Anti-AI Design Template: Stop AI From Generating Cookie-Cutter Websites

Why Do AI-Generated Websites Always Look the Same?

Open any AI-generated landing page, and you'll likely see these elements: a shiny star logo, a prominent "Beta" tag, a fake terminal window, and a bunch of unverified numbers. A designer with 20 years of experience said bluntly in a video: "Your AI landing page looks exactly like every other AI landing page." Behind this phenomenon is the tendency of AI coding tools to output highly templated interfaces when design constraints are absent.

These elements aren't wrong in themselves, but their combination has become a visual cliché. AI models learn the visual symbols of "techiness" from massive training data, but they can't judge whether those symbols actually serve the product. The result: no matter what product you build, AI gives you a page that feels familiar.

The Four Hallmarks of AI Design Templates

The video summarizes four key clues for spotting the "AI-generated feel":

1. Shiny star logo: Has become the default icon for almost every AI product, lacking brand uniqueness.
2. Beta tag: A safe play that says "we're still testing," but often masks the true state of product maturity.
3. Fake terminal window: Uses code snippets to create an illusion of technical depth, when it's actually unrelated to user value.
4. Unmeasured numbers: Claims like "10x efficiency boost" that lack any data backing.

These elements are so widespread because AI tools, when generating code, prioritize "looking the part" over "solving a specific problem." The designer's perspective reminds us: piling up visual symbols cannot replace design decisions.

Anti-slop: A Free Open-Source Solution

To address this problem, a free repository called Anti-slop has been proposed. Its goal is clear: stop AI coding tools like Claude Code, Cursor, and Codex from outputting the templated content described above. According to the video, the repository provides design constraints that steer AI away from common "slop" patterns when generating pages.

The word "slop" in "Anti-slop" refers to low-quality, mass-produced content. At its core, this repository is a set of rules that helps AI understand which design choices are cheap and which are thoughtful. For developers using AI coding tools, it's essentially an extra layer of design quality control on top of prompts.

What are Claude Code, Cursor, and Codex?

All three are mainstream forms of AI coding assistants today, but with slightly different positioning. Cursor is an AI-first code editor built on VS Code that supports generating and modifying code through in-editor chat; Claude Code is Anthropic's command-line AI coding agent that can read project files, execute commands, and complete multi-step development tasks in the terminal; Codex originates from OpenAI's code generation model series and is often used as the underlying capability for code completion and agentic programming. What they share: all generate frontend code automatically based on natural language instructions, and without extra constraints, common templates from training data tend to be reproduced first. Anti-slop targets this generation pipeline by using rule files to constrain these tools' output tendencies.

How Do You Get AI to Stop Outputting "Plain" Results?

The video mentions a key observation: running the same prompt twice, once with constraints triggered, produced a "plain" result. This shows that AI output depends heavily on context and constraints. Relying solely on prompts cannot reliably produce good design—you need a structured design document.

The specific approach: pair Anti-slop with a design.md file. design.md can be understood as a design specification document that explicitly tells AI which design principles to follow and which patterns to avoid. This "constraints + documentation" combination is more effective than repeatedly tweaking prompts.

The difference between design.md and prompt engineering

design.md is a Markdown specification file kept in a code repository that typically describes design principles, color and typography rules, component usage restrictions, and visual patterns to avoid. Its key difference from one-off prompts: prompts only affect the current conversation and are easily forgotten or diluted by the model, whereas design.md exists long-term as a project file, and AI coding tools continuously reference it when reading repository context—essentially turning design constraints into a project asset. This approach also facilitates team collaboration and version control—design decisions can be reviewed, traced, and iterated, rather than scattered across someone's chat history. Combined with rule sets like Anti-slop, design.md ensures AI faces the same clear boundaries every time it generates.

Implications for AI Coding Workflows

This case reveals a common shortcoming of current AI coding tools: they're good at generating functional code, but easily fall into patterns when it comes to design decisions. For developers, this means actively introducing design constraints rather than expecting AI to automatically produce tasteful interfaces.

From a broader perspective, Anti-slop represents an "anti-templating" movement. It reminds us that the output quality of AI tools depends on the quality of the input constraints. Rather than complaining that AI-generated things are all the same, it's better to give AI clear design boundaries.

For teams using Cursor, Claude Code, or Codex, we recommend putting design specification documents under version control and combining them with rule sets like Anti-slop. This not only improves interface quality but also reduces rework later on.

Conclusion

AI-generated websites look alike because they lack design constraints. Anti-slop and design.md offer a pragmatic solution: guide AI with rules and documentation instead of letting it run free. For product teams seeking differentiation, this may be the key step toward making AI truly serve the brand rather than dilute it.