Step-by-step project tutorials for building summarizers, RAG pipelines, monitoring agents, content repurposing tools, and LangChain integrations.
LangChain's built-in YouTubeLoader wraps a scraper that breaks in production. Replace it with a custom TranscriptAPI-backed loader in about 50 lines, then build a knowledge base that ingests entire channels and answers questions with source citations and timestamps.
By Nikhil Kumar
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Build an agent that watches any YouTube channel for new uploads, fetches the transcript, and auto-summarizes via OpenAI or Claude. Full code, ~80 lines.
By Nikhil Kumar
Build a production-quality YouTube video summarizer API in about 30 minutes with FastAPI, TranscriptAPI for extraction, and GPT-4 for the summary — under 100 lines of Python that returns a title, key points, timestamps, and a summary for roughly two cents a video.
By TranscriptAPI Team
Every YouTube video is a blog post waiting to happen. This Python pipeline extracts the transcript with TranscriptAPI, cleans up the spoken language with an LLM, and rewrites it into a structured, SEO-ready article — turning one recording into several content formats automatically.
By TranscriptAPI Team
Replace hours of manual channel-watching with a ~40-line Python agent: it detects new uploads, pulls each transcript through TranscriptAPI, runs it through an LLM for structured analysis, and posts a clean report to Slack — turning a weekly chore into a scheduled job.
By Nikhil Kumar
YouTube transcripts are an underused source for RAG: they arrive pre-segmented with timestamps, cover knowledge that never gets written down, and span 800M+ videos. Here's how to chunk them by timestamp, embed to Chroma or Pinecone, and answer questions grounded in real video content.
By Nikhil Kumar
