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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

What is an AI Code Generator? LLM Coding, Productivity, & Risk

What is an AI Code Generator? LLM Coding, Productivity, & Risk

AI code generators leverage LLMs to translate natural language into code, significantly boosting developer productivity and job satisfaction by automating boilerplate and accelerating learning. However, they introduce risks like subtle security vulnerabilities and require rigorous human review. Evaluating tools hinges on trust, demanding features like data provenance, governance, secure deployment, and curated training data for enterprise adoption.

RLM: Recursive Language Models for Large Codebases - Shashi, Superagentic AI

RLM: Recursive Language Models for Large Codebases - Shashi, Superagentic AI

This talk introduces Recursive Language Models (RLM) as a solution for coding agents struggling with large codebases. RLM externalizes context management into a programmable REPL, allowing the model to write code, inspect the repository, and recursively delegate sub-questions via `llm_query` to curate relevant context. The speaker demonstrates `RLM Code`, an independent implementation, showcasing its operational loop, observability features, and practical applications for AI engineers dealing with complex software projects.

The AI bugpocalypse is here. Now what? - Jack Cable, Corridor

The AI bugpocalypse is here. Now what? - Jack Cable, Corridor

Jack Cable discusses the "AI bug apocalypse" driven by advanced AI models finding and exploiting vulnerabilities and AI coding tools increasing attack surfaces. He champions a "secure by design" approach, advocating for systemic changes like using memory-safe languages to prevent common vulnerability classes rather than just patching. He also addresses AI's role in introducing new vulnerabilities, the shift towards autonomous AI in development, and policy recommendations for securing the future of AI-powered coding.

Semantic Blindness: 500,000 Sensors Confused an LLM - Raahul Singh & Vanč Levstik, Phaidra

Semantic Blindness: 500,000 Sensors Confused an LLM - Raahul Singh & Vanč Levstik, Phaidra

Modern LLMs struggle with combinatorial engineering problems in industrial settings due to "Semantic Blindness" – an inability to understand physical system topology, scale efficiently, or handle repetitive naming conventions. This talk introduces a hybrid AI architecture that combines LLMs for high-level planning with deterministic systems for execution, leveraging hierarchical structures and pattern-based search. This approach achieves 100% accuracy and flat operational costs at massive scales, demonstrating an "inversion" of the Software 1.0/3.0 paradigm where AI-native systems mature by integrating deterministic code for reliable, structured tasks.

The Agentic Web and the Bazaar Era of AI - Ramesh Raskar, MIT Media Lab

The Agentic Web and the Bazaar Era of AI - Ramesh Raskar, MIT Media Lab

Project Nanda introduces the Agentic Web, an open infrastructure designed to break AI agents out of "walled gardens" and enable truly interconnected, autonomous interactions. It focuses on three core layers: Discovery, using the Nanda Index for agents to find and trust each other; Commerce, facilitating "knowledge pricing" markets for agents to pay for intelligence and services; and the Bazaar, fostering machine co-learning through the Nanda Town simulator, a sandbox for testing large-scale agent coordination and protocols. This initiative aims to establish an open, permissionless ecosystem for trillions of AI agents.

Why the tech workforce is quietly splitting in two | Annual AI sentiment survey (Noam Segal)

Why the tech workforce is quietly splitting in two | Annual AI sentiment survey (Noam Segal)

Noam Segal and Lenny Rachitsky's second annual Tech Worker Sentiment Survey reveals a striking bifurcation in the tech workforce: 50% feel energized and amplified by AI, while the other half feels redefined, destabilized, or diminished. Burnout is surging (up 11 points in a year) and optimism is declining, even as 97% believe AI makes them 'better' (often meaning faster, not higher quality, leading to 'cognitive rot'). The #1 fear isn't job loss to AI, but being squeezed to do more for the same pay. Nobody would recommend their job to someone entering the industry today, with designers and researchers being the most negative. Managers are identified as the single biggest lever for employee well-being, yet most are rated ineffective. The industry is described as 'chaotic,' with advice for employees to go deep on AI, manage the 'squeeze,' and invest in manager relationships, while leaders are urged to invest in managers, manage expectations, and support early talent and struggling roles.

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