Codex Slides: Turning Repos into Decks from Scratch

Codex Slides, an open-source tool from nexu-io, converts GitHub repositories into presentation slides by scanning file structures and identifying key logic flows, cutting preparation time from hours to minutes. The tool requires users to provide their own LLM API keys and supports customization via prompt engineering, with recommended models including Claude 3.5 Sonnet and GPT-4o for optimal code synthesis.

Codex Slides: Turning Repos into Decks from Scratch The core value here isn't just "AI-generated slides"—it's the ability to feed it a technical repository and have it synthesize the architecture and logic into a visual format. If you're trying to onboard a new teammate to a codebase or present a project's progress, this cuts the preparation time from hours to minutes. Getting it Running Since this is an open-source tool, you can deploy it locally. The setup is straightforward, but you'll need your own LLM API keys to power the generation engine. 1. Clone the repository and install dependencies: git clone https://github.com/nexu-io/codex-slides.git cd codex-slides npm install 2. Configure your environment variables. Create a .env file in the root directory to link your AI provider: OPENAI API KEY=your api key here If using a different provider via OpenAI-compatible API OPENAI API BASE=https://api.your-provider.com/v1 3. Fire up the development server: npm run dev Practical Workflow: Repo to Presentation The real power comes when you use the "Repo to Deck" feature. Instead of writing a manual prompt, you provide a link to a public repository. The agent scans the file structure, reads the README, and identifies key logic flows to build the slides. To optimize the output, I've found that prompt engineering the "Style" or "Context" field helps avoid generic AI fluff. For example, instead of saying "Make a presentation about this repo," use a specific technical instruction: Analyze the /src/core directory and create a 5-slide technical deep dive. Focus on the data pipeline logic and the specific implementation of the caching layer. Use a professional engineering tone and include pseudo-code examples from the source. Technical Breakdown and Performance From a developer's perspective, the tool's architecture is interesting because it doesn't just output a static PDF. It generates a structured representation of the slides that can be edited. Input Processing: It handles both raw text prompts and repository crawling. Rendering Engine: Uses a web-based renderer that ensures the layout remains consistent regardless of the amount of text generated. Customization: You can tweak the theme and layout without needing to touch a GUI, fitting perfectly into an AI workflow for those of us who prefer Markdown over drag-and-drop. Is it worth the setup? If you are a student or a developer who frequently has to present technical architecture, yes. The "from scratch" setup takes about 5 minutes, and the time saved on slide formatting is immediate. The only real friction point is the reliance on external LLMs. If you're using a smaller model, the summaries can sometimes be too brief. I recommend using Claude /en/tags/claude/ 3.5 Sonnet or GPT-4o for the best synthesis of complex codebases. For those looking for a complete guide on how to integrate this into a CI/CD pipeline e.g., auto-generating documentation slides on every major release , there is significant potential here for further automation. For those interested in the source code, you can find the project here: https://github.com/nexu-io/codex-slides Next Critical Thinking with LLMs: A Practical Workflow → /en/threads/2882/