Don't waste 2 years learning to build LLMs like Claude & ChatGPT.
Stanford just dropped a 2 hour course on how to build LLMs from scratch.
00:00 - break text into tokens
25:44 - decode a user prompt
35:40 - train the model end to end
1:16:47 - build the architecture from scratch
Anthropic pays $750,000/year to engineers who understand this exact knowledge of LLMs.
Watch it, then go further with the step-by-step guide below on how to build a system that improves itself.
Odoo is an open-source enterprise resource planning (ERP) platform that helps teams manage operations.
And choosing how to host it comes down to how much control you want vs the operational overhead.
In this article, @AbdulTa62185243 compares self-hosted and managed Odoo hosting, including the tradeoffs around maintenance, security, customization, and cost.
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study calculus.
not because you need to pass an exam.
because calculus teaches you how the world changes.
• derivatives → how fast something is changing right now
• integrals → how tiny changes accumulate into something larger
• differential equations → how systems evolve through time
• partial derivatives → how one variable changes inside a system with many moving parts
• gradients → which direction changes something fastest
• optimization → finding the best solution under constraints
then connect it to reality.
• velocity is the derivative of position.
• acceleration is the derivative of velocity.
• energy can be accumulated through integration.
• control systems are built around changing states.
• neural networks learn using gradients.
• physics is full of differential equations.
don’t memorize calculus as a collection of formulas.
draw it. simulate it. derive it. write code for it. connect it to motion and physical systems.
calculus becomes beautiful when you stop seeing x and y.
and start seeing change.
Did you know you can use Docker to spin up and test entirely different operating systems in seconds?
Watch the full video to discover 18 weird and wonderful ways I use Docker👇
https://t.co/oAvEa7Q5Dn
If you're preparing for a DevOps interview, bookmark these 7 repos:
1️⃣ DevOps Interview Questions
https://t.co/lcBAh0pE9x
2️⃣ System Design Primer
https://t.co/sGzVwRRco0
3️⃣ Linux Sysadmin Interview Questions
https://t.co/eomBs6XtOI
4️⃣ Kubernetes Interview Questions
https://t.co/llyBFLQJG9
5️⃣ AWS Interview Q&A
https://t.co/4RjaWucYd0
6️⃣ DevOps Exercises
https://t.co/ulbnupaeUM
7️⃣ The Book of Secret Knowledge
https://t.co/u9v3ONDMj6
Save these. You'll probably need them before your next interview.
Así se ve un sueldo de 750.000 dólares al año: un tipo en camiseta blanca, un pizarrón y 2 horas y media.
Stanford, CS336. Percy Liang construye un LLM desde cero. Lo que hay debajo de Claude y ChatGPT, y arranca por la parte que todos saltan: el modelo no lee tu texto, lee números.
Anthropic paga ese sueldo a los ingenieros que entienden esa capa.
Lo único que cobra Stanford son 2 horas y media de tu atención.
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Lol… but B-trees are self-balancing trees used for efficient searching on disk in SQL databases like Postgres, MySQL, etc.
They are directly connected to database indexing. Unless you are building a database engine, I don’t see how this connects to backend engineering unless you want to understand how database indexes work under the hood.
You absolutely do not need to implement B-trees. I also made a video on indexes and B-trees for those interested.
Practice cybersecurity skills using 70 hands-on projects with full source code, organized from beginner fundamentals to advanced security operations.
https://t.co/xu93Q5bOoU
My friend applied to 150 tech jobs in two years. No MIT. No Stanford.
Last month Anthropic offered him $750,000.
I asked him how he broke in from zero.
He sent me the exact video that got him in. A 3-hour course to build a full LLM from scratch.
A developer teaches you exactly how LLMs like ChatGPT and Claude are actually built.
I watched it last night.
Halfway through, I realized I could break into an AI lab in weeks, not years.
Bookmark this and read the article below.
• 00:00 - intro to building LLMs
• 17:58 - LLM tokenization
• 01:06:03 - LLM embedding vectors
• 02:46:55 - LLM transformer architecture
• 03:25:05 - GPT architecture
Big Tech interviews don't test 500 completely different DSA ideas. 📌
The same patterns keep coming back in different disguises.
If you're preparing for software engineering interviews, learn to recognise these patterns instead of memorising solutions.
Here are the ones I'd master first 🧵👇