Tokenless

Tokenless

Stop Making Your Customers Click Approve - Emil Chetty, Wise

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
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
Bringing Continual Learning to Every Company: Trajectory’s Ronak Malde

Bringing Continual Learning to Every Company: Trajectory’s Ronak Malde

Ronak Malde, co-founder of Trajectory, argues that agents need to learn continuously from real-world experience instead of relying on offline batch updates. He covers how training custom models has become accessible to companies, two research directions (Group Size-1 GRPO and a meta-grader for reward propagation), the Harvey and Rogo results, the distinction between continual learning and recursive self-improvement, and a vision of product builders as gardeners who shape products that grow on their own.

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.

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

Why ChatGPT will be the next big growth channel (and how to capitalize on it) | Brian Balfour

Why ChatGPT will be the next big growth channel (and how to capitalize on it) | Brian Balfour

Brian Balfour, founder of Reforge, explains the predictable four-step cycle of technology platforms, from open to closed, and argues that ChatGPT is about to enter the "open" phase, creating a massive, time-sensitive distribution opportunity that startups and incumbents alike cannot afford to ignore.

Google DeepMind Lead Researchers on Genie 3 & the Future of World-Building

Google DeepMind Lead Researchers on Genie 3 & the Future of World-Building

Google DeepMind researchers Jack Parker-Holder and Shlomi Fruchter detail the creation of Genie 3, a model that generates interactive, persistent worlds from text in real time. They cover its breakthrough spatial memory, emergent physical intuition, and its potential to revolutionize gaming, robotics, and AI agent training.

Identity Fabric: How AI Enhances IAM & Modern Cybersecurity

Identity Fabric: How AI Enhances IAM & Modern Cybersecurity

Bob Kalka from IBM and Tyler Lynch from HashiCorp discuss the critical disconnect between managing human and non-human identities, which contributes to 80% of all cyberattacks. They introduce the concept of an 'Identity Fabric'—an AI-augmented approach to unify existing tools—and outline six key use cases and a three-phase strategy to address modern identity and access management (IAM) challenges.

When AI Eats the Bottom Rung of the Career Ladder

When AI Eats the Bottom Rung of the Career Ladder

Ben Lorica and Evangelos Simoudis analyze three pivotal AI trends: the "Great Hollowing Out" of entry-level jobs, the financial disconnect between AI hardware depreciation and its useful life, and OpenAI's strategic shift to router-based models in the quest for a sustainable business model.

Interpretability: Understanding how AI models think

Interpretability: Understanding how AI models think

Members of Anthropic's interpretability team discuss their research into the inner workings of large language models. They explore the analogy of studying AI as a biological system, the surprising discovery of internal "features" or concepts, and why this research is critical for understanding model behavior like hallucinations, sycophancy, and long-term planning, ultimately aiming to ensure AI safety.

AGI progress, surprising breakthroughs, and the road ahead — the OpenAI Podcast Ep. 5

AGI progress, surprising breakthroughs, and the road ahead — the OpenAI Podcast Ep. 5

OpenAI's Chief Scientist Jakub Pachocki and researcher Szymon Sidor discuss the rapid progress towards AGI, focusing on the shift from traditional benchmarks to real-world capabilities like automating scientific discovery. They share insights into recent breakthroughs in mathematical and programmatic reasoning, highlighted by successes in competitions like the International Math Olympiad (IMO), and explore what's next for scaling and long-horizon problem-solving.

← PreviousNext →

Stay In The Loop! Subscribe to Our Newsletter.

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