Tokenless

Tokenless

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

Interactive discovery

Explore the topic map

Follow the connections between themes, people, and ideas across the Tokenless archive in an interactive topic modeling map.

Open the map Browse posts

Machine Learning

View All
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

View All
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

View All
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

Alibaba's Qwen3.8-Max: Open-Weight Model Surpasses Most American Frontier Labs (Ep. 1018)

Alibaba's Qwen3.8-Max: Open-Weight Model Surpasses Most American Frontier Labs (Ep. 1018)

Jon Krohn dissects Alibaba's Qwen 3.8 Max, a 2.4-trillion-parameter Mixture-of-Experts (MoE) model positioned as the largest open-weight release in history if its promised weights ship. The discussion covers its multimodal capabilities, 1M token context window, and performance competitive with Anthropic's Claude Fable 5. Key highlights include its advanced multi-day agentic capabilities and aggressively low pricing ($2 in / $6 out per million tokens), intensifying the AI price war. Krohn also provides critical insights into the safety of using Chinese models, emphasizing data handling practices and the benefits/risks across different deployment scenarios.

Continual Learning: How AI Agents Get Better With Every Use | Arjun Karanam, Trajectory

Continual Learning: How AI Agents Get Better With Every Use | Arjun Karanam, Trajectory

Arjun Karanam from Trajectory discusses the "experience gap" in AI, where models excel in intelligence but lack real-world experience, advocating for continual learning. He outlines four key areas for the agent ecosystem: robust traceability including corrective actions, evaluations drawn from production traffic, harnesses that orchestrate rather than constrain, and comfort with open-weight models. Trajectory aims to provide a platform for companies to own and continuously improve their AI intelligence.

When to Build Your Own Agent Harness | Harrison Chase, LangChain

When to Build Your Own Agent Harness | Harrison Chase, LangChain

Harrison Chase, co-founder of LangChain, delves into the critical role of the 'harness' in LLM agents, explaining how it orchestrates models and context. He covers customizing harnesses with middleware and sub-agents, the trade-offs between off-the-shelf and custom solutions for in- and out-of-distribution tasks, and the importance of evaluations and observability for continuous agent improvement. The discussion culminates in the 'data flywheel' concept and the `LangSmith Engine` for automating agent intelligence through iterative refinement.

AI & Data Science Periodic Tables: How They Work Together

AI & Data Science Periodic Tables: How They Work Together

Aaron Baughman and Martin Keen present a unified framework using "periodic tables" to integrate AI and Data Science. They illustrate how elements like pipelines, embeddings, and RAG combine to build real-world AI applications, using a detailed document Q&A system example. The discussion emphasizes the critical interdependence of data science in grounding AI models and ensuring continuous improvement through an innovative feedback loop.

Why Old GPUs Keep Gaining Value

Why Old GPUs Keep Gaining Value

Steve Hou, Head of Research at Silicon Data, unpacks the data behind the AI compute market, revealing persistent tightness in GPU rentals and rising residual values, despite talk of oversupply. He discusses the methodologies behind their GPU price indices and forward curves, the evolving hardware landscape beyond Nvidia (including AMD, Cerebras, and TPUs), and trends in LLM token economics. The conversation also delves into the complex financial aspects of AI data center buildouts, the rise of specialized models, China's emerging AI hardware ecosystem, and the growing importance of power constraints and distributed AI infrastructure.

LLM Knowledge Bases: a practical guide — Ben Holmes, Warp

LLM Knowledge Bases: a practical guide — Ben Holmes, Warp

Ben Holmes outlines a personal knowledge management system leveraging LLMs and automation to transform raw voice-dictated notes into an organized, browsable wiki and visual graph. The process emphasizes rapid, "sloppy" capture, followed by AI agents enriching notes with tags, web research, and backlinks, then generating structured wikis based on Karpathy's methodology, all automated via cloud sandboxes and Obsidian's headless CLI.

← PreviousNext →

Stay In The Loop! Subscribe to Our Newsletter.

Get updates straight to your inbox. No spam, just useful content.