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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

Recall.ai: Unlocking the World’s Conversations

Recall.ai: Unlocking the World’s Conversations

Co-founder David shares Recall.ai's journey from a YC hackathon project to the essential data infrastructure for over 1,000 AI companies. He covers their strategic pivot to an API, the fundraising grind, and the lessons learned in building a lean, high-agency team that powers the future of AI with conversation data.

Chris Dixon on How to Build Networks, Movements, and AI-Native Products

Chris Dixon on How to Build Networks, Movements, and AI-Native Products

a16z partners Chris Dixon and Anish Acharya discuss the exponential forces shaping technology, including network effects, composability, and Moore's Law. They explore how these principles apply to the age of AI, covering strategies for founders like 'come for the tool, stay for the network,' the nature of defensibility, investing in movements, the shift to native AI platforms, and the crucial role of open-source AI.

AI Assistance for Software Teams: The State of Play • Birgitta Böckeler • GOTO 2024

AI Assistance for Software Teams: The State of Play • Birgitta Böckeler • GOTO 2024

Birgitta Böckeler from Thoughtworks provides a comprehensive overview of the current AI tooling landscape for software delivery. The talk focuses on the architecture and capabilities of modern coding assistants, differentiating between the underlying model and the crucial role of tooling in providing context. It explores what works today, the promising but challenging frontiers like testing and refactoring, and offers a practical guide to the major tools in the ecosystem.

Karl Friston - Why Intelligence Can't Get Too Large (Goldilocks principle)

Karl Friston - Why Intelligence Can't Get Too Large (Goldilocks principle)

Professor Karl Friston provides a 20-year retrospective on the Free Energy Principle, exploring its implications for life, intelligence, and consciousness. The discussion delves into the nature of agency in "strange things" that can model their own future, the crucial distinction between intelligence and consciousness, the potential for a "Goldilocks zone" for intelligent systems, and the profound challenges of building conscious AI, which may require a move beyond current computer architectures toward "mortal computation".

Vibe hacking, HexStrike AI and the latest scheme from Scattered Lapsus$ Hunters

Vibe hacking, HexStrike AI and the latest scheme from Scattered Lapsus$ Hunters

Experts discuss the weaponization of AI in cybercrime, from "vibe hacking" that lowers the barrier for attackers, to frameworks like HexStrike AI enabling AI agent armies. The conversation covers new extortion tactics and the evolution of malware like Remote Access Trojans (RATs).

Motion: Redefining How Work Gets Done

Motion: Redefining How Work Gets Done

The founders of Motion discuss their journey from lucrative seven-figure trading jobs to building a work management platform. They detail a critical pivot away from a successful $20M ARR consumer product to a B2B suite, and their current strategy of embedding "AI employees" directly into workflows, leveraging proprietary data and a unified human-AI task management system.

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