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

Building Bridges: From Developer to Developer Advocate • David Edoh-Bedi & James Beswick

Building Bridges: From Developer to Developer Advocate • David Edoh-Bedi & James Beswick

David Edoh-Bedi, a Developer Advocate at Stripe, shares his journey from growing up in Togo to working on large-scale systems like Windows at Microsoft and eventually transitioning into developer relations. The conversation covers the essential skills for DevRel, the hidden complexities of global payment systems, and the evolution of software development from libraries to API-centric architectures.

Self-Driving Storage: AI Agent Automation for Data Infrastructure

Self-Driving Storage: AI Agent Automation for Data Infrastructure

Explore the concept of "self-driving storage," where AI and AIOps autonomously manage data infrastructure. Learn how mobile storage partitions, predictive analytics, and agentic AI are used to automate capacity management, workload placement, and on-demand performance optimization without human intervention.

915: How to Jailbreak LLMs (and How to Prevent It) — with Michelle Yi

915: How to Jailbreak LLMs (and How to Prevent It) — with Michelle Yi

Tech leader and investor Michelle Yi discusses the critical technical aspects of building trustworthy AI systems. She delves into adversarial attack and defense mechanisms, including red teaming, data poisoning, prompt stealing, and "slop squatting," and explores how advanced concepts like Constitutional AI and World Models can create safer, more reliable AI.

Dylan Patel on GPT-5’s Router Moment, GPUs vs TPUs, Monetization

Dylan Patel on GPT-5’s Router Moment, GPUs vs TPUs, Monetization

A deep dive into the AI hardware landscape, exploring NVIDIA's dominance, the challenges for competitors like custom silicon and startups, and the critical infrastructure bottlenecks of power and data centers that define the next phase of the AI race.

Lets See What We Can do! with F# Computation Expressions • Andrew Browne • YOW! 2015

Lets See What We Can do! with F# Computation Expressions • Andrew Browne • YOW! 2015

Andrew Browne demystifies F# Computation Expressions, showing how this powerful feature is a simple syntactic transformation. He demonstrates building custom expressions for handling optional values (maybe), asynchronous sequences, and even creating a Domain-Specific Language (DSL) with Free Monads for robust testing.

Knowledge is Eventually Consistent // Devin Stein // MLOps Podcast #335

Knowledge is Eventually Consistent // Devin Stein // MLOps Podcast #335

Devin Stein, CEO of Dosu, discusses a new paradigm for knowledge management where an AI agent learns from code, conversations, and tickets to create an 'eventually consistent' knowledge base. The conversation explores the lifecycle of facts, the challenges of agent interaction, and the future of documentation in a world of collaborating AI agents.

← PreviousNext →

Stay In The Loop! Subscribe to Our Newsletter.

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