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The new post-quantum cryptography executive order. Plus: What is Q-Day, really?

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Machine Learning

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Frontier results, on device - RL Nabors, Arize

Frontier results, on device - RL Nabors, Arize

RL Nabors discusses the significant costs associated with using frontier AI models, covering security, latency, and financial implications. She introduces a framework for right-sizing AI solutions by leveraging smaller, task-specific models and Small Language Models (SLMs). The framework details how to prove task feasibility, establish success criteria with golden datasets, conduct capability evaluations (using tools like Phoenix), and select the most appropriate "Small And Good Enough" (SAGE) model. Nabors further demonstrates how prompt engineering, particularly few-shot prompting, and post-processing can close performance gaps with larger models, while advocating for continuous regression evaluations to maintain performance integrity. The overarching message is to "prototype big, deploy small" to optimize AI deployments.

Research to Reality: Bringing Frontier ML Research to Production - Vaidas Razgaitis, Higharc

Research to Reality: Bringing Frontier ML Research to Production - Vaidas Razgaitis, Higharc

Vaidas Razgaitis, Senior Research Engineer at Higharc, shares three tactical tips to accelerate the transition of novel AI/ML research into production-ready features. He emphasizes addressing the critical handoff challenge between ML researchers and software engineers through structured documentation (Research Prototype Taxonomy Document), a well-organized monorepo utilizing decoupled microservices, and a systematic approach to code decomposition and PR review. These strategies aim to improve legibility, maintainability, and delivery speed for ML-driven products.

Uncertainty-Guided Data Augmentation for Engineers | Deep Dive - Yongmin Kwon

Uncertainty-Guided Data Augmentation for Engineers | Deep Dive - Yongmin Kwon

This session details a data-efficient method for training engineering surrogate models by using uncertainty quantification (UQ) to guide geometric data augmentation. Instead of random deformations, the approach lets the deep ensemble model identify its own knowledge gaps (epistemic uncertainty), then uses Free-Form Deformation (FFD) to generate new shapes specifically in those uncertain regions. This ensures every expensive simulation run yields maximally informative data, significantly improving model accuracy for a fixed computational budget across domains like structural mechanics and aerodynamics.

Artificial Intelligence

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Session on Reasoning

Session on Reasoning

This session features two talks on optimizing and verifying AI reasoning. Hongxiang Fan discusses cross-stack co-design for efficient AI, focusing on Test-Time Scaling (TTS) challenges, optimal verification granularity, and system-level optimizations for edge deployments. Nagarajan Natarajan introduces 'Advancing Verified Reasoning' with the InterVent platform, aiming to ensure AI agents comply with complex policies through formal verification, dynamic steering, and leveraging verification signals for training. Both emphasize addressing the computational and reliability costs of advanced AI.

Multimodal & Embodied Intelligence (Pt 1), Panel on Multimodal AI: Progress, Pitfalls, Possibilities

Multimodal & Embodied Intelligence (Pt 1), Panel on Multimodal AI: Progress, Pitfalls, Possibilities

This session explored Multimodal and Embodied Intelligence, featuring talks on hybrid AI in robotics (classical vs. end-to-end), AI's role in healthcare (focusing on NCDs, deployment, and uncertainty modeling), and fundamental perception challenges in multimodal reasoning (using educational video QA and visual puzzles). A panel discussed the impact of foundation models, the blurred lines between AGI and human-like AI, critical deployment pitfalls (human factors, efficiency, architectural limits), and future directions, emphasizing task-specific models and the redefinition of 'foundation models.'

Grant Sanderson (@3blue1brown) – AI and the future of math

Grant Sanderson (@3blue1brown) – AI and the future of math

Grant Sanderson and Dwarkesh Patel discuss AI's rapid but uneven progress in mathematics, exploring whether AI can achieve true conceptual breakthroughs, the challenge of measuring creativity, and the long-term implications for human understanding and the future roles of mathematicians. They delve into the unique 'grindability' of math for AI training, the potential of formalization, and why AI currently struggles with 'theory of mind' in writing, offering advice for students navigating an AI-transformed world.

Technology

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The new post-quantum cryptography executive order. Plus: What is Q-Day, really?

The new post-quantum cryptography executive order. Plus: What is Q-Day, really?

This episode delves into Q-Day, the anticipated future when quantum computers can break public key cryptography, and the U.S. Executive Order accelerating the transition to post-quantum cryptography. Experts discuss why Q-Day is a gradual process rather than a sudden event, the critical importance of "crypto-agility" as a long-term strategy, and the necessity for organizations to begin immediate discovery and planning to secure data against "collect now, decrypt later" threats. The discussion also touches upon the broader, transformative benefits of quantum computing beyond just security.

Plenary Talk 3​: Challenges and research opportunities for global hyperscale services

Plenary Talk 3​: Challenges and research opportunities for global hyperscale services

Jim Kleewein's talk outlines the immense challenges and critical research opportunities in building and operating global hyperscale services like Microsoft 365 and Azure. He emphasizes that at this scale, traditional approaches fail, necessitating a "new golden age of applied research" across areas like continuous availability, data management, security, and sustainability. Kleewein also discusses AI's powerful but limited role, stressing the ongoing need for human expertise, and highlights the ethical imperative to prevent failures that can have life-or-death consequences.

Platforms: Build Abstractions, not Illusions • Gregor Hohpe • GOTO 2025

Platforms: Build Abstractions, not Illusions • Gregor Hohpe • GOTO 2025

Gregor Hohpe explains the critical role of platforms in managing the growing cognitive load on developers due to complex distributed systems. He contrasts platforms, driven by "economies of speed" and fostering innovation through diversity, with traditional IT services and oversimplified abstractions that create dangerous illusions. Hohpe emphasizes building platforms that provide intuitive, domain-specific abstractions to solve real business problems, rather than just repackaging existing cloud services.

Recent Post

The Department of War is making a huge mistake.

The Department of War is making a huge mistake.

An analysis of the conflict between Anthropic and the US Department of War, exploring its implications for AI alignment, regulation, and the future of mass surveillance. The author argues that while Anthropic's stance is commendable, the structural nature of AI favors authoritarianism, making societal norms and specific laws—not broad regulatory bodies—the only viable defense for a free society.

Breaking & Securing OAuth 2.0 in Frontends • Philippe De Ryck • YOW! 2025

Breaking & Securing OAuth 2.0 in Frontends • Philippe De Ryck • YOW! 2025

This talk by Philippe De Ryck explains why common OAuth 2.0 patterns in Single Page Applications (SPAs) are fundamentally insecure against Cross-Site Scripting (XSS) attacks. He demonstrates how defenses like refresh token rotation can be bypassed and introduces the Backend-for-Frontend (BFF) pattern as the secure, recommended solution.

The conference that changed our minds about AI

The conference that changed our minds about AI

A deep dive into the [un]prompted AI security conference, the new Zero Day Clock initiative for vulnerability management, the emergent risks of autonomous AI agents, and the pervasive issue of burnout in the cybersecurity field.

Why AI Engineers Need to Understand GPU Hardware (with Chris Fregly)

Why AI Engineers Need to Understand GPU Hardware (with Chris Fregly)

Chris Fregly, author of 'AI Systems Performance Engineering', explains that true performance gains in AI come not from raw compute but from a deep, holistic understanding of the entire hardware and software stack. He emphasizes that memory bandwidth is the most critical GPU metric and introduces the concept of 'mechanical sympathy'—the co-design of hardware, software, and algorithms—as the key to unlocking efficiency and overcoming modern bottlenecks.

Build Hour: API & Codex

Build Hour: API & Codex

A deep dive into building agent-powered engineering workflows with Codex and OpenAI APIs, covering the shift from pair programming to agentic delegation, the principles of 'Harness Engineering' to make agents reliable, and practical examples from OpenAI and Basis.

10 years of AlphaGo: The turning point for AI | Thore Graepel & Pushmeet Kohli

10 years of AlphaGo: The turning point for AI | Thore Graepel & Pushmeet Kohli

Ten years after the historic match between AlphaGo and Lee Sedol, Google DeepMind's Thore Graepel and Pushmeet Kohli reflect on its legacy. They discuss how AlphaGo's blend of deep learning and tree search conquered the game of Go, the significance of creative breakthroughs like 'Move 37', and how these foundational concepts evolved into systems like AlphaZero, which learns without human data. The conversation bridges the gap from game-playing to solving scientific grand challenges, detailing how the same principles are now used in tools like AlphaTensor to discover novel, more efficient algorithms for fundamental problems like matrix multiplication.

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