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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 Linear Algebra Powers Machine Learning (ML)

How Linear Algebra Powers Machine Learning (ML)

Fangfang Lee from IBM explains how linear algebra is the mathematical foundation of machine learning, enabling computers to understand data. The summary covers key concepts like vectorization, similarity metrics (Euclidean distance, cosine similarity), and dimensionality reduction using Singular Value Decomposition (SVD).

Inside the New Media Team with Marc Andreessen & Ben Horowitz

Inside the New Media Team with Marc Andreessen & Ben Horowitz

Erik Torenberg, Ben Horowitz, and Marc Andreessen of a16z dissect the paradigm shift from old to new media. They explore why an offensive strategy of 'flooding the zone' has replaced defensive PR, how authentic individuals have eclipsed sterile corporate brands, the strategic importance of speed and the OODA loop, and the nuanced interplay between oral and written cultures on the internet.

How Debuggers Work • Sy Brand • GOTO 2025

How Debuggers Work • Sy Brand • GOTO 2025

Sy Brand demystifies the internal workings of native code debuggers, explaining how core features like breakpoints, stepping, stack unwinding, and variable inspection are implemented using operating system primitives, debug information formats like DWARF, and low-level hardware features.

Perplexity Comet, agentic blabbering, and the shift-left failure

Perplexity Comet, agentic blabbering, and the shift-left failure

This episode explores the security risks of AI, including 'agentic blabbering' in AI browsers that aids phishing attacks, the ability of models like Claude Opus to resurrect vulnerabilities in legacy code, the debate on 'shift left' security practices, and new threats like AI-generated 'ephemeral malware' and the challenges of the post-authentication perimeter.

Write Reliable Software with Temporal

Write Reliable Software with Temporal

Johann Schleier-Smith from Temporal explains Durable Execution, a paradigm for building reliable, long-running applications. He details how Temporal's model of deterministic workflows and stateful activities provides a robust alternative to traditional checkpointing and event-driven architectures, especially for complex, LLM-driven agentic systems.

Greetings, Earthlings: Philip Johnston of Starcloud on Data Centers in Space

Greetings, Earthlings: Philip Johnston of Starcloud on Data Centers in Space

Philip Johnston of Starcloud argues that space will become the primary location for AI compute within a decade. He explains how plummeting launch costs, superior solar energy economics in orbit, and the physics of heat dissipation will soon make space-based data centers cheaper and more scalable than their terrestrial counterparts, predicting a future where nearly a trillion dollars in annual CapEx shifts to space.

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