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

Aug 28, 2026

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Machine Learning

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Aug 27, 2026

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.

Aug 27, 2026

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.

Aug 19, 2026

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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Aug 28, 2026

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.

Aug 28, 2026

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.

Aug 28, 2026

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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Aug 26, 2026

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.

Aug 19, 2026

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.

Aug 18, 2026

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

Aug 26, 2026

AI in GTM at Notion — Flora Liu

Flora Liu from Notion's GTM engineering team discusses transforming a fragmented Go-to-Market system into a unified, agent-driven platform. She details how Notion tackles challenges like dispersed customer data and unstructured insights by building a four-layered architecture (Know, Decide, Act, Learn) where humans and AI agents operate on the same substrate, leveraging Snowflake, DynamoDB, and Notion itself to create durable, self-improving workflows and boost sales and marketing effectiveness.

Aug 26, 2026

Platform Engineering for Developers, Architects & the Rest of Us • Daniel Bryant • GOTO 2025

Aug 26, 2026

Who’s afraid of an open-weight model? GLM, context bombing and post-Black Hat attacks

This episode dives into GLM-5.3's advanced vulnerability discovery, debating its potential for good or harm. It then explores "context bombing," an innovative defensive use of prompt injections, and analyzes recent social engineering attacks targeting cybersecurity pros after the Black Hat conference, emphasizing human vulnerability and the need for robust AI defenses.

Aug 26, 2026

The Missing Layer in Agentic AI — Giedrius Šteimantas, Oxylabs

Giedrius Šteimantas discusses the often-overlooked infrastructural layer for AI agents operating on the open web. He highlights how traditional browser automation leads to high costs and unreliability, particularly due to CAPTCHAs and invalid content. Drawing from ten years of web scraping experience at Oxylabs, he presents a multi-stage solution that prioritizes cost-efficiency and content validation, using specialized APIs and hardened headless browsers only when absolutely necessary, thus significantly reducing token waste and improving agent performance.

Aug 25, 2026

Einstein Arena: Harnessing Collective Agent Intelligence for Open Science — James Zou, Together AI

James Zou presents a novel approach to AI development by advocating for the design of environments over workflows, fostering emergent intelligence and creativity. He introduces the **Einstein Arena**, where AI agents collaboratively and competitively solved open scientific problems like the kissing number problem, achieving breakthrough results (e.g., 604 spheres in 11 dimensions). The same principles successfully optimized GPU kernels, leading to 2x+ speedups. He also discusses **DSGym**, an environment for data science agents, addressing shortcomings of existing benchmarks by eliminating 'shortcuts' and enabling the training of high-performing, fine-tuned open-source models runnable on laptops.

Aug 25, 2026

⏭️ Forward Deployed: Voice AI on what works in 2026

This podcast episode delves into the pragmatic realities of building and deploying voice agents in enterprise. Featuring leaders from Decagon, Vapi, Retell, Daily, and Smallest AI, the discussion uncovers why the current state-of-the-art relies on cascaded pipelines (STT -> LLM -> TTS) instead of direct voice-to-voice models. Key challenges explored include managing latency versus intelligence, ensuring system reliability through fallback models, the complexities of turn-taking, and architectural strategies to overcome LLM context limitations and optimize costs. The experts also touch upon the differences between inbound and outbound use cases, multilingual considerations, and the potential future of hybrid voice agent architectures.

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