Mat Velloso

For years, The Cuckoo’s Egg by Clifford Stoll has been required reading in my Introduction to Cybersecurity course, and although the technology described in The Cuckoo's Egg is undeniably dated, its human lessons remain timeless. The modems are slow, the systems primitive, and the investigative tools far less sophisticated than what our students encounter today, yet the core qualities on display relentless curiosity, vigilance, persistence, and moral courage are exactly what define effective cybersecurity professionals. Stoll’s refusal to ignore a minor accounting discrepancy, his determination to pursue the truth despite skepticism from colleagues, and his unwavering commitment to evidence over opinion model the mindset we strive to cultivate in our students. Cybersecurity is not simply about mastering tools or frameworks; it is about developing the intellectual discipline to question anomalies, the resilience to endure doubt, and the integrity to follow facts wherever they lead. Cliff Stoll stands as a living example of what success looks like in this field not because of the era’s technology, but because of the enduring character traits that made his investigation possible. #captechu #cybersecurity

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As a prelude to the AI Infra Summit starting today at the Santa Clara Convention Center (Sept 9-12 2025), I published a paper highlighting the architecture challenges facing the industry. You can download the paper from the following link: https://lnkd.in/gz3-3cTn Artificial Intelligence is no longer a niche workload—it is the engine driving hyperscale data centers, autonomous vehicles, and mission-critical systems worldwide. Yet behind the promise lies a crisis in design: unpredictable workloads, skyrocketing silicon costs, interconnect bottlenecks, thermal hotspots, and the fragility of heterogeneous chiplet integration. This paper cuts through the noise, showing why traditional tools and benchmarks are failing us and why predictive, system-level exploration is now essential. From chiplets to cloud, it demonstrates how early modeling uncovers bottlenecks, reduces risk, and saves millions in time and cost, while enabling safer, faster, and more energy-efficient AI infrastructure. If you care about building AI systems that actually deliver in the real world—reliably, at scale, and ahead of competitors—this is the paper you cannot afford to ignore #AISystemArchitecture #ErchitectureExploration #PredictiveModelingForAISystems, #AIInfrastructureDesign #ChipletSystemDesign #SoCArchitectureExploration #DataCenterAIArchitecture #VisualSimArchitect #AIWorkloadModeling #ChipletIntegrationChallenges #SystemLevelSimulationTools #CacheHierarchyOptimization #MemorySystemArchitecture #InterconnectBottlenecksInAI #AIPowerAndThermalManagement #AIPerformanceVSReliabilityTradeOffs #EarlyStageSystemDesignExploration

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⚙️ Claude Code is creating a buzz among Seattle engineers, who describe the AI tool as a supercharged coding partner that can refactor large codebases, handle tedious boilerplate, and stitch services together at speed. The article paints a picture of software teams rethinking roles, workflows, and onboarding as coding assistants shift from autocomplete to true collaborators. For engineering leaders, the big challenge is how to harness this productivity jump while maintaining code quality, security, and human expertise in the loop. #AI #Claude #SoftwareDevelopment #EngineeringLeadership #DevTools #Productivity #AIFreakyFacts

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Researchers at UCLA and UC Riverside have created a quantum computing device that operates at room temperature, potentially reducing energy costs and advancing hybrid quantum-classical systems.

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Terence Tao - discusses many tools and techniques solving “Erdos” problems leveraging AI as an assistant. Paul Erdős was actually a tutor to Terence as a child. “Machine assistance and the future of research mathematics” - 29 minute YouTube video Terence Tao, Institute for Pure & Applied Mathematics, UCLA, 10 February 2026 Link: https://lnkd.in/eCtzmTs3 #math #science #verifiable

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Andrew Ng reported from Davos, that AI deployment for transformative impact will require workflow redesign. It is "theory of constraints" all over again (remember Eli Goldratt's "The Goal"?), we have to understand that real business benefits come only from working across silos, from analyzing flow bottlenecks, from understanding that optimizing non-bottlenecks creates waste. As Ng noted, letting a thousand (AI) flowers bloom has failed to lead to significant payoffs. Obviously! We in the silicon industry are no strangers to this. Workflows have been stagnant for decades. Technologies for high level specification, high level synthesis, and formal verification are not yet integrated into the full silicon development flow. They are often done piece-meal, and enthusiasts' pilots are dropped at the hint of a failure or delay. People say they want formal verification in their flow, but want it yesterday and do not have the time to invest in training engineers for its rigorous usage. Ashish Darbari, responding to a question on "if formal verification is so powerful then how come it is not widely used", noted that the problem is in the name - the actual task to be done is formal analysis while the bug detection is only a corollary, but the industry puts the cart before the horse. Based on my experience with industrial practice of high level specifications and formal verification, I tend to agree with his succinct comment. Jan Rabaey, in a DAC keynote speech, said that someday he expects to see a comic book series entitled "adventures in formal verification". AI deployment faces the same problem, if not actively worked on by business leaders. To be fair, deployments of all disruptive technologies face this mental barrier, we are so used to thinking in terms of the old flow (using the old technology) that thinking of a new flow literally means us having to begin again from first principles - few leaders have the courage and the time to do that. Goldratt mentioned that he himself had faced the same mindset problem when deploying the early ERP solutions and was surprised why he wasn't getting the expected large benefits, and he said it took him many years before he could identify the core problem and define solutions for it, which he named (rather academically) as "theory of constraints". AI leaders would do well to rework the conventional workflows in a holistic manner in order to get the real benefits from this new technology. As Henry Ford had said, "If you always do what you've always done, you'll always get what you've always got".

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🚀 My Java learning journey has officially paid off! After weeks of diving deep into a new language, I’m excited to share the results: I just published a full article on building generative AI applications in Java using LangChain4j. Java has always been a powerhouse in enterprise systems, but seeing how quickly generative AI tooling is evolving around it has been eye-opening. From structured output to RAG, I walked through how to build real GenAI workflows entirely in Java — and reflected on how it compares to the Python ecosystem we’re all so familiar with. Whether you come from the Java world stepping into GenAI, or from Python wondering how Java is adapting, I hope this deep dive gives you a clear, grounded view of where the ecosystem is heading.

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