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Artificial Intelligence, Internet of Things, Cloud and more. #MachineLearning #AR #VR #Automation

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Joined August 2010
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Digital twins are virtual models of physical entities used to optimize business operations. Success lies in understanding best practices, defining business goals, testing via prototyping, assessing organizational readiness, and adopting an agile approach. RT @antgrasso
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AI — the new law of unintended consequences…
Microsoft is spending $175 billion on AI while offering buyouts to 60,000 employees. 17 days later, a routine network update took down Outlook, Teams, and Copilot across 74 countries. 430 million users were locked out for 18 hours while European hospitals went back to paper fax machines. This is the story of incident EX1550543 and how Wall Street's favorite AI accounting trick broke the internet's backbone. October 26, 2026 Microsoft Chief People Officer Amy Coleman sends an internal memo across the US, UK, Canada, and India. The company is rolling out a Voluntary Exit Offer to 60,000 employees, roughly 27% of its global workforce. The buyouts reach all the way up to Level 70: - veteran systems engineers - senior directors and general managers - institutional memory built over 20 years Why would a profitable tech giant push 60,000 people toward the door by December 31? Because of how Wall Street grades AI spending: - $175B in AI server racks is CapEx, depreciated slowly over years - 60,000 human salaries are OpEx, deducted 100% from quarterly margins right now Cut the people, buy the GPUs, and your operating margin looks like a masterpiece. November 12, 2026 17 days after the buyout memo, at 12:30 PM EST, a network config change hits Exchange Online. A large batch of core servers suddenly fails Microsoft's automated health checks. The automated system does what it was programmed to do and pulls every failing server offline at once. Then the real nightmare starts. Live traffic from 430 million users dumps onto the few remaining servers in seconds. The sudden flood crushes the login and routing layer, sending 140,000 outage reports straight up on Downdetector. Outlook goes down. Teams goes down. SharePoint goes down. Even Copilot, the flagship AI assistant meant to replace human workflows, goes dark because it runs on the exact same legacy plumbing. Every time engineers try to restart a dropped server, the wall of waiting traffic immediately freezes it again. 18 hours later No AI agent could break the loop. Human engineers had to manually halt deployments, override the health checks, and bleed traffic back in slow stages while extending email timeouts to 48 hours. To this day, Microsoft incident codes EX1550543 and MO1550821 stand as a live stress test of replacing institutional memory with automation. You can spend $175 billion on AI clusters and cut 60,000 roles to make a quarterly earnings slide glow for Wall Street. In enterprise infrastructure, senior engineers aren't bloat on a spreadsheet, they are the only circuit breaker standing between a bad config file and a global blackout.
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Knowledge Structures — Recent Developments in Theory and Application (Textbook in Advanced Mathematical Psychology): link.amazon/B083oKoDI #mathematics 𝔹𝕠𝕠𝕜 𝕠𝕧𝕖𝕣𝕧𝕚𝕖𝕨 𝕗𝕣𝕠𝕞 𝔸𝕞𝕒𝕫𝕠𝕟: “Founded in 1985 by Jean-Claude Falmagne and Jean-Paul Doignon, Knowledge Structure Theory (KST) constitutes a rigorous and current mathematical theory for the representation and the assessment of human knowledge. The seminal work of these authors initiated a highly active research strand with an ever-growing literature, mostly scattered across various technical journals. Starting from a concise but comprehensive introduction to its foundations, this volume provides a state-of-the-art review of KST. For the first time the volume brings together the most important theoretical developments and extensions of the last decade and presents new areas of application beyond education, with contributions by key researchers in the field. Among the important advances covered by this book are (1) a comprehensive treatment of probabilistic models in KST; (2) polytomous extensions of the theory; (3) KST-based psychological diagnostics and neuropsychological assessment; (4) the representation and assessment of cognitive skills in problem solving, as well as procedural skills. In addition, this book also includes an overview of available software for the application of KST.”
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You chose a cheaper AI model. Good. But are you sure you saved money? Capgemini warns that errors and human review can change the final bill. Token prices are easy to compare. The harder part is working out what the organization spends to get a result. Microblog by @antgrasso Consider a marketing team preparing descriptions for a product catalog. One AI model costs less per request. Another costs more, but its drafts may need fewer revisions before publication. The team needs to compare what it spends for each approved description, including the work required to check the product information. The model with the higher price per request could turn out to be the less expensive choice. Capgemini calls this measure Cost Per Verified Outcome. It shifts the comparison from token consumption to the cost of a verified business result. But even a verified result needs to produce enough value to justify what the company spends to obtain it. #AIEconomics #AIManagement
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Highly rated book from @PacktPublishing @PacktDataML "Architecting Generative AI Applications: Build, deploy, and scale production-ready GenAI systems with LLMOps best practices" See it at amzn.to/3Pv4dyF
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Just released 2nd edition from @PacktPublishing … Building Agentic AI Systems — Learn to Build connected agent systems with MCP, A2A, agent loops, harness engineering, and cloud deployment: link.amazon/B0hWR3fPX … provides deep, broad, and comprehensive coverage of all components, connections, concepts, and controls in building Agentic AI systems
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The Claude Code Operating Model — Build scalable AI coding systems with Skills, MCP, Hooks, agent orchestration, and SDK patterns: amzn.to/4gwq1TV New release from @PacktPublishing @PacktDataML
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We’re giving away a hand-signed Picasso print for the best AI-generated video that teaches one idea about AI or data in 60 seconds or less. Before we name the winner on Wednesday, meet the 4 runners-up. #WinAPicasso
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Nobody goes to a Grand Prix for the queues. Catching crowd bottlenecks early is one of those behind-the-scenes improvements that can make the whole experience better. @TMobileBusiness Partner
We're putting our network to work at @F1LasVegas. From 5G-connected cameras and drones to real-time event operations, we're helping power race weekend. Next week, we're taking you behind the scenes. Learn more: secure.t-mobile.com/FVqHa6zL….
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Creative Intelligence In Mathematics And Science: link.amazon/B01qu2x0w
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88% of AI projects never reach production, and the model isn't always the problem. At Anaconda Scale 2026, Alan McCarty shared how AI orchestration brings trusted environments and reproducible workflows together. Watch on demand: anaconda.com/resources/webin…
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Machine Learning System Design Interview — insiders guide: link.amazon/B0dgxUq0Y What's inside: 🟦 An insider’s take on what interviewers really look for and why. 🔵 A 7-step framework for solving any ML system design interview question. 🔷 10 real ML system design interview questions with detailed solutions. 💙 211 diagrams that visually explain how various systems work.
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Your org chart may still look stable while Artificial Intelligence changes how work is done and what people need to do it well. Leaders need to see that gap early. A job title may stay the same even when the work attached to it changes. By @antgrasso.
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Who owns an AI agent after it goes live? And what happens when business teams start making changes? In part 5 of the CIO confessions series explores an accountability blind spot enterprises can't afford to ignore. Read more: bit.ly/4iPaXlt #AIConfessions
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New from @PacktPublishing Options Pricing with Python — Master option pricing and apply Python to quantitative finance, trading, and risk management: link.amazon/B03tXslwm
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20 Embedded Patterns Every Engineer Should Know — Design embedded systems that remain responsive, observable, and reliable: link.amazon/B00djJM4h from @PacktPublishing
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📣 Registration is NOW OPEN for #PyTorchDayChina, taking place December 12 at the Alibaba Beijing Chaoyang Science and Technology Park. This is a free, full-day event. Registration is required. Save your seat now and join us for technical talks and interactive discussions exclusively for the #PyTorch community >> bit.ly/3VmDBmf
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Map of Computer Science -- winner of "Information is Beautiful" Award in 2017 Poster: informationisbeautifulawards… Video: youtu.be/SzJ46YA_RaA
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Winners over the years — The "Information Is Beautiful" Awards: informationisbeautifulawards… 2022 Winner in the Exemplary Book category: "Data Sketches" — available at link.amazon/B06Prijfw
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How well do you understand neural net architectures? How are residual connections and layer norms interacting during training? Test your intuition with this CS312 question: halfway through training an LM, what happens if we pin the RMS norm after each residual connection? ↓ We train a 35M-parameter, 8-layer Transformer LM for 9,375 updates. Halfway through training (update 4,688), we fork into two runs: Control: continue training normally. Modified: after every attention/MLP residual addition, rescale the activations to match their RMS at the fork point. This is post-norm-like RMS scaling, with a fixed target scale. The RMS targets are measured from 16 validation sequences and then held fixed. How much does this change final validation loss? A. Improves by more than 0.02 B. Almost no change (within ±0.02) C. Worsens by 0.02–0.05 D. Worsens by 0.05–0.15 E. Worsens by more than 0.15, or diverges Which would you predict, and why? Try the full Quiz #4: deep-learning-alchemy.github…
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