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AI-Native Organisations Run on Skills: How to Structure and Scale Them — Imad Touil, QuantumBlack

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Machine Learning

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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.

PhylaFlow: Hybrid flow matching in phylogenetic tree space

PhylaFlow: Hybrid flow matching in phylogenetic tree space

PhylaFlow is a hybrid flow-matching framework that navigates Billera–Holmes–Vogtmann (BHV) tree space to accelerate Bayesian phylogenetic inference. By learning geodesic paths from random trees to posterior samples, PhylaFlow efficiently initializes MCMC chains, drastically reducing the "burn-in" time. A PhylaFlow-MCMC variant, which guides MrBayes move acceptance, significantly outperforms traditional methods and even existing machine learning baselines for posterior sampling, achieving better results within the same computational budget. The work also explores conditioning on sequence embeddings, aiming for a future phylogenetics foundation model capable of zero-shot inference.

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

Rethinking Legacy Data Infrastructure with Eon Co-Founders Ofir Ehrlich and Gonen Stein

Rethinking Legacy Data Infrastructure with Eon Co-Founders Ofir Ehrlich and Gonen Stein

Eon co-founders Ofir Ehrlich and Gonen Stein discuss the critical role of data as a protective moat in the AI era, exemplified by Google's purchase of Spirit Airlines' data. They explain how Eon addresses the challenges of scattered enterprise data by providing tools for mapping, classification, and secure access, enabling its use in AI workflows. The conversation also delves into the emerging threat of autonomous AI agents with legitimate system permissions, the fundamental shifts required in enterprise data infrastructure, and how the rapid, fear-driven adoption of AI contrasts sharply with the slower cloud migration era.

The Death of Developer Advocates — Stephanie Jarmak, Sourcegraph

The Death of Developer Advocates — Stephanie Jarmak, Sourcegraph

Stephanie Jarmak argues that Developer Relations (DevRel) isn't dead, but its audience has shifted to include AI agents. She introduces the "Agent Advocate" role, focusing on measuring agent experience (e.g., through CodeScaleBench), optimizing for "Generative Engine Optimization" (GEO), and adapting traditional DevRel principles like enablement and community for this new user. The core message is that designing for agents, like a curb cut, ultimately improves the experience for all human developers.

🔬 The Physical World Is More Forgiving Than You Think — Anima Anandkumar, Caltech

🔬 The Physical World Is More Forgiving Than You Think — Anima Anandkumar, Caltech

Anima Anandkumar discusses her vision for AI in science, moving beyond language models to apply machine learning to the physical world. She introduces neural operators, especially Fourier neural operators, as a solution to data scarcity and resolution challenges in domains like weather, climate, and fusion. These models integrate physical constraints and data to achieve unprecedented speed and accuracy, even on consumer hardware, enabling capabilities from early hurricane prediction to digital twins for fusion reactors and inverse design for advanced materials. The conversation highlights the need for principled AI design for scientific discovery and advocates for distinct regulatory approaches for AI in science.

Knowledge Systems: The New GTM Stack — Jeffrey Wang, Exa

Knowledge Systems: The New GTM Stack — Jeffrey Wang, Exa

Jeffrey Wang, co-founder of Exa, details how "go to market" (GTM) is transforming into an AI engineering problem. He showcases Exa's agent-first approach, using tools like an ICP dashboard for market intelligence and a personal AI clone (Jeffbot) to automate and optimize sales, emphasizing the need for robust APIs and arbitrarily customizable systems in this new AI-driven landscape.

The mathematics of AI uncertainty

The mathematics of AI uncertainty

Zoubin Ghahramani, a leading researcher at Google DeepMind and professor at Cambridge, argues that incorporating uncertainty is a missing piece for ever-improving AI. He discusses the critical difference between correctness and confidence in AI, tracing the historical evolution of probabilistic models from early neural networks to modern Bayesian approaches. Ghahramani highlights how current large language models often 'fake' uncertainty and explores successful implementations in areas like weather forecasting and AlphaFold, ultimately advocating for architectural innovations over pure scale to build more robust, trustworthy, and human-aligned intelligent systems that understand their own limitations.

The State of AI: Models, Moats, and the Consumer Renaissance

The State of AI: Models, Moats, and the Consumer Renaissance

Anish Acharya and Jen Kha delve into the evolving AI landscape, dissecting the model layer with its emerging multiple winners and the strategic choice between frontier and open-weight models. They explore how the application layer captures value through model aggregation and specialization, ultimately ushering in a renaissance for consumer AI with personal agents, coding tools, and new economic models, urging founders to think big.

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