HTML / CSS / JS
Best for direct control and small custom artifacts, but every interaction, responsive rule, and safety check has to be rebuilt by hand.
MinoraBetaCompare
React, Vue, and Next.js are powerful engineering tools. Minora is built for a different moment: when the first author is a model, the reviewer is a human, and the product still needs stable structure, links, versions, and governance.
Best for direct control and small custom artifacts, but every interaction, responsive rule, and safety check has to be rebuilt by hand.
Excellent for engineering teams building complex products, but AI edits still have to reason across component files, state, styling, routes, and build rules.
A production-grade web framework with routing and deployment patterns, but it still assumes an engineering workflow around code ownership and runtime infrastructure.
A governed creation layer for AI-made sites. People can preview, point at what should change, revise safely, and keep the work maintainable.
A better fit for AI work
Instead of asking a model to rewrite a whole codebase, Minora treats a site as a set of inspectable parts. The model can change a section, a link, a language string, or a visible state while the rest of the site stays stable.
You describe what should change in a specific section, button, link, or piece of copy.
The model works inside that visible boundary instead of rethinking the entire site.
You inspect the result first, then publish only the version that is ready.
AI is good at generating. The hard part is changing something later without breaking nearby work. Clear boundaries make follow-up edits easier to review.
Code generation often creates a large result first and asks people to inspect it afterward. Minora keeps the editing loop visible: preview, point, revise, approve.
Why it can feel faster
The speed comes from less rework. Pages, shared parts, links, language content, and visible behavior have clearer boundaries, so each revision can focus on what actually changed.
When AI gets it wrong
The goal is not to pretend AI edits are perfect. The goal is to make every change easier to inspect: what changed, where it changed, and whether it belongs in that part of the site.
A small request can touch selectors, components, state, routes, and build assumptions. When something breaks, the model often has to rediscover the surrounding system before it can repair the issue.
The change is tied to visible parts: a page section, shared shell, language string, link, or interaction state. If the model works in the wrong place, that becomes easier to catch and correct.
You review a preview and a versioned result instead of searching through an entire generated codebase. The feedback loop becomes point, revise, verify, then publish.
This is why Minora can feel faster after the first draft: the repair path is shorter, not just the generation step.
Install before signup
Add the MCP server first. When the agent needs access, Minora opens a browser flow so the right user can sign in or create a workspace.
Create a workspaceFor Claude Code remote MCP.
claude mcp add minora --transport sse https://app.minora.dev/mcp
For Codex CLI and the Codex desktop app configuration.
mkdir -p ~/.codex && touch ~/.codex/config.toml && { grep -q '^\[mcp_servers\.minora\]' ~/.codex/config.toml || printf '\n[mcp_servers.minora]\nurl = "https://app.minora.dev/mcp"\n' >> ~/.codex/config.toml; } && codex mcp login minoraThe goal is not to replace engineering judgment. It is to give AI a production surface that keeps more of the work visible, testable, and editable.