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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

The Way the Future Was • Kevlin Henney & James Lewis • GOTO 2025

The Way the Future Was • Kevlin Henney & James Lewis • GOTO 2025

Kevlin Henney and James Lewis explore the evolution of our vision of the future, contrasting the boundless optimism of the 20th century with today's more cynical outlook. They analyze the economic and technological forces, such as Jevons' Paradox and the power of connectivity, that have shaped our present reality and offer a hopeful perspective on how a better future could still emerge through gradual, cumulative change.

Building Voice Agents Just Got Easier

Building Voice Agents Just Got Easier

Anoop Dawar from Deepgram discusses the evolution of voice AI, from basic transcription to sophisticated, real-time voice agents. He covers the key technical challenges in production, such as latency and interruption handling, and introduces Deepgram's Flux system. The talk concludes with a look at the future of speech-to-speech models that can understand emotional nuance, moving closer to passing the audio Turing Test.

The AI Data Engineer is Here

The AI Data Engineer is Here

Ciro Greco, CEO of Bauplan, outlines a new paradigm for data engineering that applies software engineering principles like version control, transactionality, and a code-first approach to build a programmable lakehouse designed for AI-driven automation and enhanced developer productivity.

No Priors Ep. 140 | With Benchling Co-Founder and CEO Sajith Wickramasekara

No Priors Ep. 140 | With Benchling Co-Founder and CEO Sajith Wickramasekara

Sajith Wickramasekara, CEO of Benchling, discusses the broken economics of drug development, where high costs and failure rates stifle innovation. He explains how the biotech industry is facing a 'dot com' bust and new competition from China, and argues that AI agents and simulation can provide the solution by making science less artisanal and helping scientists create better molecules through faster, cheaper experiments.

Sim: The Visual, End-to-End Agent Builder

Sim: The Visual, End-to-End Agent Builder

Emir Karabeg, co-founder of Sim, shares the journey of building a visual, end-to-end platform for AI agents. He discusses their pivot from a failed idea, their explosive open-source growth strategy, the philosophy of building an AI-native workflow tool, and the operational culture that took them from a YC batch to 60,000 developers.

Satya Nadella – How Microsoft is preparing for AGI

Satya Nadella – How Microsoft is preparing for AGI

Satya Nadella discusses Microsoft's massive AI infrastructure build-out, the transition to AI-driven business models, and the company's multi-pronged strategy for navigating competition, in-house model development, and the geopolitical landscape of AGI.

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