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Guardians of the State: An Air-Gapped AI Fortress for Consumer Data — Rachna Srivastava, DFPI

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Machine Learning

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Guardians of the State: An Air-Gapped AI Fortress for Consumer Data — Rachna Srivastava, DFPI

Guardians of the State: An Air-Gapped AI Fortress for Consumer Data — Rachna Srivastava, DFPI

Rachna Srivastava discusses building a legally defensible AI system for financial fraud detection, emphasizing that generative AI has eroded traditional trust. Her team at DFPI engineered an offline, hardware-secured data pipeline using Kafka, Spark, and semantic routing to ensure explainability, reproducibility, and auditability. They implemented a one-way data diode for secure learning and Apache Iceberg for time-travel queries, asserting that "trust is a physical property" built into the system's core.

Invited Research Talk: Measuring Generalization in EEG Foundation Models

Invited Research Talk: Measuring Generalization in EEG Foundation Models

This talk presents a multi-dimensional evaluation and interpretability framework for EEG foundation models. It reveals that current models often fail to outperform supervised baselines for BCI tasks, lack robustness to sparse channels, and exhibit an aperiodic low-frequency bias, making them better at capturing subject-specific rather than task-specific information. The analysis highlights critical deficiencies and suggests future directions for pre-training objectives and data collection to improve generalization.

Can LLMs Write Fast Multi-GPU Kernels? — Simran Arora, Together AI

Can LLMs Write Fast Multi-GPU Kernels? — Simran Arora, Together AI

Simran Arora discusses the critical bottleneck shift in large AI workloads from GPU compute to inter-GPU communication. Her team's solution, ParallelKittens, offers a set of primitives to optimize multi-GPU kernels by leveraging fundamental transfer mechanisms and compute-communication overlapping. They introduce ParallelKernelBench, a benchmark to evaluate AI models' ability to generate such kernels, revealing that while models can handle syntax, they struggle with deeper reasoning about communication patterns and hardware trade-offs.

Artificial Intelligence

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AI-Native Organisations Run on Skills: How to Structure and Scale Them — Imad Touil, QuantumBlack

AI-Native Organisations Run on Skills: How to Structure and Scale Them — Imad Touil, QuantumBlack

Imad Touil explores the critical need for governing AI skills within organizations, asserting that skills represent the true repository of enterprise know-how. He contrasts simplified coding agent loops with complex, real-world product lifecycles, demonstrating how ungoverned skills lead to technical debt—including duplication, quality degradation, security risks, and lack of discoverability. Proposing a microservices-inspired approach, Touil outlines a centralized skills platform and human-led governance model essential for achieving deterministic workflows, boosting productivity, and mitigating risks in AI-native enterprises.

Your Code Has Bugs. Lean4 Has Proofs: Formal Verification for Engineers — Varun Pant, AWS

Your Code Has Bugs. Lean4 Has Proofs: Formal Verification for Engineers — Varun Pant, AWS

Varun Pant introduces formal verification as the solution to reliably validate AI-generated code, proposing a division where humans define specifications and machines handle code and proof. He details Lean's role as a unified language for code and proof, exemplified by an AI rewriting zlib with 32,000 lines of proof, and AWS's Cedar using Lean specs with Rust production code reconciled by 100 million nightly tests. The talk also covers deductive verification with solvers and future cross-language verification with Strata, aiming for "provably correct" software.

How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage) — Eyal Blum, Figma

How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage) — Eyal Blum, Figma

Figma's internal AI agent adoption journey faces challenges like reduced developer agency, skepticism from senior engineers, and communication inefficiency. Solutions include investing in verification, using a testing pyramid for agent review, prioritizing detailed planning over prompting, engaging skeptics to build AI safety roadmaps, and implementing attention-aware communication by clearly marking AI-generated content.

Technology

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Platform Engineering for Developers, Architects & the Rest of Us • Daniel Bryant • GOTO 2025

Platform Engineering for Developers, Architects & the Rest of Us • Daniel Bryant • GOTO 2025

Daniel Bryant discusses platform engineering for software developers and architects, emphasizing treating platforms as internal products with developers as customers. He outlines a three-layered architecture, the evolution from monolithic systems to microservices, and the importance of 'golden bricks' over 'golden paths' for composability. Key takeaways include API-first design, minimizing cognitive load, avoiding leaky abstractions, and measuring success through frameworks like DORA and DevEx to achieve speed, safety, and scale.

Modern Enterprise Architecture: Architecting for Outcomes • Simon Rohrer • GOTO 2025

Modern Enterprise Architecture: Architecting for Outcomes • Simon Rohrer • GOTO 2025

Simon Rohrer challenges traditional Enterprise Architecture (EA) principles, proposing a modern approach focused on outcomes, continuous evolution, and socio-technical alignment. He outlines five key tenets: Aligning Value, People & Technology; achieving Better Value Sooner, Safer, Happier; implementing Continuous Conversational & Automated Governance; scaling DevOps across the enterprise; and fostering Evolutionary Enterprise Architecture.

Elon's Former Battery Chief on Making Transformers 100x Smaller | Drew Baglino, Heron Power

Elon's Former Battery Chief on Making Transformers 100x Smaller | Drew Baglino, Heron Power

Drew Baglino, former Tesla Powertrain & Energy head and now CEO of Heron Power, reveals why the current electricity grid is inadequate for the explosive growth of AI data centers. He explains how Heron Power's wideband gap power semiconductors will revolutionize grid-to-chip infrastructure, cutting power losses by half, shrinking massive transformers by 100x, and transforming data centers into grid-positive assets for a more efficient and sustainable energy future.

Recent Post

Open Models at Google DeepMind — Cassidy Hardin, Google DeepMind

Open Models at Google DeepMind — Cassidy Hardin, Google DeepMind

Cassidy Hardin from Google DeepMind introduces Gemma 4, a new family of open-weight models with significant architectural and performance improvements. This summary covers the four new models (31B Dense, 26B MoE, and two "Effective" on-device models), deep dives into architectural changes like mixed global/local attention and Per-Layer Embeddings (PLE), and details the new native multimodal capabilities for vision and audio.

The $15B Physical AI Company: Simulation, Autonomy OS, Neural Sim, & 1K Engineers—Applied Intuition

The $15B Physical AI Company: Simulation, Autonomy OS, Neural Sim, & 1K Engineers—Applied Intuition

Qasar Younis and Peter Ludwig, founders of Applied Intuition, discuss the shift from autonomy tooling to a comprehensive physical AI platform. They explain why physical AI is more than just LLMs on wheels, highlighting the critical bottleneck of deploying models onto constrained hardware. The conversation covers their three-pillar tech stack—simulation, operating systems, and AI models—and makes the case for an 'Android for every moving machine' to solve the fragmentation in safety-critical systems like cars, trucks, and robots.

It's 2026, and We're Still Talking Evals

It's 2026, and We're Still Talking Evals

Maggie Konstanty, AI Product Manager at Prosus, provides a candid look into the realities of LLM evaluation in production. She argues that standard metrics like accuracy are misleading and advocates for a culture of continuous, goal-oriented evaluation focused on deep failure analysis and understanding real user behavior, asserting that mature teams inevitably build custom tooling to meet their specific needs.

Why Agents are Driving Software Development to the Cloud

Why Agents are Driving Software Development to the Cloud

Zach Lloyd, CEO of Warp, explains why the future of software development is moving from local, interactive agents to cloud-native, collaborative systems. He discusses the flaws in the "dev box" sandbox model, the decline of traditional SaaS interfaces in favor of "just-in-time apps," and how platforms like Warp's Oz are providing the necessary orchestration, observability, and access control for teams to effectively deploy AI agents at scale.

What is OpenClaw? Inside AI Agents, LLMs and the Agentic Loop

What is OpenClaw? Inside AI Agents, LLMs and the Agentic Loop

AI agents represent a paradigm shift from conversational AI to autonomous systems that can perform actions. This is achieved through an 'agentic loop' combining Large Language Models (LLMs) with tools, as exemplified by the OpenClaw framework, which enables complex, automated workflows while also raising important security considerations.

Collaborative AI Engineering — Maggie Appleton, GitHub Next

Collaborative AI Engineering — Maggie Appleton, GitHub Next

Maggie Appleton from GitHub Next argues that current agentic tools are flawed by focusing on individual productivity, ignoring the collaborative nature of software development. She introduces ACE (Agent Collaboration Environment), a multiplayer platform designed to solve team alignment issues by integrating planning, development, and shared context in a real-time, sandboxed environment.

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