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Machine Learning

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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

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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

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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

No Priors Ep. 139 | With Snowflake CEO Sridhar Ramaswamy

No Priors Ep. 139 | With Snowflake CEO Sridhar Ramaswamy

Snowflake CEO Sridhar Ramaswamy discusses the company's rapid transformation into an AI-first data platform. He introduces Snowflake Intelligence, an agentic system for enterprise data, and explores the strategic pivot, the ROI of AI for businesses, and the evolving roles of data platforms, partnerships, and search in the age of AI.

Intelligence as "Less is More" - Prof. David Krakauer [SFI]

Intelligence as "Less is More" - Prof. David Krakauer [SFI]

Prof. David Krakauer redefines intelligence not as possessing more knowledge, but as the ability to do more with less. He argues that LLMs are mere 'libraries' and proposes a universal theory where all life is intelligent, operating across strategic, inferential, and representational dimensions, with the latter being key to making hard problems easy.

Building Claude Code: Origin, Story, Product Iterations, & What's Next // Siddharth Bidasaria// #342

Building Claude Code: Origin, Story, Product Iterations, & What's Next // Siddharth Bidasaria// #342

Siddharth Bidasaria from Anthropic shares the origin story of Claude Code, from a simple internal terminal app to a powerful coding agent. He discusses the team's core philosophy of 'letting the model cook,' the evolution of agentic capabilities, the critical role of verification and testing, and the future of complex, multi-agent systems.

Seeing The Future from AI Companions to Personal Software

Seeing The Future from AI Companions to Personal Software

Eugenia Kuyda, CEO of Wabi and founder of Replika, explains her vision for the future of personal software, where AI transitions from command-line interfaces to a creative, user-generated medium. She details how ephemeral, deeply personalized mini-apps will replace many traditional applications, fostering a new creator economy and transforming our interaction with technology.

GenAI Grows Up: Building Production-Ready Agents on the JVM • Rod Johnson • GOTO 2025

GenAI Grows Up: Building Production-Ready Agents on the JVM • Rod Johnson • GOTO 2025

Rod Johnson explains the high failure rate of enterprise GenAI projects, attributing it to the misuse of the technology and a disconnect from established software engineering principles. He argues for a paradigm shift away from Python-centric approaches towards the JVM, introducing Embabel, a framework designed to build reliable, testable, and domain-integrated AI agents by tackling non-determinism and leveraging the strengths of the enterprise Java ecosystem.

What Engineers Get Wrong About Liquid Cooling - Wendy Luiten | Podcast #163

What Engineers Get Wrong About Liquid Cooling - Wendy Luiten | Podcast #163

Thermal engineer and 2024 Thermy Award winner Wendy Luiten discusses the impending energy and water crisis driven by AI data centers. She explores how computational fluid dynamics (CFD) and a shift to sustainable liquid immersion cooling, particularly with plant-based oils, can mitigate the environmental impact while ensuring performance.

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