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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

How to Build a Self-Improving Company with AI

How to Build a Self-Improving Company with AI

YC General Partner Tom Blomfield explains how to move beyond the traditional hierarchical company structure and build a self-improving organization using AI. He introduces the concept of recursive, self-improving AI loops that can optimize a company's operations, products, and knowledge base while the founders sleep.

Your Coding Agent Should Do AI System Engineering — Ben Burtenshaw, Hugging Face

Your Coding Agent Should Do AI System Engineering — Ben Burtenshaw, Hugging Face

Ben Burtenshaw from Hugging Face demonstrates how coding agents are tackling complex AI systems engineering tasks. He outlines a three-tiered approach: interactively writing CUDA kernels, autonomously fine-tuning LLMs, and deploying a multi-agent research lab (AutoLab) to parallelize experiments, all powered by file-based "skills" and open primitives on the Hugging Face Hub.

"Garbage In, Garbage Out" is a LIE

"Garbage In, Garbage Out" is a LIE

Terrence Lee-St. John, author of "From Garbage to Gold," challenges the "garbage in, garbage out" mantra. He presents a data-architectural theory explaining why models trained on noisy, high-dimensional tabular data can achieve robust predictive performance by focusing on recovering latent signals rather than exhaustive data cleaning.

CAG vs Long Context: How AI Models Use and Remember Information

CAG vs Long Context: How AI Models Use and Remember Information

Martin Keen explains how Long Context and Cache Augmented Generation (CAG) serve as powerful alternatives to RAG for providing external knowledge to LLMs. This summary details the mechanics of each approach, the role of the KV cache, the practical application through prompt caching, and the trade-offs in performance, cost, and latency for real-world AI workloads.

The Story Behind Cerebras’ $63 Billion IPO with Founder and CEO Andrew Feldman

The Story Behind Cerebras’ $63 Billion IPO with Founder and CEO Andrew Feldman

Cerebras CEO Andrew Feldman discusses the company's journey from a contrarian bet on wafer-scale computing to a $63 billion public company. He details the technical breakthroughs, the challenge of being ahead of the market, and how the recent explosion in AI demand for fast inference validated their architecture, leading to a landmark $20 billion deal with OpenAI.

What AI Agents Can Do Inside MATLAB and Simulink - Tianyi Zhu | Podcast #173

What AI Agents Can Do Inside MATLAB and Simulink - Tianyi Zhu | Podcast #173

Tianyi Zhu from MathWorks explains the key differences between AI agents and chatbots, highlighting how agentic AI acts as a powerful amplifier for engineers. The discussion covers practical use cases in MATLAB and Simulink, measurable ROI in automotive workflows, and strategies for safely integrating non-deterministic AI into high-stakes engineering environments.

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