unparalleled User Empathy:** Replit's developers don't have to guess what users need; they *are* users, facing the same challenges and seeking the same efficiencies.
* **Rapid Feedback and Iteration:** Bugs, usability hurdles, and feature requests can be identified and addressed much faster when the feedback loop is internal.
* **Real-World Validation:** New features and platform updates are tested in genuine development scenarios before reaching the broader community.
* **Innovation Driven by Need:** Many of Replit's most powerful features, particularly around AI and collaboration, are likely born from the internal drive to make their own development process better.
* **Authenticity:** When the company building the tools uses those same tools to achieve its own ambitious goals, it lends immense credibility to the platform. As CEO Amjad Masad often discusses, the vision is to make software development easier and more accessible (Source: [Lenny's Podcast via Musixmatch](https://podcasts.musixmatch.com/podcast/lennys-podcast-product-growth-career-01hjz7abr0w71n49nd313an6as/episode/behind-the-product-replit-amjad-masad-co-founder-and-01jd7a2aa8365tr4k9tt4c73qn)).

### Building for Everyone, Starting with Themselves

While "dogfooding" ensures a product is robust and meets the needs of its creators, it's also important to balance internal perspectives with the diverse needs of a global user base, which includes everyone from beginners learning to code to experienced engineers building complex applications. Replit's mission to empower *all* creators suggests an awareness of this, and their community engagement and user feedback mechanisms likely complement the insights gained from internal use.

The general principles of dogfooding, as outlined by sources like [HeroCoders](https://www.herocoders.com/blog/dogfooding-in-software-development-eat-your-own-code), highlight benefits like improved product quality and faster feedback, alongside risks like potential tunnel vision if not balanced with external testing. Replit's highly iterative nature and its large, vocal community likely help mitigate these risks.

### Conclusion: The Replit Virtuous Cycle

Replit's approach to product development is a powerful example of a company living its own vision. By making their online IDE, collaborative tools, and cutting-edge AI coding assistants integral to their own workflows, they create a virtuous cycle. Internal needs drive innovation, internal usage refines usability and reliability, and the resulting platform empowers millions of developers around the world to build the next generation of software. Replit isn't just building a place to code; they're coding the future of how we build, and they're doing it, first and foremost, for and with themselves.

Coding Their Own Reality: How Replit Builds the Future of Development by Using Itself


The rise of Large Language Models (LLMs) has unlocked a universe of possibilities for application development. Yet, harnessing their power effectively requires sophisticated orchestration, integration with external data sources, and robust observability. Enter LangChain, the open-source framework that has rapidly become a cornerstone for developers looking to build context-aware, reasoning applications powered by LLMs. But LangChain Labs, the driving force behind this ecosystem, isn't just shipping code; they are deeply enmeshed in using their own creations, effectively "chaining themselves" to their own tools to forge the future of LLM application development.

### Building the Scaffolding While Standing On It

LangChain provides a comprehensive suite of tools: the core **LangChain** framework for composing LLM workflows (chains, agents, RAG), **LangSmith** for debugging, testing, evaluating, and monitoring LLM applications, **LangServe** for deploying these applications as APIs, and the newer **LangGraph** for building stateful, multi-actor applications. For a company whose mission is to simplify the complex and provide developers with composable building blocks, being the first and most demanding user of these tools is not just a good idea—it's practically a necessity.

Harrison Chase, co-founder and CEO of LangChain, has consistently emphasized a developer-first approach. As noted in a [profile by The Key Executives](https://www.thekeyexecutives.com/2025/05/16/inside-langchains-rise-why-harrison-chase-focuses-on-function-over-flash/), Chase is "most animated when talking about user experience, seeing transparency, editability, and collaboration as critical ingredients in the success of AI agents." This focus on practical, trustworthy systems suggests a development process deeply informed by the act of building itself.

While LangChain Labs doesn't have a plethora of public "we built feature X using LangChain in Y way" blog posts (common for many developer tool companies focused on enabling others), the nature of their work implies constant internal application:

* **Developing LangChain with LangChain:** As the framework evolves, adding new integrations (over 600, as mentioned by [Sequoia Capital](https://www.sequoiacap.com/article/partnering-with-langchain-the-llm-application-framework/)), modules, and abstraction layers, the LangChain team itself is ideally positioned to use the existing parts of the framework to test and build these new components. For example, when creating a new LLM provider integration, they would naturally use LangChain's existing model I/O abstractions and potentially LangSmith to trace its behavior.
* **LangSmith as an Indispensable Internal Tool:** LangSmith was born out of the critical need for better observability in LLM applications. As developers of a framework that enables complex, multi-step LLM interactions, the LangChain team would have been acutely aware of the debugging and tracing challenges. It's almost certain that early versions of LangSmith were used internally to understand the "inner workings and 'magic'" of LangChain applications, as described in an [Attempto Blog post on LangSmith](https://attempto.blog/blog/langchain-debugging-with-langsmith/). This internal proving ground would have been crucial for refining LangSmith into the comprehensive platform it is today, essential for any serious LangChain developer—including those at LangChain Labs.
* **Prototyping and Examples:** Creating documentation, tutorials (like the [simple LLM application guide on LangChain.js docs](https://js.langchain.com/docs/tutorials/llm_chain/)), and example applications to showcase LangChain's capabilities would inherently involve the team building extensively with their own framework. This process naturally uncovers usability issues, API quirks, and areas where abstractions can be improved.
* **Internal Tools and Automation:** Like any software company, LangChain Labs would have internal tooling needs—for CI/CD, documentation generation, community support bots, internal data analysis, etc. It's highly plausible they leverage LangChain to build some of these internal LLM-powered applications, gaining firsthand experience in deploying and managing them, potentially using LangServe for internal API endpoints.

### The "Dogfooding" Culture in a Developer-First Company

The concept of "eating your own dog food" is particularly potent for companies building tools for developers. A [MongoDB blog post](https://www.mongodb.com/blog/post/ai-powered-java-applications-with-mongodb-langchain4j) (while discussing their own dogfooding culture in the context of a LangChain4j integration) aptly states: "Without dogfooding, things like upgrades are taken for granted and customer pain points can be overlooked. Boost credibility and trust: Relying on our own software to power critical internal systems reassures customers of its dependability." This sentiment resonates deeply with what one would expect from LangChain.

By using LangChain, LangSmith, and LangServe for their own LLM-related development tasks, the LangChain team:

* **Gains Deep Empathy:** They experience the developer journey directly—the ease of certain integrations, the complexity of particular chains, the "aha!" moments, and the points of friction.
* **Ensures Practicality:** Features and abstractions are more likely to be grounded in real-world needs and complexities rather than purely theoretical constructs.
* **Accelerates Feedback Loops:** Bugs or awkward APIs encountered by an internal developer using the framework for a project can be communicated and addressed much more quickly than if discovered solely through external community bug reports.
* **Validates New Concepts:** When exploring new LLM orchestration patterns or agentic architectures (like those enabled by LangGraph), using these patterns for internal projects or prototypes serves as immediate validation.

### Learning and Iterating in the Open (and Internally)

LangChain's open-source nature means it benefits immensely from a global community of contributors and users. This external feedback is vital. However, the internal, day-to-day usage by the core team provides a different, often more intensive and immediate, feedback mechanism. They are likely the first to try out the newest, most experimental features and integrations.

This internal proving ground is especially important in the rapidly evolving LLM space. New models, techniques, and challenges emerge constantly. The LangChain team, by actively building with their own tools, can more quickly adapt the framework, ensure its components remain interoperable, and provide guidance to the broader community based on their firsthand experience. As highlighted in various sources like [Data Science Dojo](https://datasciencedojo.com/blog/what-is-langchain-key-features-tools-and-use-cases) and [Bacancy Technology](https://www.bacancytechnology.com/blog/langchain-for-llm-application-development), LangChain's modularity is a key strength, allowing developers to swap components and experiment easily—a practice the internal team would heavily rely on.

### Conclusion: Forging the Future of LLM Development, One Internal Chain at a Time

While LangChain Labs is focused on empowering the global community of AI developers, their most immediate and arguably most critical users are their own engineers and product teams. By rigorously applying the LangChain framework, LangSmith, LangServe, and LangGraph to their own development challenges, internal projects, and the evolution of the platform itself, they gain unparalleled insights. This "dogfooding" isn't just a best practice; it's an essential part of their process for building a robust, intuitive, and powerful ecosystem for the next generation of AI applications. As they build the tools to orchestrate LLMs, they are simultaneously orchestrating their own path to innovation, directly benefiting from the very framework they champion.

Chaining Themselves: How LangChain Builds the Future of LLM Apps by Using Its Own Framework


For millions of developers and organizations worldwide, GitHub is the central hub for software development—a place to host code, collaborate on projects, automate workflows, and increasingly, to leverage the power of AI. But GitHub isn't just a provider of these critical tools; it is, by its very nature and culture, its own most avid and demanding user. The principle of "eating your own dogfood," or as it's often put, "GitHub on GitHub," is fundamental to how the platform evolves, ensuring that the tools built for developers are forged in the fires of active, internal development by some of the world's most skilled engineers.

### The Ultimate Proving Ground: Building GitHub with GitHub

From the moment a GitHub engineer starts their day, they are immersed in the GitHub ecosystem. The platform's core functionalities are the bedrock of their own development lifecycle:

* **Repositories, Issues, and Pull Requests:** The very source code for GitHub.com, its myriad features, and its associated services like GitHub Mobile and GitHub Desktop are hosted on GitHub. Internal project management, bug tracking, feature requests, and extensive code reviews all happen within GitHub Issues and Pull Requests. This constant, high-stakes usage by thousands of "Hubbers" (GitHub employees) ensures these core collaborative tools are robust, intuitive, and continuously refined.
* **GitHub Actions for CI/CD and Automation:** GitHub Actions is a powerful workflow automation tool, and GitHub's own teams rely on it extensively for their continuous integration, testing, and deployment (CI/CD) pipelines. As highlighted in [GitHub's own resources](https://digital.ai/catalyst-blog/github-cicd/), Actions helps streamline the development process. By using Actions to build and deploy GitHub itself, engineers gain firsthand experience with its capabilities, identify areas for improvement, and ensure it can handle complex, large-scale automation tasks.
* **GitHub Codespaces for Development Environments:** GitHub Codespaces provides cloud-powered development environments. GitHub's own engineers are increasingly using Codespaces for their development work, allowing them to quickly spin up pre-configured environments, collaborate seamlessly, and ensure consistency across their distributed teams. This internal adoption drives improvements in performance, usability, and feature sets for all Codespaces users.
* **GitHub Copilot: The AI Pair Programmer, Used Internally:** With the advent of GitHub Copilot, an AI pair programmer, GitHub is at the forefront of AI-assisted development. It's a given that GitHub's own developers are among the earliest and most enthusiastic users of Copilot. As stated in the [GitHub Trust Center](https://github.com/trust-center), GitHub has completed Responsible AI Impact Assessments for its AI products. This internal usage and scrutiny are crucial for understanding Copilot's impact on developer productivity, code quality, and for refining its suggestions and capabilities. CEO Thomas Dohmke is a vocal proponent, seeing tools like Copilot as key to empowering a billion developers by 2030 (Source: [Sequoia Capital podcast with Thomas Dohmke](https://www.sequoiacap.com/podcast/training-data-thomas-dohmke/)).

### "Dogfooding" as a Core Engineering Principle

This practice of internal use is not accidental; it's a core part of GitHub's engineering culture. A [GitHub Blog post on engineering architecture](https://github.blog/engineering/architecture-optimization/introducing-sub-issues-enhancing-issue-management-on-github/) explicitly states: "**Dogfooding is a best practice at GitHub and it's how we build GitHub!**" The post details how they used their own "sub-issues" feature extensively while developing it, allowing them to experience the feature firsthand and identify pain points or areas for improvement.

This commitment to "dogfooding" offers several key benefits:

* **Deep Empathy with Developers:** GitHub's engineers experience the platform just as their users do. This fosters a profound understanding of developer needs, workflows, and frustrations, leading to more intuitive and effective tools.
* **Early Bug Detection and Quality Assurance:** Using features internally, in real-world development scenarios, is one of the most effective ways to catch bugs, performance issues, and usability quirks before they impact the broader community.
* **Rapid Iteration and Feedback:** The feedback loop is incredibly short. When a GitHub engineer encounters an issue or has an idea for an improvement while using GitHub, that feedback can be channeled directly to the relevant product and engineering teams.
* **Validation of New Features:** New ideas and experimental features can be prototyped and tested internally, providing quick validation of their utility and viability.
* **Driving Developer Experience (DevEx):** GitHub is keenly focused on Developer Experience. As Greg Mondello, Director of Product at GitHub, has emphasized, "improvements to the effectiveness of software development are inherently valuable." (Source: [Pulumi Blog](https://www.pulumi.com/blog/software-developer-experience-devex-devx-devops-culture/)) By prioritizing their own DevEx, GitHub ensures the platform remains a productive and satisfying environment for all developers.

### A Developer-First Culture

Thomas Dohmke, GitHub's CEO, often speaks about the company's developer-first ethos. "We are building the tools that other developers are using. We always say we put the developers first," he mentioned in the Sequoia Capital podcast. This philosophy means that the needs and experiences of their own highly skilled engineering workforce are paramount in shaping the platform. When GitHub's engineers demand better performance, more intuitive workflows, or more powerful automation, these demands often translate into features that benefit the entire GitHub community.

The company's engineering blogs, like the one on ["The art of engineering team focus,"](https://resources.github.com/developer-productivity/engineering-team-focus/?ref=feedle.world) share internal principles for shipping value, such as making work visible and defining it in small chunks. It notes, "At GitHub, this approach helped us evolve from shipping massive internal tools to making iterative improvements that benefited both our internal development teams and our customers," directly linking internal practices to external benefits.

### Challenges and Continuous Evolution

"Dogfooding" a platform as vast and complex as GitHub, especially while it's being actively used to develop itself, presents unique challenges. Managing updates, testing breaking changes, and ensuring stability for internal teams who rely on the platform for their daily work requires sophisticated engineering practices, likely involving feature flags, staged rollouts, and dedicated internal testing environments.

Furthermore, while GitHub's engineers represent an expert user base, the company must also ensure its platform remains accessible and intuitive for developers of all skill levels, from students and hobbyists to large enterprise teams. This balance is maintained through extensive community engagement, user research, and a strong Developer Relations program that constantly gathers feedback from the wider world.

### Conclusion: Building for Developers, By Developers

GitHub's commitment to using its own platform for every facet of its software development lifecycle is a powerful testament to its belief in its products and its dedication to the developer community. By being their own most critical "customer zero," GitHub's teams ensure that the tools they offer are not just feature-rich but also robust, reliable, and genuinely enhance the productivity and satisfaction of developers. This "GitHub on GitHub" approach is a fundamental reason why the platform continues to be the leading home for all developers, constantly evolving to meet the needs of a rapidly changing software landscape, with its own internal innovators leading the charge.

GitHub Builds on GitHub: How the Developer Platform Practices What It Preaches


In the rapidly evolving landscape of software development, AI is no longer a futuristic concept but an increasingly integral co-pilot. Cursor, an AI-first code editor built as a fork of VS Code, is at the forefront of this shift. What makes Cursor's development particularly fascinating is not just the advanced AI features it offers, but the fact that its own engineering team extensively uses Cursor to build and refine the very editor itself. This deep immersion creates a powerful, real-time feedback loop, shaping a tool that truly understands and anticipates developer needs.

### Cursor: More Than Just an Editor, An AI Collaborator

Cursor aims to transform the coding experience by embedding AI deeply into the developer's workflow. Its core philosophy revolves around making software development faster, smarter, and more intuitive. Key features that enable this include:

* **Codebase-Aware AI Chat:** Developers can "talk" to their entire codebase, asking questions, understanding complex logic, and getting contextual help without leaving the editor. Features like referencing files and symbols with "@" notation, using images as context, and even pulling in web search results (@Web) make this an incredibly versatile tool.
* **Intelligent Autocompletion & Multi-Line Edits (Tab):** Moving beyond single-line suggestions, Cursor's AI can predict and generate multiple lines of code, adapting to recent changes and existing patterns.
* **Inline AI Editing & Generation (Ctrl+K):** Developers can select code and instruct the AI to refactor, debug, or add functionality using natural language, or generate new code blocks from scratch.
* **Agent Mode:** A more autonomous AI capability designed to tackle tasks end-to-end, from understanding the context within a codebase to running terminal commands and iteratively fixing errors.
* **Customizable AI Guidance (`.cursorrules`):** Teams can create `.cursorrules` files to define project-specific coding standards, preferred frameworks, and other conventions, ensuring the AI's suggestions align with their established practices.

This suite of AI-driven tools is designed to feel native to the development process, turning the editor into an active partner in creation.

### Building Cursor, With Cursor: The Ultimate Internal Proving Ground

The most compelling aspect of Cursor's development story is the explicit and intense internal use of the product. Michael Truell, co-founder and CEO of Anysphere (the company behind Cursor), has highlighted that **"Cursor's rapid success was fueled by intense internal use (dogfooding) and iterative development, ensuring the product met real user needs."** ([Reddit, discussing a Latent Space podcast episode](https://www.reddit.com/r/cursor/comments/1kc7sdt/cursors_early_pivot_rapid_growth_lessons_on/)).

This means the engineers building Cursor are:

* **Their Own First Users:** Every new AI feature, every UI tweak, every performance optimization is immediately put to the test in the most demanding scenario: building a complex, AI-powered code editor.
* **Constantly Validating AI Efficacy:** When a Cursor developer uses the AI chat to understand a legacy module of Cursor's own codebase, or employs Ctrl+K to refactor a complex function within the editor, they are directly experiencing the AI's strengths and weaknesses.
* **Driving Practical Improvements:** If an AI suggestion is slightly off, if the context-awareness isn't quite right, or if a new AI-driven workflow feels cumbersome, the internal team feels that friction immediately. This accelerates the process of identifying, prioritizing, and implementing improvements.
* **Refining the Human-AI Interaction:** The vision of "vibe coding"—where developers guide the AI with higher-level intent—is something the Cursor team is living daily. Their experience shapes how AI prompts are best formulated, how AI suggestions are presented, and how developers can maintain control while benefiting from AI assistance.

### The Benefits of This AI-Powered Symbiosis

This unique development practice yields significant advantages that directly benefit Cursor users:

1.  **Rapid, Relevant Iteration:** Because the core users (Cursor's own developers) are also the creators, the feedback loop is incredibly tight. Issues are identified and addressed quickly, and new features are often born out of the team's direct needs while building the editor. User reports often praise the [fast pace of feature rollouts and model updates](https://forum.cursor.com/t/thank-you-cursor-team-unbeatable-value-for-developers/82502), a likely consequence of this internal agility.
2.  **Deep Understanding of Developer Pain Points:** The Cursor team isn't just guessing what developers need from an AI code editor; they are experiencing those needs firsthand. This leads to features that are genuinely useful and well-integrated into common coding workflows.
3.  **Enhanced AI Reliability and Context-Awareness:** Building a tool that can understand and operate on its own codebase is a significant technical challenge. By tackling this "meta-problem," Cursor pushes the boundaries of what AI can do in terms of code comprehension and generation for complex projects.
4.  **Practical Best Practices:** Features like `.cursorrules` likely emerged from the team's own efforts to ensure the AI consistently followed their internal coding standards. The best practices for interacting with the AI are discovered and refined through their daily work.

### Navigating the Meta-Challenges

Of course, using an AI-first tool to build that same tool isn't without its unique complexities:

* **Debugging the Debugger:** When the AI offers a suboptimal suggestion for Cursor's own code, is it an issue with the prompt, the underlying LLM, Cursor's context retrieval, or the code itself? This adds layers to the debugging process.
* **Avoiding AI-Induced Echo Chambers:** The team must be mindful that their advanced understanding of how to prompt and guide *their own* AI doesn't create features that are powerful but unintuitive for new users less familiar with the nuances of AI interaction.
* **Maintaining Developer Agency:** As AI becomes more capable, ensuring that the developer remains in control and that the AI acts as an assistant rather than an opaque decision-maker is a critical design philosophy that the team must constantly reinforce, even for themselves. User feedback (like the [AIM Research article](https://aimresearch.co/market-industry/cursor-thinks-youre-getting-too-dependent-on-ai) discussing AI "paternalism") highlights this as an ongoing industry-wide conversation.

Despite these potential complexities, the overwhelming benefit is a product that evolves with an almost unparalleled intimacy with its core purpose: to make developers more productive and innovative with the help of AI.

### Conclusion: The Future of Coding, Built by Its First Adopters

Cursor's approach of its own team being the primary, most intensive user of its AI-first code editor is a powerful testament to their confidence in the AI-assisted future of software development. By "eating their own AI cooking," they are not just testing features; they are living the developer experience they aim to create for others. This symbiotic relationship between the builders and their tool ensures that Cursor is continuously sharpened against the real-world challenges of software engineering, pushing the boundaries of what's possible when human ingenuity and artificial intelligence code together.

Articles tagged with "Developer Tools"

Coding Their Own Reality: How Replit Builds the Future of Development by Using Itself

Chaining Themselves: How LangChain Builds the Future of LLM Apps by Using Its Own Framework

GitHub Builds on GitHub: How the Developer Platform Practices What It Preaches

Coding with a Co-pilot: How the Cursor Team Builds Their AI-First Editor by Using It