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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

Sandboxing, Agent Harnesses, and Agent Teamwork

Sandboxing, Agent Harnesses, and Agent Teamwork

Shahram Anver, CEO of Cleric, details how AI agents for SRE are evolving beyond fast triage to continuous learning and operational memory. He discusses Cleric's architectural shifts, from complex early designs to simpler, sandboxed query agents, and the unique challenges SRE agents face in diverse production environments. A core focus is on human-agent interaction, redefining roles as managers overseeing agents, and how agents learn from unstructured data like Slack to build robust, actionable knowledge for autonomous, self-healing infrastructure. The discussion also touches on the future of software, differentiating between durable systems and rapidly developed "skills" or "vibe-coded" solutions.

The First Dedicated YC GPU Cluster - With Together AI

The First Dedicated YC GPU Cluster - With Together AI

YC and Together AI have partnered to launch the first dedicated YC GPU cluster, addressing the critical compute bottleneck faced by AI-native startups. This initiative provides flexible, cost-effective access to GPU resources, enabling companies from early-stage research to major players to train, fine-tune, and run inference on AI models, and mitigating the financial strain of long-term compute commitments.

6 Ways to Enhance Developer Productivity with AI

6 Ways to Enhance Developer Productivity with AI

Discover how top-performing teams achieve 100-150% productivity gains with AI by strategically restructuring workflows, automating wisely, prioritizing design, fostering deep work, reducing cognitive load, investing in growth, and optimizing tools. This goes beyond mere AI adoption, focusing on human-centric practices and intelligent measurement to drive real impact and retention.

Why Your Agent Disagrees With Itself (And What To Do About It) - Diane Lin, Datadog

Why Your Agent Disagrees With Itself (And What To Do About It) - Diane Lin, Datadog

This session addresses the critical problem of AI agent inconsistency, particularly in LLMs, which leads to "flip-flops" in high-stakes domains like cybersecurity. It argues that this isn't a model failure but a signal of ambiguity in the "gray zone" near decision boundaries. The proposed solution involves using active learning to identify these ambiguous cases, followed by augmenting agents with semantic memory (explicit policies) and episodic memory (past similar cases) to clarify decisions, improve consistency, and adapt to customer-specific preferences without relying solely on expensive fine-tuning.

Enterprise Agents Have a Structure Problem - Ishita Daga, Tesla

Enterprise Agents Have a Structure Problem - Ishita Daga, Tesla

Ishita Daga, a Senior ML Engineer at Tesla, explains why enterprise AI agents fail, highlighting that current fixes like larger models or more RAG are insufficient. She identifies ambiguity, staleness, and user preference as key structural problems. Daga proposes a solution built on semantic retrieval infrastructure: a hierarchical approach to knowledge sources, including a curated semantic layer and metadata graphs. She also details a robust context life cycle with live data sources and continuous feedback loops to combat staleness. The challenge of integrating individual preferences, requiring agents to reason over business concepts rather than raw schemas, is also discussed.

From Tokens to Cells: Foundation Models for Single-Cell Biology - Akram Baharlouei, Altos Labs

From Tokens to Cells: Foundation Models for Single-Cell Biology - Akram Baharlouei, Altos Labs

Akram Baharlouei from Altos Labs discusses the engineering hurdles in developing foundation models for single-cell biology. The talk covers the importance of single-cell analysis for cellular rejuvenation and drug discovery, the complexities and challenges of single-cell data (particularly RNA-seq), and a comparative analysis of current foundation model approaches, highlighting the limitations of transformer-based models and the potential of flow matching techniques like PrimeFlow.

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