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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

Brian Chesky's secret mentor who scaled Airbnb (after dying 9 times & building a hotel empire)

Brian Chesky's secret mentor who scaled Airbnb (after dying 9 times & building a hotel empire)

Chip Conley, the founder of Joie de Vivre hotels and former Head of Global Hospitality and Strategy at Airbnb, shares his insights on intergenerational collaboration, thriving in tech as an older professional, and how human wisdom will become more valuable in the age of AI. He discusses his experience working with Brian Chesky, tactics for navigating "founder mode," and the frameworks he developed for building culture and finding meaning, which led him to create the Modern Elder Academy.

Practical tactics to build reliable AI apps — Dmitry Kuchin, Multinear

Practical tactics to build reliable AI apps — Dmitry Kuchin, Multinear

Moving an AI PoC from 50% to 100% reliability requires a new development paradigm. This talk introduces a practical, evaluations-first approach, reverse-engineering tests from real-world user scenarios and business outcomes to build a robust benchmark, prevent regressions, and enable confident optimization.

Real World Development with GitHub Copilot and VS Code — Harald Kirschner, Christopher Harrison

Real World Development with GitHub Copilot and VS Code — Harald Kirschner, Christopher Harrison

A deep dive into "Vibe Coding," a development methodology that prioritizes outcomes over code-level details, using the advanced AI features of VS Code and GitHub Copilot. The talk explores three stages of this methodology—YOLO, Structured, and Spectrum—and demonstrates how to leverage agent modes, custom instructions, reusable prompts, and the Model Copilot Protocol (MCP) to enhance productivity from rapid prototyping to enterprise-scale development.

Building Agents at Cloud Scale — Antje Barth, AWS

Building Agents at Cloud Scale — Antje Barth, AWS

A deep dive into building and scaling production-ready AI agents, detailing a model-driven approach using the open-source 'Strands' SDK and a cloud-native architecture for deploying remote tools with MCP and AWS Lambda.

State of Startups and AI 2025 - Sarah Guo, Conviction

State of Startups and AI 2025 - Sarah Guo, Conviction

Sarah Goa from the AI-native venture fund Conviction discusses the state of AI, arguing that despite the hype, building valuable AI products is challenging. She covers the rapid advancement in AI capabilities like reasoning and multimodality, the increasingly competitive model market, and provides a playbook for building successful applications, using "Cursor for X" as a framework. The key takeaway is that defensibility in AI comes from superior execution and deep workflow integration, not just the underlying model.

Useful General Intelligence — Danielle Perszyk, Amazon AGI

Useful General Intelligence — Danielle Perszyk, Amazon AGI

Danielle from the Amazon AGI SF Lab introduces a new paradigm for agent development called "Useful General Intelligence" (UGI). Instead of focusing on making AI smarter than humans, UGI aims to build AI that augments human intelligence and agency. This talk explores the cognitive science principles behind this vision and introduces Nova Act, an agentic model and SDK designed to reliably interact with computer UIs, turning the browser into a programmable tool.

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