GitTalks: Turn Any GitHub Repo Into a Podcast.

Published: 3 days ago (February 12, 2026 at 09:19 AM EST)

6 min read

Source: Dev.to

This is a submission for the GitHub Copilot CLI Challenge

What I Built

GitTalks turns GitHub repositories into multi‑episode podcast playlists. It uses AI to analyze any repository and generate a series of conversational audio episodes that explain the codebase’s architecture and implementation. Each repository becomes a structured podcast with multiple episodes covering different aspects of the code.

Live Demo: | GitHub:

Listen to Sample Podcasts

Screenshots

The Vision

Developers are drowning in documentation. Complex codebases demand hours of reading, scrolling through files, and piecing together mental models. What if you could learn from code while commuting, exercising, or doing literally anything else?

GitTalks lets you paste a repository URL and get back a podcast—not a robot reading README files, but an actual conversation between two developers walking through the codebase as if they were explaining it to a colleague.

How It Works

Repository Analysis

The system uses Google’s Gemini 2.0 Flash model (via Vercel AI SDK) to analyze repository structure. It parses file trees, identifies dependencies, and maps out component interactions. The AI decides what’s worth explaining:

Overall architecture
Core components and how they connect
Notable design patterns
Framework integrations
Implementation techniques worth calling out

Multi‑Episode Playlists

GitTalks doesn’t generate one long audio file. It creates a playlist of episodes, each focused on a specific part of the codebase. Small repos might get 2‑3 episodes; large frameworks could get 8+. Each episode covers a distinct topic, so you can skip around or focus on what matters to you. Think podcast series, not audiobook.

Text‑to‑Speech

Audio generation uses DeepInfra’s Kokoro TTS (OpenAI‑compatible API). The voices sound natural, have emotional range, and synthesize quickly. I built custom MP3 chunking with proper Xing headers so long episodes don’t break in players.

Duo Conversation Mode

The audio isn’t a monotone narration. It’s a conversation between two people: an expert host explains the architecture while a curious co‑host asks clarifying questions. This “deep‑dive” style, inspired by NotebookLM, makes complex technical decisions feel like normal developer conversations rather than dry documentation.

Pipeline

Fetch repository data from GitHub (smart file prioritization)
LLM analyzes structure and generates an episode plan
LLM writes dialogue scripts for each episode
TTS synthesizes audio (multiple episodes in parallel, rate‑limited)
Upload to S3 and cache the results

Caching

If a podcast for a repo already exists, you get instant access—no re‑processing. GitHub star counts are cached client‑side for 24 hours, and edge caching handles static assets.

What Makes This Interesting

The technical challenge wasn’t merely calling AI APIs; it was orchestrating them reliably. LLMs and TTS have rate limits, transient failures, and unpredictable latencies. The pipeline handles all of this with retry logic, exponential backoff, and parallel processing where safe.

The UX challenge was making “wait 2‑3 minutes while we generate your podcast” feel acceptable. Real‑time job status updates and caching help.

The product challenge was the episode format. One 45‑minute audio file for a large repo is unusable; breaking it into topical episodes makes the content navigable.

All of this runs on free tiers plus roughly $10 of API credits. The GitHub API is generous, Gemini has a free tier, and only Kokoro TTS incurs cost—still cheap at scale.

Demo

Try It

Go to the homepage. You’ll see popular repos (Next.js, React, VS Code, Tailwind) with live star counts.
Paste any GitHub URL or click a featured repo.
Wait 2‑3 minutes while the app:
- Fetches the repository
- Analyzes the structure
- Generates episode scripts
- Synthesizes audio
Hit Play. A web audio player with standard controls appears.
Episodes are saved; shareable URLs work.

Testing Instructions for Judges

Authentication
To generate new podcasts, sign in with your GitHub account (no special permissions required). The app only needs basic profile access.

Quick testing without signing in
You can listen to pre‑generated podcasts immediately:

Full generation flow

Sign in with GitHub.
Enter any public repository URL.
Wait 4‑10 minutes for generation.

No special credentials are needed—any GitHub account works!

Features

Browse and search public GitHub repositories
Automatic multi‑episode playlist generation
Two‑host conversational narration
Audio player with progress tracking
Recent playlists feed
Authentication for saved playlists
Mobile‑friendly

The demo video shows the UI, job tracking, episode player, and generated audio quality.

My Experience with GitHub Copilot CLI

This was my first time using Copilot CLI. I wasn’t sure what to expect, but it ended up doing most of the heavy lifting.

Setup

I installed Copilot CLI and started a session by typing:

copilot

Simple.

Plan Mode

I’d read about Plan Mode in the docs. It’s a feature where you describe what you want to build and Copilot generates a full implementation plan. I opened it (Shift + Tab, or /plan) and described the idea:

GitTalks: a podcast app where users enter a GitHub repository URL and the system generates an audio playlist of episodes that narrate everything about the repo. Use Next.js for both backend and frontend.

A couple of minutes later, I had a detailed plan: architecture decisions, file structure, API routes, database schema, deployment approach—everything laid out step‑by‑step. I approved it.

Here’s the full plan: GitTalks Implementation Plan

Agent Skills

Then I discovered Agent Skills, specialized plugins that extend what Copilot can do. I installed a few, including the frontend‑design skill from Anthropic.

Prompt:

“use frontend skill to generate the frontend of the app.”

It generated about 80 % of the production frontend you see live—component structure, responsive layouts, gradient UI, interactive elements—in one shot.

Implementation

I worked through the plan, building:

Backend API routes (GitHub integration, job management)
Authentication (Better Auth)
Rate limiting
AI content generation (LLM orchestration)
TTS integration

The only manual work was configuring external API keys (DeepInfra, Google Gemini). Copilot can’t log into third‑party dashboards, obviously.

Polish

Once the core was done, I used Copilot chat for refinements: better error handling, loading states, UI polish. It helped with all of it.

What I Learned

Plan Mode is legitimately useful for project architecture. I didn’t have to guess at folder structure or debate database choices; it proposed something reasonable that I tweaked where needed.
Agent Skills are amazing. The frontend generation was pretty good—no back‑and‑forth, no iterations, just working code.
Staying in the terminal helped. No context‑switching to web UIs or separate tools; I stayed in my editor.

Would I use it again? Yes. For complex projects, the planning phase alone saves hours.

Thanks for reading through this. Hope you enjoyed exploring GitTalks!