AI is changing the way software gets built, and nothing epitomizes this more than the rise of “vibe coding”. It’s where developers strap on a headset, listen to some funky tunes and enter text prompts to get AI to do all of the grunt work on their behalf, elevating them to more of a supervisory role and dramatically accelerating productivity to boot.
Simply enter a prompt and your AI coding model of choice, be it Cursor, AutonomyAI, Dreamspace or something else, will create the desired code. It can select the most appropriate open-source mobile, link it together with the rest of your code, and then deploy it where it needs to go in your application, while creating detailed documentation as to its purpose.
Who’s vibing in AI coding?
Vibe coding is rising fast due a perfect storm, with developer scarcity increasing and growing demand for agentic AI automation, coupled with investors furiously throwing their money around at startups, hoping for the next Replit moment.
1. Dreamspace Data Studio
An up-and-coming AI coding interface developed by the blockchain-based data warehouse startup Space and Time, Dreamspace is designed for developing smart contracts and big data applications that come with integrated crypto payment rails for automated payments. It’s a part of the Space and Time Studio platform, which is used to generate queries for AI prompts, with a Proof-of-SQL consensus mechanism that verifies accuracy of their responses on every outside data call.
Because Dreamspace Data Studio is an AI-generated coding tool specifically aimed at smart contract creation, it comes with some unique features for Web3 developers that set it apart from other vibe coding platforms. Besides its zero-knowledge proof-backed prompts, it also includes tools for estimating gas fees for smart contract calls, as well as an MEV estimator for crypto transactions, providing developers with crucial insights into deployment costs and potential network effects
2. AutonomyAI
AutonomyAI is an AI coding platform for front-end development that can create production-ready code in a matter of minutes. Unlike traditional coding tools that assist individual developers with their solo tasks, AutonomyAI goes further by integrating a suite of AI agents directly into a company’s codebase. The agents comprehend the company’s existing developmental patterns, coding style and nuances, so they can generate results that are in-line with internal expectations and long-term strategic goals. This deep level of integration means that the agents can perform a wide scope of development work, ranging from fetching UI specifications to writing components and delivering consistent, production-ready code.
Mending, a healthcare tech company, leveraged AutonomyAI’s Magician agent to enhance their user interface without distracting engineers from core roadmap work. The agent improved visual consistency, enabled rapid prototyping of new components directly in code, and refactored legacy UI, enabling a design and functional upgrade without sacrificing development velocity.
3. Replit AI
Replit AI was one of the first AI coding tools to make a splash and is considered a leading light in vibe coding with its AI Ghostwriter and one-click code deploy tools. It stands out for its cloud-based IDE and intelligent code completion features, and provides fully integrated version control with built-in GitHub integrations, making it simple to track changes, revert to previous versions and maintain a clean development history.
Replit AI has lots of traction due to its first-mover status and its comprehensive features, which include an array of tools for auditing the quality of its code outputs. Its built-in code review tool, which can help to suggest how to improve and optimize existing code, is incredibly useful. Alongside this, there’s an automated testing suite for putting freshly-generated AI code through its paces.
4. Cursor
Another of the early movers, Cursor is a VS Code-native AI copilot tool that’s able to rewrite entire code repositories, aid with code suggestions and optimize existing code, among other capabilities. The Cursor AI copilot integrates with VS Code, providing real-time coding suggestions and feedback to users, with the ability to respond to user prompts and generate code itself. Its intelligence is also visible in terms of its code understanding, providing useful insights that can help developers to better understand complex code bases, with full GitHub integration for version control.
Cursor’s Composer feature also has its own, internal versioning system that makes it quick and easy to revert to previous versions, which is especially useful to contend with code loss. For those who need to verify its AI-generated code, Cursor can easily be integrated with third-party testing tools, while its collaborative tools enable team members to review, analyze and provide feedback on any changes it makes.
5. Devin
Last on our list is Cognition Lab’s Devin, the autonomous AI software engineer that’s able to plan and execute complex tasks, essentially going all the way to create entire applications from a simple text prompt. As a fully-fledged AI agent, it also has the ability to contextually find and fix vulnerabilities in software.
Devin’s key features include the Devin AI agent that can write, test and deploy code, coupled with a unique contextual understanding that allows it to learn and adapt to new technologies, coding styles and frameworks as it goes. It lacks proprietary version control capabilities, but makes up for that with its the ability to integrate with GitHub to commit new code versions. In addition, it has received widespread praise for its bug finding and fixing capabilities, as well as its ability to suggest improvements to optimize code structure and quality.
What’s coming next?
Vibe coding has already shaken up the way software is created, but these tools are really only just getting started. In the next few months, we can expect to see many more agentic AI plugins and enhancements, with vibe coding platforms adding specialized AI agents designed to handle specific tasks in software creation.
There will also be increased focus on things such as safety guardrails to ensure full compliance with legal regulations around compliance. Meanwhile, the underlying LLMs that power AI-coding tools will evolve with new fine-tuning techniques and advancements in AI reasoning, enhancing their contextual understanding of existing codebases.
