Analysis updated 2026-05-18
Preview what a dashboard for tracking AI search visibility could look like
Study an example of an entirely AI generated frontend codebase
Explore a checklist of GEO related workflows like competitor analysis and content generation
| zshmbbs/geoforge-ui | 0xradioac7iv/tempfs | 52191314/web-agent-proxy-sdk | |
|---|---|---|---|
| Stars | 0 | 0 | 0 |
| Language | TypeScript | TypeScript | TypeScript |
| Setup difficulty | easy | moderate | moderate |
| Complexity | 2/5 | 3/5 | 4/5 |
| Audience | pm founder | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Demo login only, no real backend or AI integration is wired up.
GeoForge UI is a frontend console prototype for something the project calls GEO, short for Generative Engine Optimization. The idea is that as AI powered search tools like SearchGPT, Perplexity, and DeepSeek Search change how people find information online, the old rules of search engine optimization no longer apply, and businesses need a new way to make sure AI answers mention them accurately and favorably. This repository is only the visual front end for that idea, not a working backend service. The author explains plainly that they are not a programmer and that every line of code in this project was generated by AI tools, based on notes they gathered about GEO and fed into an AI model for planning. The business idea behind GeoForge never moved forward, so the author is sharing the front end as a visual reference for anyone curious about what a GEO optimization dashboard might look like. The README is direct that this code should not be treated as an example of good software practice and should not be used in any real project. The interface itself is built with Next.js, React, and TypeScript, styled with Tailwind CSS, and uses Recharts for charts and Zustand for state. It imagines a dashboard with a login screen, a project overview with key metrics, and, for each project, around seventeen sub pages covering things like intent mapping, competitor analysis, a knowledge base for AI models, content generation, technical optimization settings, and monitoring for negative mentions. There is also a settings area for configuring API keys for AI providers such as DeepSeek, Kimi, and GLM. Getting it running follows a normal Next.js workflow: clone the repository, run npm install, then npm run dev, and open the local address in a browser, which redirects to a demo login page with a sample email and password provided in the README. The project is released under the MIT license.
A frontend only prototype dashboard imagining a control panel for optimizing how brands appear in AI search answers, built with AI generated code.
Mainly TypeScript. The stack also includes Next.js, React, TypeScript.
Setup difficulty is rated easy, with roughly 30min to a first successful run.
Mainly pm founder.
This repo across BitVibe Labs
Verify against the repo before relying on details.