I'm not sure folks have realized just how crazy the second half of 2026 and 2027 will be for global temperatures – on the back of a record-smashing El Niño event. Here is my latest estimate of where both years will end up compared to global temperatures since 1850.

Jizai Arms is a wearable robotic backpack from the University of Tokyo that lets users control up to six extra robotic arms through body movements. The system could improve work efficiency and accessibility across many fields. It was first introduced in 2022.

Microsoft has released an open-source tool that helps teams learn ontology design before choosing a knowledge graph platform. It is called Ontology Playground. The project is a fully static React app, which means it does not need a backend, account system, database, or hosted service to run. The goal is simple: Help people understand what goes inside a graph before they buy or build the graph database. Ontology Playground includes six pre-built domain ontologies: → Retail → Healthcare → Finance → Manufacturing → E-Commerce → Education Each one gives users a starting point for understanding entities, relationships, properties, and how domain knowledge gets structured. The app also includes a live visual designer, structured learning paths, hands-on labs, and RDF/XML export for Fabric IQ. Because it is static, it can be deployed almost anywhere. No backend. No vendor lock-in. No platform commitment upfront. This matters because many teams jump into knowledge graphs too early. They focus on the database first. But the harder question is usually: What should the graph actually know? Ontology Playground teaches that layer first.

Ahora Claude Code puede leer documentaciones completas sin gastar un solo token. Solo necesitas conectarlo a NotebookLM de Google vía MCP. Aquí el tutorial de cómo hacerlo. ⬇️

unfollowing everyone on linkedin except this guy

A microscopic view of neurons forming new connections

It's over. Karpathy just open-sourced an autonomous AI researcher that runs 100 experiments while you sleep. You don't write the training code anymore. You write a prompt that tells an AI agent how to think about research. The agent edits the code, trains a small language model for exactly five minutes, checks the score, keeps or discards the result, and loops. All night. No human in the loop. That fixed five-minute clock is the quiet genius. No matter what the agent changes, the network size, the learning rate, the entire architecture, every run gets compared on equal footing. This turns open-ended research into a game with a clear score: - 12 experiments per hour, ~100 overnight - Validation loss measures how well the model predicts unseen text - Lower score wins, everything else is fair game The agent touches one Python file containing the full training recipe. You never open it. Instead, you program a markdown file that shapes the agent's research strategy. Your job becomes programming the programmer, and this unlocks a strange new loop: 1. Agents run real experiments without supervision 2. Prompt quality becomes the bottleneck, not researcher hours 3. Results auto-optimize for your specific hardware 4. Anyone with one GPU can run a research lab overnight The best AI labs won't just have the most compute. They'll have the best instructions for agents who never sleep, never forget a failed experiment, and never stop iterating.

Neural networks and machine learning, visualized

this is what a company looks like in 2026. not people. not offices. not salaries. a folder. .claude/agents/ engineering/ marketing/ design/ ops/ testing/ every role. every department. every function. all .md files. i have 12 of these running in OpenClaw right now. the org chart is dead. the directory is the new company.

So Google has just released its own agent builder?! You can now add the agent block in Google Opal and "program it" in plain English. And it has natively: - Tool call (with Nano Banana, Veo, web search...) - Memory to save infos between sessions - Conditional logic Probably the easiest way to build AI agents I've seen so far.

Dog ownership was linked to ~40% lower odds of disabling dementia. There was no protective signal for cat owners.

In China, an engineer came up with anti-mosquito air defense 🦟

Google launched a brand new AI tool. It's called CodeWiki, and it might be the biggest upgrade GitHub has had in years. And all you do is paste your GitHub repo in, and it turns your entire project into an interactive guide. It also generates diagrams, explanations, walkthroughs, everything you could ever want, and even a chatbot that knows the code better than anyone else. So you never have to dig through a giant repo again wondering what does this do

NO WAYYY Claude in PowerPoint is absolutely INSANE ! It’s so over…

Google isn’t trying to win the AI race. They’re trying to own the entire AI Agent ecosystem. While everyone argues ChatGPT vs Claude, Google quietly built: Models → Gemini Pro, Flash, Deep Think, Gemma Design → Stitch, Whisk, Imagen Research → NotebookLM, AI Mode Video → Veo, Flow, Google Vids Coding → Antigravity IDE, Gemini CLI, Jules Agents → A2A, ADK, FileSearch API The scary part? All of these tools talk to each other. That means: 10x faster prototypes End-to-end AI workflows Production-ready agents on GCP The next AI war won’t be model vs model. It’ll be ecosystem vs ecosystem. I mapped this stack out here: gamma.app/?utm_campaign=prom… Save. Share. Build.

Goldman Sachs is rolling out Claude to automate accounting. So it starts. The market already reacts.

PSA for a CTO, Head of AI, VP/Dir of Engineering, CXO: This is going to be one of the most important "back to work" weeks of your career. You must get your team aligned on agentic dev ASAP. If you're feeling behind or overwhelmed, here are some good reads to get you inspired 🧵

Robots with tentacles! 🐙 SpiRobs are soft robots inspired by nature, designed in the shape of a logarithmic spiral. They mimic the flexible movements of octopus arms and elephant trunks to grasp objects. 🐘 SpiRobs use simple cables to move, making them easy to control and operate. They can scale from small to large sizes, ranging from centimeters to meters. The robots can adapt to different object shapes, improving their grasping ability. A mini version can act as a tiny gripper, while a large version can be mounted on drones. Interestingly, multiple robots can work together to wrap around and hold large objects. That's just out of this world! 😮‍💨 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com