In This Article
Introduction
In November 2025, Peter Steinberger built the first version of what would become OpenClaw in a single weekend. He didn't write every line of code manually. Much of it was generated through conversations with AI — high-level descriptions of what he wanted, followed by AI-produced code, followed by rapid iteration and refinement. This approach has a name: vibe coding.
Vibe coding is not a formal methodology with a specification document. It's a description of a working style — highly collaborative between human and AI, iterating quickly, prioritizing working software over architectural elegance, and relying on the AI's code generation capabilities to handle implementation details while the human focuses on design intent. It's how a significant portion of new software is being written in 2026, and OpenClaw is its most famous proof of concept.
What Is Vibe Coding?
Vibe coding sits on a spectrum between "writing all code manually" and "fully automated code generation." The developer operates more like a product manager and architect than a traditional programmer: they describe desired behavior, review and understand generated code, identify what needs to change, provide feedback, and iterate. The AI model generates the implementation details.
The term captures an intuition: the developer works at the level of vibes — high-level intentions, user experiences, system behaviors — rather than at the level of individual functions and algorithms. It trusts the AI to handle the translation from "I want users to be able to set a reminder" to the specific code that implements that feature.
This works because modern frontier models are extraordinarily good at generating working code from natural language descriptions. GPT-4, Claude Opus, and their successors can produce syntactically correct, architecturally reasonable implementations of described features — especially for well-understood problem domains like web services, API integrations, and data processing pipelines. The human's role shifts from implementation to direction and quality control.
How OpenClaw Was Built
Steinberger has been public about vibe coding's role in OpenClaw's development. The initial messaging relay — Telegram to Claude API to Telegram — was straightforward to describe and straightforward for AI to implement. But subsequent features were increasingly complex: the heartbeat scheduler, the Skills architecture, the memory file management system, the multi-channel adapter framework.
Each of these was developed through dialogue. Steinberger would describe a feature — "I want the agent to wake up every 30 minutes and check a Markdown file for tasks to complete" — and use AI tools (likely Claude and GPT-4 in combination) to generate the implementation. He would review, test, identify bugs or architectural concerns, describe fixes, and iterate. The cycle ran fast — multiple features per day during the initial development sprint.
The speed of this approach is what enabled a single developer to build a sophisticated agentic framework in weeks rather than the months or years a traditional development approach would have required. It's also what created the security vulnerabilities that surfaced later. Generated code tends to lack defense-in-depth security thinking — it implements the described feature without anticipating edge cases, adversarial inputs, or the attack vectors that experienced security engineers consider by habit.
Benefits of Vibe Coding
The benefits of vibe coding are most visible in the early stages of product development:
Speed: The iteration cycle is dramatically faster. Moving from idea to working prototype in hours rather than days enables much faster exploration of the design space. OpenClaw's first version in a weekend, and full agentic framework in weeks, would have been impossible without AI-assisted code generation.
Reduced barrier to entry: Vibe coding makes complex software development accessible to people who are not trained software engineers. A product manager, a researcher, or a domain expert with a clear understanding of what they want to build can produce working software that would have required a dedicated development team five years ago.
Focus on what matters: Developers working in vibe coding mode spend more time thinking about what software should do and less time thinking about how to implement it. For exploratory projects where the design is uncertain, this is often the more valuable use of cognitive resources.
Natural documentation: The conversation history between developer and AI that produces code serves as a kind of design document — a record of why each piece was built the way it was. This is different from code comments but provides valuable context for later maintenance.
Risks & Criticisms
OpenClaw's security vulnerabilities are the primary exhibit in any critique of vibe coding. AI-generated code tends to prioritize getting features working over thorough security, error handling, and edge case management. When you're moving fast and trusting the AI to implement details, subtle vulnerabilities in authentication flows, input handling, and privilege management can slip through.
A second criticism is architectural fragility. Software designed through rapid AI-assisted iteration often accumulates technical debt faster than carefully architected code. The components work individually but may interact in unexpected ways under edge conditions. Refactoring vibe-coded software can be difficult because the structural decisions were made implicitly by the AI rather than explicitly designed by a human architect.
Third: understanding. Traditional programming practice holds that developers should understand every line of code they ship. Vibe coding blurs this — the developer understands the intent of each module but may have limited insight into the specific implementation choices the AI made. This creates maintenance challenges and debugging difficulties when things go wrong.
Tools Used in Vibe Coding
The vibe coding workflow relies on AI coding assistants that are significantly more capable than traditional autocomplete tools. The primary tools in 2026:
- Cursor: An IDE built around AI assistance, allowing developers to describe changes in natural language and have the AI implement them across multiple files simultaneously. Widely used in the OpenClaw community.
- Claude in Projects mode: Anthropic's interface for maintaining context across long coding sessions, enabling the kind of sustained architectural dialogue that complex feature development requires.
- GitHub Copilot: Microsoft's widely-adopted AI coding assistant, integrated directly into VS Code and other editors. More conservative than Cursor but very capable for line-by-line and function-level generation.
- OpenClaw itself: In a meta twist, some OpenClaw developers use their agent to generate new OpenClaw code — feeding it specifications and having the agent produce implementation drafts, which the developer then reviews and refines.
The Future of Software Development
Vibe coding is not the future of all software development — critical systems, high-security applications, and complex distributed architectures still benefit from rigorous traditional engineering practice. But for the broad category of software that explores, experiments, and moves fast, vibe coding is already the dominant approach among practitioners who have adopted it.
The honest trajectory: as AI models become more capable, more reliable in security considerations, and better at understanding complex architectural requirements, vibe coding will cover more of the development landscape. The developers who will thrive are those who can direct AI effectively — who have the product sense, architectural intuition, and quality evaluation skills to get the most from AI code generation while catching its limitations.
Wrapping Up
Vibe coding built one of the fastest-growing open-source projects in history. It also built a system that accumulated serious security vulnerabilities. Both facts are true, and both are instructive. As a development methodology, it's extraordinarily powerful for speed and accessibility. As a production practice, it requires complementary security review, architectural oversight, and testing discipline to produce software that can be trusted at scale. OpenClaw's story is both vibe coding's greatest advertisement and its most important cautionary tale.