Tokenless

Interactive discovery

Explore the topic map

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

Machine Learning

View All
Judge a Transaction by the Company It Keeps — Somya Rai & MD Sharique, EXL

Judge a Transaction by the Company It Keeps — Somya Rai & MD Sharique, EXL

EXL developed an advanced fraud detection system that augments XGBoost with Graph Neural Network (GNN) embeddings. Traditional tabular models struggle to capture fraud signals residing in transaction neighborhoods. The system's innovative design includes meticulous sampling strategies, disjoint monthly graph construction for leakage control, and a GraphSAGE architecture that aggregates mean, max, and standard deviation of neighbor features to detect subtle fraud patterns. Emphasizing explainability for regulatory compliance and analyst trust, it also features a highly optimized serving architecture achieving 3ms P99 latency, pivoting to edge prediction to reduce cache footprint from 60GB to 2-3GB. This led to a 27-33% relative lift in true positive rates and a projected $65 million incremental fraud capture.

From 15% to 90% GPU Utilization: Fix the Data Pipeline, Not the Model

From 15% to 90% GPU Utilization: Fix the Data Pipeline, Not the Model

Tarun Sunkaraneni from Amazon AGI shows how a multimodal SFT pipeline that spent about 85% of its time waiting on data was brought to roughly 90% GPU utilization. He walks through concurrency with asyncio and Ray actors, prefetching, zero-copy transfer via Ray's object store, and a network-card bottleneck that appears only at scale, and argues that the hardest part of training throughput is often keeping GPUs fed rather than optimizing the model.

Small Models, Big Results: Training a Finance Agent for Under $500 — Charles Dickens, Snorkel AI

Small Models, Big Results: Training a Finance Agent for Under $500 — Charles Dickens, Snorkel AI

Snorkel AI and UC Berkeley's Sky Computing Lab show that a 4B Qwen3 model, trained with reinforcement learning for under $500 using the rLLM framework, beats its 235B sibling on expert-validated financial question answering built from SEC 10-K filings. The talk covers the FinQA dataset construction and verification, common failure modes of frontier models, transfer to multi-table and general tool-use tasks, and ablations showing that simple data and binary rewards outperform more elaborate setups, since the real bottleneck is disciplined tool use rather than reasoning depth.

Artificial Intelligence

View All
Why Waiting for the Next AI Model Can Pay Off - John Stecher, Blackstone & Scott Worland, Norm AI

Why Waiting for the Next AI Model Can Pay Off - John Stecher, Blackstone & Scott Worland, Norm AI

Blackstone CTO John Stecher and Norm AI CTO Scott Worland discuss how to build AI agent systems that remain useful as models improve. They cover why data strategy and straight-through processing come first, how to weigh ROI against waiting for a better model, how evals anchor financial-services work, and why Norm rebuilt its platform after a large hand-labeled eval set exposed the cost of over-engineered code.

How We Took an AI Agent From 41% to 97% Accurate - Milind Sunil Shah, Brainbase Labs

How We Took an AI Agent From 41% to 97% Accurate - Milind Sunil Shah, Brainbase Labs

Milind Sunil Shah of Brainbase Labs presents a six-step framework for improving back-office agents, illustrated with a Domino's project that tracks competitor pizza prices across websites and delivery apps. Accuracy rose from 41% to 97%, cost per source fell from $2 to $1, and run time dropped from 129 to 71 minutes, all through instructions, skills, and playbooks rather than a new model. The talk covers kickoff, ontology, benchmarks and evals, a narrow first agent, iteration, and handing off the full loop.

Stop Making Your Customers Click Approve - Emil Chetty, Wise

Stop Making Your Customers Click Approve - Emil Chetty, Wise

Emil Chetty of Wise explains how the company lets AI agents handle customer payments without turning customers into approval queues. The core idea is a locked customer agreement covering the whole job, agent-chosen execution steps, and existing payment software that enforces the terms, even when an agent is manipulated by a prompt injection.

Technology

View All
What iPhone Packaging Reveals About the Robotics Opportunity

What iPhone Packaging Reveals About the Robotics Opportunity

Tutor Intelligence CEO Josh Gruenstein explains why robotics is a systems problem spanning AI, hardware, manufacturing, and customers, and how deploying robots into real factories and warehouses, with human tutors as backup, generates the data that makes them more capable. The discussion covers the company's own robot designs, its focus on small and mid-sized businesses, the economics of flexible robotic labor, and the implications for American manufacturing and supply chains.

Why Founders Are Betting Big on Factories, Mines, and Energy

Why Founders Are Betting Big on Factories, Mines, and Energy

Erin Price-Wright and Connor Love unpack the AD50 and the rebuilding of America's industrial base, covering how defense demand is financing manufacturing capacity for commercial markets, how AI is reshaping factory economics, how a new generation of founders applies lessons from SpaceX, Tesla, Anduril, and Palantir to mining, energy, and manufacturing, and why private capital is now willing to fund physical infrastructure before government demand is guaranteed.

Mark Cuban’s Plan to Make Healthcare Affordable

Mark Cuban’s Plan to Make Healthcare Affordable

Mark Cuban and Julie Yoo discuss how transparency, direct contracting, and AI could reshape healthcare economics, from Cost Plus Drugs and modular pod manufacturing to personalized medicine, the future of physicians, and advice for founders who want to use AI to learn rather than to stop thinking.


Recent Post

Most cybersecurity training doesn’t work. Can we change that?

Most cybersecurity training doesn’t work. Can we change that?

In an era where AI accelerates the speed, scale, and polish of cyberattacks like phishing and deepfakes, the human element remains the most critical variable. This discussion explores why traditional 'checkbox' training fails and how immersive, stress-inducing simulations like the cyber range are essential for building the muscle memory, confidence, and decision-making skills needed for effective incident response.

"We Made a Dream Machine That Runs on Your Gaming PC"

"We Made a Dream Machine That Runs on Your Gaming PC"

Shahbuland Matiana and Andrew Lapp from Overworld Labs introduce Waypoint 1, a 2 billion-parameter open-source world simulation model designed to run on consumer hardware at 60 FPS. They discuss its novel architecture, which combines a causal language model with an image diffusion model to denoise frames in real-time based on user prompts and controller inputs, emphasizing low-latency interaction and the importance of local execution for user privacy.

What a $42B Software Co. Really Spends on AI Tools | Mike Cannon-Brookes

What a $42B Software Co. Really Spends on AI Tools | Mike Cannon-Brookes

Atlassian Co-Founder & CEO Mike Cannon-Brookes shares insights from a massive internal study of over 10,000 engineers using AI coding tools. He discusses the true measures of developer productivity, the future of developer roles, and why human-AI collaboration, powered by organizational context, is the key to the future.

AI & Education: Generative AI & the Future of Critical Thinking

AI & Education: Generative AI & the Future of Critical Thinking

The author argues for embracing generative AI in education, shifting the focus from outdated skills like memorization to future-ready competencies such as critical thinking, adaptability, and creativity. The summary explores the pros and cons, practical applications like personalized tutoring and enhanced accessibility, and the necessity of teaching AI literacy and ethics to prepare students for the modern workplace.

Why The AI Era Is Unlike Any Technology Shift Before

Why The AI Era Is Unlike Any Technology Shift Before

The a16z AI Apps team outlines their investment framework for the current AI application cycle, which they see as the fastest product shift in software history. They detail three core themes for building defensible businesses: AI-native software for greenfield markets, applications that replace labor by owning entire workflows, and "walled garden" businesses built on proprietary data moats.

State of the AI Industry — the OpenAI Podcast Ep. 12

State of the AI Industry — the OpenAI Podcast Ep. 12

OpenAI CFO Sarah Friar and investor Vinod Khosla discuss the immense demand for AI compute, debunking the 'bubble' narrative by highlighting real-world productivity gains. They explore the future of agents, AI in healthcare, enterprise adoption, and the transformative potential of robotics on the global economy.

Stay In The Loop! Subscribe to Our Newsletter.

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