The Space-Eater 🚀 💀 Dead Space reimagined as a classic 80s horror movie. (Can't wait to play the remake!) #DeadSpaceRemake #horror #scifi #amv #posterdesign

Alternative Poster for Dead Space Videogame (2023) Aka: Dead Space Remake By Estevan Silveira @estevansilveira estevansilveira.com instagram.com/estevan_silvei… #DeadSpace #AlternativePoster #EstevanSilveira

This Sunday, we’ll be hosting a very special Space focused on robotics, featuring several experts in the field. We’ll discuss key topics around Tesla Optimus and the new robotics companies that are growing rapidly. Don’t miss it. x.com/i/spaces/1jGXgBDBlOeKZ

Tesla Optimus will soon be able to perform all of these tasks and much more. As @elonmusk said: “Optimus will be the biggest product ever.”

Me acaban de asignar 16 nodos Nvidia GB200, cada uno con 20 TB en storage NVMI y 740 GB de GPU. El objetivo es montar un LLM con algunos modelos para el team de research y diseño de tecnología.

la marca de agua tokenizadora no funciona con tokens. Tiene que funcionar con los embeddings de cada token. No importa si hay sinónimos, o si la secuencia de los tokens cambia.

MCP lovers after reading this:

Jay Alammar is the best teacher in AI. Period. If you have ever seen "The Illustrated Transformer," you know his diagrams are legendary. He also open-sourced the entire codebase for his O'Reilly book: Hands-On Large Language Models. It’s effectively a visual masterclass in LLMs for free. Chapter 1: Introduction to Language Models Chapter 2: Tokens and Embeddings Chapter 3: Looking Inside Transformer LLMs Chapter 4: Text Classification Chapter 5: Text Clustering and Topic Modeling Chapter 6: Prompt Engineering Chapter 7: Advanced Text Generation Techniques and Tools Chapter 8: Semantic Search and Retrieval-Augmented Generation Chapter 9: Multimodal Large Language Models Chapter 10: Creating Text Embedding Models Chapter 11: Fine-tuning Representation Models for Classification Chapter 12: Fine-tuning Generation Models I will put the repo link in the comments.

Google Brain founder, Andrew Ng: "Prompting will be dead in 6 months, graphs are what's replacing it." In 2 hours at Stanford he shows how to build agents that work and improve entirely on their own. The first 10 minutes cover what most $500 courses never do. Watch the lecture first, then read the guide below on how to build a system that improves itself.

Learn AI for free directly from top companies. 1 - Anthropic: anthropic.skilljar.com 2 - Google: grow.google/ai 3 - Meta: ai.meta.com/resources/ 4 - NVIDIA: developer.nvidia.com/cuda 5 - Microsoft: learn.microsoft.com/en-us/tr… 6 - OpenAI: academy.openai.com 7 - IBM: skillsbuild.org 8 - AWS: skillbuilder.aws 9 - DeepLearning.AI: deeplearning.ai 10 - Hugging Face: huggingface.co/learn Comment "Learning" if you find this helpful. Repost so others can take help. Must bookmark for future reference.

Google just released a free 2-hour course on full Graph Engineering. How to go from one prompt to 100 agents running inside one graph: 17:44 - Build your first AI agent 39:30 - Run agents with loop engineering 1:12:38 - Turn agent loops into graphs 1:34:26 - Build agents that throttle themselves 1:55:05 - Orchestrate the full multi-agent system Most people build one agent and stop there. Google is teaching the full stack: Prompt → Agents → Loops → Graphs → Multi-Agent Systems Single agents are the old workflow. Self-regulating agent graphs are the new one. This 2-hour watch is worth more than most paid agent engineering courses. Bookmark and watch it today Then read the full architecture below ↓

Just saw that the LLMs-from-scratch repository passed 100,000 stars on GitHub! This is super cool and motivating. I am really happy to see that this open-source repo has helped so many people. Thanks also to everyone who shared ideas and opened PRs with improvements! Of course, I plan to keep adding new material, including new attention variants and architectures (while bigger projects like RL and Reasoning From Scratch live in their separate repositories). I am also currently working on a larger applied custom “small” LLM project. It has been keeping me super busy this month, but I will share more on that soon. If you are new to it, some of the highlights include 1. Of course, the complete code path from tokenization and attention to pretraining, classification, and instruction fine-tuning, etc. All of it FROM SCRATCH, of course! (RL lives in a companion repo.) 2. From-scratch implementations of Llama, Qwen, Gemma, and Olmo (smaller variants that run locally and can be plugged into the training scripts). 3. From-scratch implementations of attention alternatives and other architecture components, such as GQA, MLA, sliding-window attention, Gated DeltaNet, DeepSeek Sparse Attention, cross-layer KV sharing, and mixture-of-experts 4. Materials on KV caching, training performance, memory-efficient weight loading, DPO, evaluation, and LoRA So, if you don’t have any weekend plans yet, happy tinkering!

Head of Claude Code, Boris Cherny: "You're not supposed to write code anymore. You're supposed to build a graph that writes itself." In 30 minutes he breaks down how engineers at Anthropic build graphs that write the code, catch the bugs, and ships. The missing piece most developers skip: how to wire those agents into a graph that runs, verifies, and improves without you. Watch it, then read the full guide on graph engineering below.

Esto es una auténtica joya. Hace cinco semanas, Andrej Karpathy se unió a Anthropic. Dos ingenieros senior de Anthropic llevaron el enfoque de Karpathy un paso más allá con el concepto de Graph Engineering. Los sistemas agénticos mejoran de verdad cuando conectas los agentes en forma de grafo. Lo integré en mi configuración y la primera respuesta ya fue completamente distinta. No fue una mejora pequeña. Fue un cambio total. Claude dejó de dar respuestas genéricas y empezó a trabajar exactamente como yo quería. Guárdalo antes de que se pierda en tu feed. Léelo ahora y luego échale un vistazo al artículo de abajo👇

Anthropic engineer: “90% of our engineers were using self‑improving loops. Now everyone shifted to building agentic Graphs" "No more prompting” In 10 minutes she shows her full Claude Code setup and workflow live, from a blank terminal. Worth more than a $500 agentic course. Watch this video, then save the article below on how to become a graph architect👇

This guy bought a $400 Mac mini and walked into a coffee shop with $2,100 for installing an AI agent that never touches the internet. I had to rewatch this because the pitch is almost too simple. He shows up, plugs in the Mac mini, installs a local AI agent running entirely through Ollama, done in under an hour. Zero cloud dependency, no API bills, no monthly charges bleeding the client dry. The coffee shop owner gets a private AI system that keeps working even if the WiFi dies, and he walks out with $2,100 for an install that takes less time than a long lunch. The hardware cost him $400. One deployment and the machine's already paid for five times over. Then he locks in a monthly retainer for support, somewhere around $100-150, which means every client after the first is close to pure margin stacking on top of recurring revenue. Most people hear "AI business" and think they need to build a SaaS platform or learn to code or raise funding from someone. This skips all of that and goes straight to walking into coffee shops with a Mac mini under one arm. Not a company that scales to a billion dollars, obviously, but a solo operator clearing a few thousand a month from local installs while everyone else is still arguing about which LLM is best on Twitter.

Karpathy’s llm.c and did the matrix multiplications by hand. How a Transformer actually works by doing matrix multiplication by hand in pure C. No PyTorch. Just Andrej Karpathy’s llm.c & pen-and-paper matrix multiplies.

Converts a corpus of text into a knowledge graph by extracting concepts and relationships, enabling Graph Retrieval Augmented Generation and local execution via Ollama. github.com/rahulnyk/knowledg…

This is f*cking gold. I've just made the clearest explanation of graph engineering you'll find anywhere. No jargon, no code, no 2-hour lecture, just the four shapes every agent graph is built from. Read on, and you'll never build with AI the way you did before. ------- Shape 0 - Basics A graph is just a plan for your AI work, drawn out so you can see it - which jobs run and which one waits for which. Graph engineering is the skill of drawing that plan well, so the work that doesn't depend on anything runs all at once, instead of one slow step at a time. Get it right and one person can direct a whole fleet of agents. That is why the best engineers jumped on it the moment loops got old. ------- Shape 1 - The Chain This is the one everyone builds by default. Do A, then B, then C, each step waiting politely for the last to finish. It works, and it is the slowest shape there is. If one step stalls, everything behind it stops. Use it only when the steps genuinely need each other's output. ------- Shape 2 - The Diamond This is the workhorse of every serious graph. You split the job, fan the workers out in parallel, verify what they found, then merge it all into one answer. Use it the moment the work breaks into pieces that don't read each other. Same shape behind a research report, a market scan, or a code review. ------- Shape 3 - The Router Sometimes the next step depends on what you just found. One node checks a result and picks the path. a small job gets a quick pass, a big one triggers a full audit. Use it when the work needs to branch. The decision runs the same way every time, because it lives in the structure, not in a guess. ------- Shape 4 - The Cycle Some jobs you can't size up front. A bug sweep where finding one problem reveals three more. So you add a controlled edge back: keep finding, checking, looping until two rounds turn up nothing new, then stop. Use it for discovery of unknown size. The one rule is a hard limit, or it runs until your budget is gone. ------- That's the base, now you know the four shapes and when each one fits. If you want to go deeper, I wrote a full article just for that, it explains everything step by step and breaks down where graph engineering wins and where it breaks. It also walks you through building your own graph from scratch, read it below.