The Model Doesn't Matter. The Harness Does. (Cursor + Anthropic)
Unlock all features
FREE: Get instant access to 10 AI summaries, chats, or transcripts per day.
Unlock all features
FREE: Get instant access to 10 AI summaries, chats, or transcripts per day.
Unlock all features
FREE: Get instant access to 10 AI summaries, chats, or transcripts per day.
Unlock all features
FREE: Get instant access to 10 AI summaries, chats, or transcripts per day.
Unlock all features
FREE: Get instant access to 10 AI summaries, chats, or transcripts per day.
Related videos
Engineering The Perfect First Date
Mark Rober
209.5k views
Anthropic confirms software engineering is NOT dead
ThePrimeTime
341.6k views
The best model Anthropic has ever made
Theo - t3․gg
59.0k views
Sonnet 4.5 Is Here—And It’s a Beast at Coding
Prompt Engineering
52.0k views
Strange Dark Matter Discoveries That Can't Be Explained With Current Models
Anton Petrov
97.8k views
NVIDIA Canceling H20 GPU Production -- China Doesn't Need American AI
Eli the Computer Guy
40.0k views
GPT-OSS Jailbreak with this Simple Trick
Prompt Engineering
54.4k views
Context Engineering is All You NEED!
Prompt Engineering
38.7k views
The Only Embedding Model You Need for RAG
Prompt Engineering
35.2k views
AI prompt engineering in 2025: What works and what doesn’t | Sander Schulhoff
Lenny's Podcast
68.3k views
Top Comments (10)
It will be great if you create a playlist of “system design for AI” and discuss about system design of all these AI related stuff which includes Harness as most important part. But there’s a lot in this topic.
The multi-agent error comoounding was profound for me.
Hermes agent for me is the best tool around any models for my coding and other tasks.
Get started with SerpApi using 250 free credits: https://serpapi.com/?utm_source=youtube&utm_campaign=promptengineering_may_2026
Thanks for the amazing and useful content 🌹
If the harness matter so much, are we in a hard takeoff scenario? I just read an article about agents communicating through latent space embeddings, speeding up agents by 2..4x and reducing significantly the context memory (i.e. each LLM will operate at its peak performance because its not going to read summaries and reason through them - it practically has telepathic connection to the other agents).
@13:34 The compounding error math looks like a fallacy to me. The synergy works in the other direction. The slides are basically claiming that if a team gets rid of the planner, debugger, reviewer and the tester, then the quality of the sole dev's code is back to 95%. It don't work like that.
Can you share your workflow for creating this video? I really like the slides and take on things.
I do mid chat switching but sometimes, I have the first chat run a review on the sunsequent chat for accuracy.
I’m building 4 agents in OpenClaw, Plan and Builder with Minimax m2.7 (local, bf16, 204k kv-cache) and Validator and Researcher with ChatGPT. I use Claude to review the harness plan and Md file creation. The overall result is amazing. I agree, the moat is in the harness of each model.
Unlock the Data Inside
Turn Videos into Knowledge
- Get FREE 10/day: transcripts, summaries, chats
- Chat with videos, export text & PDF
- $1 free API credit for RAG, chatbots & research
Free forever plan • All features unlocked
Top Comments (10)
It will be great if you create a playlist of “system design for AI” and discuss about system design of all these AI related stuff which includes Harness as most important part. But there’s a lot in this topic.
The multi-agent error comoounding was profound for me.
Hermes agent for me is the best tool around any models for my coding and other tasks.
Get started with SerpApi using 250 free credits: https://serpapi.com/?utm_source=youtube&utm_campaign=promptengineering_may_2026
Thanks for the amazing and useful content 🌹
If the harness matter so much, are we in a hard takeoff scenario? I just read an article about agents communicating through latent space embeddings, speeding up agents by 2..4x and reducing significantly the context memory (i.e. each LLM will operate at its peak performance because its not going to read summaries and reason through them - it practically has telepathic connection to the other agents).
@13:34 The compounding error math looks like a fallacy to me. The synergy works in the other direction. The slides are basically claiming that if a team gets rid of the planner, debugger, reviewer and the tester, then the quality of the sole dev's code is back to 95%. It don't work like that.
Can you share your workflow for creating this video? I really like the slides and take on things.
I do mid chat switching but sometimes, I have the first chat run a review on the sunsequent chat for accuracy.
I’m building 4 agents in OpenClaw, Plan and Builder with Minimax m2.7 (local, bf16, 204k kv-cache) and Validator and Researcher with ChatGPT. I use Claude to review the harness plan and Md file creation. The overall result is amazing. I agree, the moat is in the harness of each model.