4N50N

[Argona @Argona0x]

a new kind of engineer is showing up. they don't write prompts, they design how the AI works. it's called graph engineering, and the people who get it now are about to make everyone else look slow https://t.co/z1HhE6voRu

[Gipp 🦅 @gippp69]

https://t.co/ITKQ6Eq8sI

[beamnxw ./ @beamnxw]

https://t.co/Qd15VI1bFb

[Ricker @0xRicker]

Andrej Karpathy just dropped a full 116 min course on: "How to Build LLM From Scratch"

He built LLM from scratch in 200 lines of code in 2 hours.

90% of AI engineers can't tell you what happens inside the model they use every day.

In 2 hours, he shows you the entire stack:

transformer blocks + attention heads + positional encoding + residual connections.

Worth more than any $5000 AI course you've been eyeing.

[Adrian Punk @AdrianPunk115]

https://t.co/eC1mkWh1GT

[Lunar @LunarResearcher]

4 Anthropic engineers. 75 minutes.

Their best lessons on how to actually prompt Claude:

14:12 - The prompts Anthropic engineers actually use
32:33 - The simple fix that makes prompts work better
59:44 - How to give Claude the context it needs
1:07:59 - The prompt that keeps working while you sleep

Most prompting courses teach theory.

This shows how the people building Claude actually use it.

This 75-minute watch is worth more than most paid prompting courses.

Bookmark and watch it tonight

Then read the step-by-step guide below

[Codez @0xCodez]

Anthropic engineer just dropped a 2-hour workshop on “Graph Engineering” for agentic systems:

“80% of our engineers are using self-improving loops. Now everyone is building agentic graphs.”

• 04:43 - Introduction to RAG and graphs
• 26:30 - Core of graph engineering (nodes, edges)
• 01:09:11 - Agents data indexing in Graphs
• 01:30:50 - Three layers of Graphs for agents
• 01:48:15 - Adaptive RAG for self-verification
• 02:16:37 - The future of graph engineering

This 2-hour workshop will replace your $500 agentic memory course.

Watch it today, then read how to become a graph engineer in the article below.

[Hamza Khalid @humzaakhalid]

Anthropic will pay you $750,000 a year to know this one thing.

Stanford teaches the entire thing in 1 hour and 44 minutes.

Not prompting. How a model gets built, trained, and aligned.

Bookmark it now, because it's 100% free and always will be.

Then read the full guide on building AI agents below:

https://t.co/ZetkE7IQqT

[seeco @seeconvm]

https://t.co/mm6Jy6Ofzu

[Raytar @Raytar]

https://t.co/eAsr7cajuP

[Jey @0xJeyx]

https://t.co/BmDzlVwhaa

[Yarchi @undefinedKi]

https://t.co/8nVlrEbsnN

[Akshay 🚀 @akshay_pachaar]

https://t.co/YUGDPxpYvy

[Akshay 🚀 @akshay_pachaar]

https://t.co/7KT3Yudw66

[Ridark @ridark_eth]

https://t.co/dM7dRCaw7X

[seeco @seeconvm]

https://t.co/p1WdjxIhMj

[Mark Poiler @mpoilerfx]

MIT put its full multivariable calculus course online in 2007 and charged nobody a dollar for it.

Lecture 34 is the final review. A professor in an orange sweater writes Unit 1 on a chalkboard and starts working through the whole semester.

The camera never moves much. No editing, no music, no production budget visible anywhere.

MIT OpenCourseWare launched in 2001 with 50 courses. The plan was to publish materials from every course the university taught.

Faculty were told they would get no royalties and no extra pay. Most agreed anyway.

The catalog passed 2,400 courses. Downloads run past 300,000,000 and the site draws millions of visitors a year from countries with no equivalent institution.

Universities charging 60,000 dollars a year watched a peer give the lectures away and kept charging.

The thing people pay for was never the lecture.

[darkzodchi @zodchiii]

https://t.co/VpA4KNrMw8

[Aleiah @AleiahLock]

https://t.co/DhszHZLFGk

[Kanika @KanikaBK]

WAIT... WHAT!!

Anthropic burned $3 million and 30 months running AI agents against real tasks in real companies and published everything in 12 pages and the people who read those 12 pages this week are going to make decisions about how they build and work that are fundamentally different from the people who scroll past it thinking they already understand what AI can do.

1,287 real tasks. 2.4 million lines of code. AI outperforming humans on their own projects. 63% of routine work automated. Engineer output multiplied three times with the right memory and the right cycles.

Someone paid $3 million so you could read that for free and most people are going to bookmark it and never open it again which is the most expensive free resource in the history of AI research.

But here is the gap that the 12 pages do not close.

Knowing that the right system multiplies output three times and knowing how to build that system are two completely different things and the second one is where everyone gets stuck and stays stuck while the people who figured it out quietly operate at a different level.

My article is the bridge between the $3 million research and the actual system build and it is the thing you read immediately after you bookmark those 12 pages.

Do not let this be another thing you meant to act on.

Full guide below.