The Haystack Editor is an open-source project developed by Haystack Software, designed to provide a user-friendly code editing experience. It combines the functionality of a traditional code editor with a canvas user interface, allowing users to visualize and understand their code more effectively. The editor supports comprehensive code editing, navigation, and understanding, along with lightweight debugging capabilities and a rich extensibility model. It integrates seamlessly with existing tools, making it a versatile choice for developers. The project is actively maintained, with weekly updates that introduce new features and address bugs. Users can download the Haystack Editor for various operating systems, including Windows, macOS, and Linux, directly from the Haystack website. Contributions to the Haystack Editor are encouraged, and there are multiple ways for individuals to get involved. Users can report bugs, request features, review code changes, and contribute to documentation. For those interested in coding, a guide on how to contribute is available, detailing the process of building the software from source, the development workflow, and how to submit pull requests. The Haystack Editor also includes a variety of built-in extensions that enhance its functionality. These extensions provide support for multiple programming languages, offering features like syntax highlighting and code completion. The project is structured to allow for easy integration of additional components, with many core features and extensions hosted in separate repositories on GitHub. The source code for the Haystack Editor is available under the PolyForm Strict License 1.0.0, ensuring that it remains open for community collaboration and development. The project has garnered attention, reflected in its star and fork counts on GitHub, indicating a growing interest and user base. Overall, the Haystack Editor aims to simplify the coding process while providing powerful tools for developers.

Mesh is a data and code editor that feels like a spreadsheet.

CodeHelium is a React editor component that compiles, executes, and returns the outputs of any user-written Python code using WebAssembly.

Canvas is an innovative interface introduced by OpenAI that enhances the way users can write and code with ChatGPT. This new feature allows for a more collaborative experience, moving beyond simple text-based interactions to a more integrated project environment. Canvas opens in a separate window, enabling users to work alongside ChatGPT on various writing and coding tasks, facilitating the creation and refinement of ideas in a more structured manner. The canvas interface is designed to improve collaboration by allowing users to highlight specific sections of their work, which helps ChatGPT understand the context and focus on particular areas. This functionality is akin to having a copy editor or code reviewer, as ChatGPT can provide inline feedback and suggestions while keeping the entire project in mind. Users maintain control over their projects, with the ability to directly edit text or code, utilize shortcuts for various tasks, and restore previous versions of their work. The introduction of canvas is particularly beneficial for coding, as it simplifies the iterative process of coding and makes it easier to track changes. Users can access coding shortcuts that allow for code reviews, debugging assistance, and even translating code into different programming languages. This structured approach aims to enhance the user experience by making it easier to manage and understand the revisions made by ChatGPT. To ensure that the model effectively collaborates with users, OpenAI has trained GPT-4o to recognize when to trigger the canvas feature for writing and coding tasks. The model has been fine-tuned to make targeted edits or rewrites based on user interactions, improving its ability to provide relevant feedback. The training process involved extensive evaluations and the use of synthetic data generation techniques to enhance the model's performance without relying solely on human-generated data. As the canvas feature is still in its early beta phase, OpenAI plans to continue refining its capabilities based on user feedback. This update marks a significant evolution in the ChatGPT interface, aiming to make AI more useful and accessible for a variety of tasks. The development team behind canvas includes a diverse group of researchers and engineers dedicated to enhancing the functionality and user experience of ChatGPT. Overall, canvas represents a major step forward in how users can interact with AI for writing and coding, providing a more dynamic and collaborative environment that encourages creativity and productivity.

Sandbox is an open-source cloud-based code editing environment with custom AI code autocompletion and real-time collaboration.

OpenAI has recently unveiled a new interface for ChatGPT called "Canvas," designed specifically for writing and coding projects. This innovative feature introduces a separate workspace that operates alongside the traditional chat window, allowing users to generate text or code directly within this canvas. Users can highlight portions of their work to request edits from the AI, enhancing the collaborative experience. The Canvas feature is currently in beta, available to ChatGPT Plus and Teams users, with plans to extend access to Enterprise and Edu users shortly thereafter. The introduction of editable workspaces like Canvas reflects a broader trend among AI providers, who are increasingly focusing on creating practical tools for generative AI applications. This new interface is similar to offerings from competitors such as Anthropic’s Artifacts and the coding assistant Cursor. OpenAI aims to keep pace with these competitors while also expanding the capabilities of ChatGPT to attract more paid users. While current AI chatbots struggle to complete extensive projects from a single prompt, they can still provide valuable starting points. The Canvas interface allows users to refine the AI's output without needing to rework their initial prompts, making the process more efficient. Daniel Levine, a product manager at OpenAI, emphasized that this interface facilitates a more natural collaboration with ChatGPT. In a demonstration, Levine showcased how users can generate an email using ChatGPT, which then appears in the canvas window. Users can adjust the length of the email and request specific changes, such as making the tone friendlier or translating it into another language. The coding aspect of Canvas offers unique features as well. For instance, when generating code, users can request in-line documentation to clarify the code's functionality. Additionally, a new "review code" button allows users to receive suggestions for code improvements, which they can approve or modify. OpenAI plans to make the Canvas feature available to free users once it exits the beta phase, further broadening access to this enhanced collaborative tool.

OpenAI has recently unveiled a new interface for ChatGPT called "Canvas," designed specifically for writing and coding projects. This innovative feature introduces a separate workspace that operates alongside the traditional chat window, allowing users to generate text or code directly within this canvas. Users can highlight portions of their work to request edits from the AI, enhancing the collaborative experience. The Canvas interface is currently in beta, available to ChatGPT Plus and Teams users, with plans to extend access to Enterprise and Edu users shortly thereafter. The introduction of editable workspaces like Canvas reflects a broader trend among AI providers, who are increasingly focusing on creating practical tools for generative AI applications. This new interface aligns with similar offerings from competitors, such as Anthropic’s Artifacts and the coding companion Cursor. OpenAI aims to enhance its capabilities in response to competition and to attract more paid users. While current AI chatbots struggle to complete extensive projects from a single prompt, they can still provide valuable starting points. The Canvas feature allows users to refine the AI's output without needing to rework their initial prompts, making the process more efficient. OpenAI's product manager, Daniel Levine, emphasized that this interface facilitates a more natural collaboration with ChatGPT. In a demonstration, Levine showcased how users can generate an email using ChatGPT, which then appears in the canvas. Users can adjust the email's length and request specific changes, such as making the tone friendlier or translating it into another language. The coding aspect of Canvas offers unique functionalities as well. For instance, when users prompt ChatGPT to create a web server in Python, they can add comments to the code for clarity and ask the AI to explain specific sections or suggest edits. A new "review code" feature will allow users to approve or modify the AI's suggestions for code improvements. Once the Canvas feature exits beta testing, OpenAI plans to make it available to free users, further expanding access to this innovative tool.

OpenAI has recently unveiled a new interface for ChatGPT called "Canvas," designed specifically for writing and coding projects. This innovative feature introduces a separate workspace that operates alongside the traditional chat window, allowing users to generate text or code directly within the canvas. Users can easily highlight sections of their work to request edits from the AI, enhancing the collaborative experience. The Canvas feature is currently in beta, available to ChatGPT Plus and Teams users, with plans to extend access to Enterprise and Edu users shortly thereafter. The introduction of editable workspaces like Canvas reflects a growing trend among AI providers, who are recognizing the need for practical tools that facilitate the use of generative AI. This new interface aligns with similar offerings from competitors, such as Anthropic’s Artifacts and the coding companion Cursor. OpenAI is actively working to enhance its capabilities and attract more paid users by introducing features that meet the demands of its audience. While current AI chatbots struggle to complete extensive projects from a single prompt, they can still provide valuable starting points. The Canvas interface allows users to refine the AI's output without needing to rework their initial prompts, making the process more efficient. OpenAI's product manager, Daniel Levine, emphasized that this interface offers a more intuitive way to collaborate with ChatGPT. In a demonstration, Levine showcased how users can generate an email using ChatGPT, which then appears in the canvas window. Users have the flexibility to adjust the length of the email and request specific changes, such as making the tone friendlier or translating the text into another language. For coding tasks, the Canvas interface offers unique features tailored to developers. Users can prompt ChatGPT to create code, such as an API web server in Python, and utilize an "add comments" button to generate in-line documentation. Additionally, users can highlight code sections to receive explanations or ask questions, and a new "review code" button will suggest edits for user approval. If users approve the suggestions, ChatGPT can attempt to fix any identified bugs. OpenAI plans to make the Canvas feature available to free users once it exits the beta phase, further expanding access to this innovative tool.

Aider has introduced an innovative approach to coding tasks by separating the processes of code reasoning and code editing. This new method employs two distinct models: an Architect model, which is responsible for conceptualizing how to solve a coding problem, and an Editor model, which takes the Architect's solution and generates specific code editing instructions to modify existing source files. This separation has led to state-of-the-art (SOTA) results in Aider's code editing benchmarks. The motivation behind this dual-model approach stems from the capabilities of OpenAI's o1 models, which excel in reasoning but often struggle to produce well-formatted code editing instructions. By allowing the Architect to describe the solution in its own terms and then passing that output to a more traditional language model (LLM) for editing, Aider can enhance the quality of code modifications. This method has gained traction due to recent advancements in the speed and cost-effectiveness of cutting-edge models, making it feasible to implement in an interactive coding environment. Traditionally, Aider would prompt a single model to both solve a coding problem and return formatted editing instructions in one go. This required the model to juggle the complexities of problem-solving and adhering to specific formatting requirements simultaneously. The new Architect/Editor framework divides this task into two steps: first, the Architect focuses solely on solving the problem, and then the Editor concentrates on formatting the necessary code edits. This division allows each model to operate within its strengths, with the Architect handling reasoning and the Editor managing the technicalities of code formatting. The results of this new approach have been promising. Benchmark scores indicate that pairing the o1-preview model as the Architect with either DeepSeek or o1-mini as the Editor achieved a remarkable score of 85%. This configuration, while effective, is noted to be slower and may not be practical for real-time interactions. However, using o1-preview with Anthropic's Sonnet as the Editor also yielded strong results, demonstrating a viable option for users who can access both models. The performance improvements are not limited to specific model pairings; many models benefit from the Architect/Editor configuration. For instance, models like Sonnet, GPT-4o, and GPT-4o-mini showed enhanced scores when used in this dual role setup. DeepSeek has also proven to be an effective Editor, adept at transforming proposed solutions into updated source files, particularly when using the efficient "diff" editing format. To facilitate this new functionality, Aider's development version includes built-in defaults for Architect/Editor coding with various models. Users can easily set up their environment to utilize these capabilities by installing Aider and configuring their API keys for the desired models. The introduction of the "architect" chat mode further streamlines the process, allowing users to engage with the system more intuitively. Overall, the separation of code reasoning and editing in Aider represents a significant advancement in AI-assisted programming, enhancing both the efficiency and effectiveness of coding tasks. The results from various model combinations underscore the potential of this approach, paving the way for more sophisticated and user-friendly coding experiences.

Deco is a real-time TypeScript web editor. You can code directly on the web or on your machine and get instant feedback.

Pragtical is a lightweight, highly extensible cross-platform text editor that prioritizes performance, ease of use, and open-source principles.

Quill is a rich text editor that has official TypeScript support, support for multiple editors on the same page with different configurations, auto-scrolling container detection, a smoother editing experience, and more.

Aider is a command-line tool that lets you directly edit code in your files while pair-programming with GPT. It will git commit changes with AI-generated commit messages.

Onlook enables developers to build their React + TailwindCSS apps visually in the browser. It's an open-source, local-first visual editor that lets you make live edits directly in the browser DOM.

This is a CodeMirror component for React. CodeMirror is a code editor component for the web, with a rich, comprehensive API that is easy to configure. This React-specific component supports React hooks, TypeScript, and more.

Paper Code is a fast and performant C++ IDE.

CodeViz is a VSCode extension for visualizing and navigating through your codebase's architecture and functions.

Syntax highlighting today uses color to distinguish code elements, but this doesn’t fully utilize the power of color to convey information. We could use dynamic color highlighting overlays to communicate things like scope, imports, argument flow, types, or even metadata about test failures.

Zed AI is a hosted service that integrates AI-powered coding into the Zed text editor, letting developers use LLMs while maintaining full control over their code. Zed has partnered with Anthropic to allow fast editing with Claude.

Heatbot.io is an innovative platform designed to transform user interaction data into actionable website improvements through a generative UI builder. By allowing users to upload heatmaps—visual representations of user engagement on their websites—Heatbot leverages artificial intelligence to generate optimized code for enhanced user interfaces in a matter of seconds. The process begins with users uploading a screenshot of the website or specific UI elements they wish to improve. Following this, they upload a corresponding heatmap image that illustrates how users interact with that part of the site. Users can then set specific improvement goals, choosing from a library of predefined options or creating custom objectives. Heatbot's AI analyzes the heatmap data alongside these goals to produce improved HTML, CSS, and JavaScript, accompanied by a detailed report on the enhancements made. Heatbot addresses the complexities involved in implementing changes based on heatmap data. It recognizes the challenges of accurately interpreting data, prioritizing changes, and balancing user experience with business objectives. The platform also alleviates the time and financial burdens associated with cross-device consistency, requiring collaboration among UX designers, marketers, and business stakeholders. Traditional methods often involve extensive A/B testing and significant investment in developer resources, making Heatbot's streamlined approach particularly appealing. The pricing structure of Heatbot offers various plans tailored to different user needs. The Basic plan is ideal for individuals and small projects, priced at $9 per month, allowing for limited project generations. The Pro plan, at $29 per month, caters to power users and small businesses, offering more projects and the ability to generate comprehensive reports. For larger enterprises, a custom solution is available starting at $2,990 per month, providing unlimited access and tailored support. User feedback highlights the effectiveness of Heatbot in simplifying the process of making data-driven design decisions. Many users express excitement about the potential for automated improvements in conversion rates and overall user experience. The platform's ability to generate high-converting pages based on collected data is seen as a significant advancement in the field of web design. Heatbot also provides answers to common questions, clarifying its capabilities in generating quality code based on user-defined frameworks and how its AI can enhance conversion rates by analyzing heatmap data in conjunction with website screenshots. This comprehensive approach positions Heatbot as a powerful tool for unlocking the full potential of website heatmap analysis, making it easier for users to implement effective design changes based on real user interactions. In summary, Heatbot.io stands out as a data-driven solution that empowers users to turn heatmap insights into tangible website improvements, streamlining the redesign process and enhancing overall user engagement.

htmx offers a simpler alternative to frameworks in frontend development by enhancing HTML with attributes that enable AJAX-like functionality, utilizing the hypermedia concept to create RESTful applications without excessive JavaScript.

Developer consoles in web browsers offer more than just text logging. You can use the %c format specifier to insert CSS, SVG, and even limited HTML directly into console.info() or console.log() output. This opens up possibilities for stylized text, animated graphics, and interactive elements right in the console. While there are limitations, like no external images or event listeners, this technique offers a unique way to enhance debugging and add surprises for curious users.

Srcbook allows developers to create TypeScript and JavaScript notebooks to prototype quickly. These notebooks can run code locally with a web interface, export to Markdown, and be shared easily.

Visual Studio Code (VSCode) has become a central tool in software development, yet its open-source nature is complicated by proprietary elements that create legal and functional challenges for users. Geoffrey Huntley reflects on the implications of using VSCode, particularly after reading Rob O'Leary's article on its extensive data collection practices. Huntley, no longer affiliated with Gitpod, expresses concerns about how Microsoft has transformed VSCode from a purely open-source project into a product that enforces a proprietary ecosystem, making it legally risky for users to utilize alternatives to Microsoft's official distribution. The blog post delves into the ecosystem of open-source forks of VSCode, such as VSCodium and OpenVSCodeServer, which aim to provide a version of the editor without Microsoft's telemetry and proprietary features. However, these alternatives face significant limitations, including the inability to connect to the Microsoft Visual Studio Code Marketplace, which further fractures the ecosystem. Huntley emphasizes that while these forks do a commendable job at disabling data collection, they cannot completely eliminate it due to the shared codebase with VSCode. Huntley also discusses Microsoft's broader strategy of transitioning from traditional software products to a service-oriented model, which has implications for the entire developer tooling landscape. This shift has led to a decline in non-subscription IDEs, as Microsoft increasingly positions VSCode and GitHub Codespaces as integral parts of its service offerings. The author warns that this strategy not only risks legal complications for companies that attempt to build on the open-source version of VSCode but also creates a dependency on Microsoft's proprietary tools and services. The blog post highlights the challenges faced by companies like Gitpod, GitLab, and others that adopt the open-source code of VSCode. These companies encounter legal barriers when trying to offer services that align with user expectations of the official VSCode experience, particularly in popular programming languages like .NET, Python, and Java. Huntley argues that Microsoft's control over the ecosystem allows it to strategically divide the market, making it difficult for competitors to thrive without adhering to Microsoft's proprietary framework. Huntley concludes by advocating for a renewed focus on open-source principles that prioritize user freedoms and combat the encroachment of proprietary software. He suggests that the future of software development tooling is at risk of becoming increasingly closed, and he calls for a movement to ensure that open-source remains a viable alternative to proprietary offerings. The blog post serves as a cautionary tale about the complexities of navigating the VSCode ecosystem and the potential consequences of relying on a platform that is designed to fracture its community.

DALL-E images can now be modified using a new editor interface from OpenAI that lets users describe changes using text prompts. Users can use the new select button to give specific instructions for a particular part of an image. Alternatively, users can make general changes to the image by entering a prompt in the chat sidebar.

Gemini Code Assist offers AI-powered assistance to software engineers in their IDE, similar to GitHub Copilot. It is available to try at no cost until July 11.

This article is a detailed walkthrough in building a basic code formatter, with code examples along the way. To create a code formatter, first transform the code into an abstract syntax tree (AST) and then build a formatting tree using nodes that represent concepts like text, whitespace, and indentation. Traverse the formatting tree, rendering each node into the final formatted code.

This is Atlassian's open-sourced drag-and-drop toolchain. It can be used with any view layer, such as React, Svelte, and Angular, and currently powers products like Trello, Jira, and Confluence.