Finds the thesis, evidence, assumptions, and limitations.
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Research papers,
made runnable.
We turn the most important AI papers into clear tutorial videos, production-grade code, diagrams, and live demos.
15 minto understand
1 repoto start building
0 fluffjust useful details
paper2tutorial / live-run
⌘K
Loading today’s highest-signal paper...
01Read & summarize paperdone
02Map core architecturedone
03Generate Python demorunning
04Render tutorial videoqueued
05Deploy live applicationqueued
6 agents collaborating · elapsed 04:18
AGENTIC AI✦GRAPH RAG✦ROBOTICS✦DIFFUSION✦LOCAL LLM✦MCP✦
AGENTIC AI✦GRAPH RAG✦ROBOTICS✦DIFFUSION✦LOCAL LLM✦MCP✦
Daily tutorial video
Watch the paper.
Watch the paper.
Then build it.
A presenter-led walkthrough generated from today’s lead paper, with narration, diagrams, captions, and direct links to the source.
Today / Latest papers
Fresh research.
Fresh research.
Dated every day.
Scanning the newest arXiv submissions and turning the strongest papers into build-ready tutorial briefs.
Loading today’s paper edition...
01 / The system
One paper in.
One paper in.
Every useful format out.
A coordinated AI production line turns dense research into things developers can watch, read, fork, and deploy.
New paperPDF + metadata
MULTI-AGENTReasoning
Engine6 agents active
Engine6 agents active
VideoYouTube + TikTok
CodeGitHub repo
DemoDeployed app
Article5-min read
Maps systems and concepts into visual explanations.
Builds a clean, tested reference implementation.
Turns complexity into a compelling learning sequence.
Combines narration, slides, code, and animation.
Packages the lesson for discovery and attention.
02 / Example output
Don’t just explain it.
Don’t just explain it.
Make it work.
LATEST TUTORIAL · 15:24
GraphRAG
explained.
explained.
Paper → Tutorial → Demo
GraphRAG Explained in 15 Minutes (with Python Code)
Why vector RAG misses the big picture, how knowledge graphs change retrieval, and how to build the architecture yourself.
- 01 The problem with vector-only RAG
- 02 Knowledge graph construction
- 03 Local vs. global retrieval
- 04 Python implementation
- 05 LangGraph integration
03 / Learning libraryCurrent frontier.
Current frontier.
Clear explanations.
Follow a topic and get the papers worth knowing, translated into practical engineering lessons.
⌘
Agentic AI
Reasoning, memory, planning, and multi-agent systems.
12 tutorials ↗⌬
GraphRAG
Graph retrieval, community summaries, and global search.
8 tutorials ↗⚙
Robotics AI
Vision-language-action models and embodied agents.
9 tutorials ↗◌
Local LLMs
Inference, quantization, fine-tuning, and deployment.
15 tutorials ↗✳
Diffusion
Image, video, audio, and world generation models.
11 tutorials ↗↔
MCP + A2A
Protocols for connected tools and collaborating agents.
7 tutorials ↗
04 / Built to compound
A media business with an engineering moat.
Every tutorial creates a durable set of assets: a discoverable video, an evergreen article, a reusable codebase, and a working product demo.
“Many developers would rather watch this than read a 30-page paper.”
Revenue mixLONG-TERM POTENTIAL
Paid courses
Excellent
Consulting leads
Excellent
AI SaaS referrals
Very good
YouTube ads
Good
GitHub sponsors
Good
Hardware affiliates
Moderate
Read less.
Build sooner.
One deeply practical AI tutorial in your inbox each week.
No noise. Just research worth building.