Creating memories is more important than the car will ever be. That’s any car.

https://www.tiktok.com/t/ZP8poykC3/

Most AI content falls into two buckets. Either it's super abstract ("AI will transform product management!") with zero practical steps, or it's deep engineering tutorials that assume you already know how to code.

Skill Agents AI sits right in the middle. Run by a PM with 15 years at Flipkart, Walmart, and Zynga who built his entire SaaS (Codepup AI) solo using Claude Code with no dev background. Every video maps to something a PM can actually do: ship a prototype, make a build-vs-buy decision on RAG, brief your engineering team without sounding lost.

What's on it:

• Learn RAG from Scratch: covers chunking, re-ranking, hybrid search, routing, evals, agentic RAG. Not "here's what RAG is" more like "here's why your retriever returns the billing FAQ when the user asks about refunds and how to fix it."
• Claude Code for PMs setup to deployment. First website in 30 min, connecting Supabase, deploying to Vercel, MCP servers, the commands you'll use daily.
• AI Agents & Agentic Workflows real use cases like automating a sales pipeline and screening thousands of resumes.
• Claude Hacks building PRDs 10x faster, frontend design using skills, cost management with /compact and /context.

The metaphors are what make it stick. https://www.youtube.com/@SkillAgentsAI?utm_source=reddit

I added video metadata support to my desktop app, YouTube History Transcriber.

The app already lets me browse YouTube history, detect subtitles, cache transcripts, search transcript text, and fall back to Whisper when subtitles are missing. Now each cached transcript can also pull and display useful metadata for the selected video.

Current metadata support includes:

• Video title
• Channel name
• Upload date
• Duration
• View count
• Like count
• Categories
• YouTube URL
• Full description
• Source links / references from the description
• Language badge for cached transcript files

Why this matters: transcripts alone are useful, but metadata gives the transcript context. When I search through old videos, I do not just want the text — I want to know who published it, when it was uploaded, how popular it was, what category it belongs to, and what links/sources were attached to it.

The workflow is now:

YouTube History → Subtitles / Transcript Cache → Metadata Fetch → Searchable Knowledge Base

This makes the app feel less like a simple transcript viewer and more like a personal research archive for YouTube history.

Next step: connecting metadata + transcript content into the chat workflow so I can ask questions like:

“Find videos from this channel about GitHub”
“Show me political videos from last month with cached transcripts”
“Summarize this video and include its original source links”
“Search only transcript text, but rank results using video metadata”

They slip in quietly…
small thoughts… small doubts…
easy to ignore at first.

Until they aren’t.

Until they start stacking.
Repeating.
Shaping the way you see yourself without you even realizing it.

But I don't play with broken toys. So let's fix it...together.