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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

OpenAI’s Plan to Make ChatGPT the Everything App — Akshay Nathan, OpenAI

OpenAI’s Plan to Make ChatGPT the Everything App — Akshay Nathan, OpenAI

Akshay Nathan, head of Core Product Engineering at OpenAI, discusses the journey and rationale behind ChatGPT Work. He explains how Codex's unexpected adoption by non-developers led to a unified agent harness, blurring the lines between developer and knowledge worker tools. The conversation delves into model capabilities, the role of artifacts and interactive 'Sites' in replacing traditional documents, and how AI fosters a new era of 'T-shaped' generalists. Nathan emphasizes the shift in productivity bottlenecks to 'ideas and taste' and the importance of 'quality at-bats' over mere 'motion' in an AI-powered development landscape.

The Next Frontier of AI Is Spatial Intelligence | Fei-Fei Li on a16z

The Next Frontier of AI Is Spatial Intelligence | Fei-Fei Li on a16z

Fei-Fei Li and Yunzhu Li discuss World Labs' acquisition of SceniX, focusing on building "spatial intelligence" and "large world models" to enable robots to understand and interact with the physical world. They elaborate on SceniX's "real-to-sim-to-real" pipeline, emphasizing how simulation, coupled with generative models like Marble, addresses the data bottleneck in robotics by providing consistent, scalable, and efficient training and evaluation environments. The conversation covers the role of counterfactual reasoning, the development of robotics foundation models, and the strategic focus on semi-structured environments for pragmatic, reliable robot deployment.

Serving 2 Million Models Without Melting: Scaling the Hugging Face Hub — Arek Borucki, Hugging Face

Serving 2 Million Models Without Melting: Scaling the Hugging Face Hub — Arek Borucki, Hugging Face

Arek Borucki details how Hugging Face scales its infrastructure to serve millions of models and users, focusing on the evolution of search architecture using MongoDB Atlas and Apache Lucene, robust database scaling with a seven-node cluster and sharding, and dynamic frontend autoscaling with Kubernetes and KEDA to ensure an instant, seamless user experience.

Green AI: Making Machine Learning Environmentally Sustainable • Charles Humble • YOW! 2025

Green AI: Making Machine Learning Environmentally Sustainable • Charles Humble • YOW! 2025

Charles Humble explores the significant environmental impact of AI, particularly generative AI, on global carbon emissions. He offers practical, actionable strategies across the entire AI lifecycle—from project planning and data collection to training and deployment—to reduce this footprint. Key recommendations include questioning the necessity of AI solutions, choosing open-source models, leveraging carbon-aware computing for demand shifting, employing model compression techniques like distillation and quantization, and integrating sustainability as a fundamental architectural principle in software engineering.

What Actually Makes an Algorithm Terrifying (with Cathy O'Neil)

What Actually Makes an Algorithm Terrifying (with Cathy O'Neil)

Dr. Cathy O'Neil, author of "Weapons of Math Destruction," asserts that terrifying algorithms are defined by secrecy, unaccountability, and a lack of opt-out, not mathematical complexity. She details how Taylorism's labor degradation now extends to white-collar jobs via AI surveillance. O'Neil discusses her firms, ORCAA and OCEAN, which provide statistical evidence for lawsuits against tech giants and advocate for algorithmic accountability through "cockpits" of metrics and transparent auditing, urging collective action against unchecked technological power.

Llama.cpp vs vLLM: Which Local LLM Engine Actually Scales?

Llama.cpp vs vLLM: Which Local LLM Engine Actually Scales?

Explore the strengths and optimal use cases of `llama.cpp` and `vLLM` for local LLM inference. `llama.cpp` excels on consumer hardware with optimizations like quantization and CPU support, while `vLLM` is designed for production-scale efficiency with features like continuous batching and speculative decoding on high-performance accelerators.

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