The World’s Biggest Cloud Computing Service Providers visualcapitalist.com/worlds-… #Cloud #AWS #Azure

How to set up Claude Code so it runs like a full dev team: 5 folders. That's the entire system. 1. CLAUDE.md → Memory. Your repo's constitution. Naming rules, structure, expectations. One global file for all projects, one local file per repo. 2. skills/ → Knowledge. Reusable workflows Claude auto-invokes by matching the task description. No slash commands. It just knows. 3. hooks/ → Guardrails. Shell scripts that run before and after every tool call. Block dangerous commands. Auto-lint on save. Ping Slack on deploy. Deterministic. Not AI. 4. subagents/ → Delegation. Isolated agents with their own context window. A code reviewer that only sees the diff. A test runner with custom permissions. Keeps your main session clean. 5.plugins/ → Distribution. Bundle the whole system into one install. Every teammate gets the same skills, same hooks, same agents. Aligned from day one. This is the Agent Development Kit. Five layers, one stack. To learn how and get the full Claude guide: 1. Go to simplifyingai.co 2. Subscribe free by just writing your email. 3. Open my welcome email and get the free resources. Repost ♻️ to help someone in your network.

An awesome regex visualization tool regex-vis.com

Always popular! Fifty Things You Can Do With a Software-defined Radio (SDR). blinry.org/50-things-with-sd… @MehdiHacks @cqcqcqdx @K3TripleR

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.

Hello you fine Internet folks, Today's article is on @Arm's Cortex A725 core found inside the @Dell Pro Max with GB10 and how Arm has evolved their density focused core from the A710 to the A725. Hope y'all enjoy! chipsandcheese.com/p/arms-co… old.chipsandcheese.com/2026/…

How Dijkstra's Algorithm find the best shortest paths to real world maps, 🫡 The new algorithm runs in O(m |log^ {2/3} n) time. Potential applications? Faster shorter waits for route calculations, fewer traffic jams, cheaper deliveries, and more efficient computer networks.

#Tescan, world leading electronic microscope company, has been acquired by Shimadzu. What thé hell Is #Shimadzu?!? See below...

What #AI model is the best one for a specific purpose? zive.cz/clanky/nejlepsi-ai/s… by @marekl

Setup WPS web w/o any programming knowledge by @claudecode

Your next portable? kv4p HT – Turn an Android smartphone into an ham radio transceiver cnx-software.com/2025/10/04/…

100k H100 GPU Colossus 1 blew our minds in July 2024. It was an unfathomable amount of compute becoming the most powerful AI Supercomputer ever created. Then it was increased to 200k by October, just 92 days after the initial 100k began training. This was 150k H100 50k H200. The H200 had the same amount of compute as H100 but more memory and higher bandwidth to help train bigger models. Colossus 1 then had an additional 30k GB200 chips bringing it to 230k GPUs. The GB200 is roughly 10x the AI compute performance of a H100. Now with Colossus 2 coming online in a few weeks with 550k GB200 GPUs, it’s roughly the equivalent of 5.5 million H100s!! So Colossus 2 is equivalent of building 55 of the Colossus 1 clusters with 100k GPUs, which was the most powerful AI supercomputer the world had ever seen just 12 months ago. 55x in one year. And Elon is saying they will get to equivalent of 50 million H100 within 5 years. So that will be 500x from the state of the art cluster 12 months ago. xAI speed of execution is unmatched.

Nvidia: New kid on the #ARM block (forget #Tegra) tomshardware.com/pc-componen…

VMware ESXi free again? #WMware #Esxi servethehome.com/broadcom-vm…

ChatGPT is a super powerful tool, but only if you write good prompts. 15 Powerful ChatGPT Prompts to Save You 20 Hours a Week:

Got a detailed price/die/density analysis of Apple A-silicon over time at TSMC. Some nuggets. From A7 to A18: - progression from 28nm to 3nm - Most dramatic shrinks occurred early (28nm → 20nm → 16nm/14nm) - Steady increase in transistor count from 1 billion (A7) to 20 billion (A18 Pro) - Die Size: Relatively consistent between 80-125 sq.mm despite increasing transistor count - Wafer prices increased from $5,000 (A7) to $18,000 (A17-A18 Pro) - Cost per sq.mm rose from $0.07 to $0.25 - Peak density improvements around the A11-A12 period (86% and 69%) A particularly interesting observation is that while manufacturing costs have increased significantly, Apple has managed to maintain relatively stable die sizes while dramatically increasing transistor density. The slowdown in density improvement percentages in recent generations (A16-A18 Pro) suggests we may be approaching the physical limits of current manufacturing technologies.

15. Places where birthright Citizenship is based on land and places where it is based on blood

This blows my mind! NotebookLM is the most impressive AI tool I’ve seen since ChatGPT. From students to professionals, it’s a game-changer for everyone: