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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

Infrastructure as Code • Kief Morris & Abby Bangser

Infrastructure as Code • Kief Morris & Abby Bangser

Kief Morris, author of 'Infrastructure as Code', and Abby Bangser discuss the evolution of IaC over the past decade. They explore the move from server configuration to complex cloud architectures, the limitations of current tooling, and the need for higher-level abstractions, while also looking ahead to the potential impact of AI and the critical role of platform engineering in connecting infrastructure to specific business needs.

OpenAI, Oracle & AMD shake up AI

OpenAI, Oracle & AMD shake up AI

The panel discusses the shifting AI hardware landscape as Oracle and OpenAI bet on AMD, challenging Nvidia's dominance. They also analyze a US government report on the risks of the DeepSeek model, debate the viability of Reflection AI's new $2B open-source venture, and dissect the story of a VC fund replacing analysts with AI agents.

Part 2: Social engineering, malware, and the future of cybersecurity in AI

Part 2: Social engineering, malware, and the future of cybersecurity in AI

A deep dive into the human side of cybersecurity, exploring the motivations of bad actors, the evolution of social engineering in the age of AI, and the defensive strategies being developed. The discussion covers the move beyond passwords with passkeys and risk-based authentication, and confronts the complex security and privacy challenges introduced by autonomous agents.

Emergent: The AI App Builder for Everyone

Emergent: The AI App Builder for Everyone

Mukhund and Madhav Jha, co-founders of Emergent, detail their journey of building an AI-powered app builder that reached $15M ARR in three months. They discuss their pivot from enterprise agents, the multi-agent architecture that enables production-ready apps, and their vision for a future with a 'billion builders'.

Keith Rabois: Israel, OpenAI, Opendoor, and DOGE

Keith Rabois: Israel, OpenAI, Opendoor, and DOGE

Keith Rabois predicts a new American economic boom driven by AI, forecasting 4-6% GDP growth without inflation. He discusses the rise of sovereign AI, OpenAI's dominance as a multi-trillion dollar company, Google's existential threat, and the strategic challenges facing Microsoft, Meta, and Apple in the new AI paradigm.

Biometrics & Security: Privacy, Deepfakes, & Cyber Threats

Biometrics & Security: Privacy, Deepfakes, & Cyber Threats

An in-depth analysis of biometric authentication, exploring how the technology works, its applications in security, and the significant privacy risks involved. The summary covers biometric templates, threats like deepfakes, and mitigation strategies such as cancellable biometrics and multi-factor authentication.

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