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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

Inside the $41B AI Cloud Challenging Big Tech | CoreWeave SVP

Inside the $41B AI Cloud Challenging Big Tech | CoreWeave SVP

Corey Sanders, SVP of Product at CoreWeave, explains why the unique, high-stakes demands of AI workloads are driving a shift away from general-purpose clouds toward specialized "Neo Clouds." He details the specific hardware and software innovations in storage, cooling, and networking that allow CoreWeave to maximize GPU utilization and deliver superior performance, arguing that this focused approach creates a durable competitive advantage.

Building Software That Survives • Michael Nygard & Charles Humble

Building Software That Survives • Michael Nygard & Charles Humble

Michael Nygard, author of 'Release It!' and Chief Architect at Nubank, explores the complexities of technical leadership at scale. He discusses the nuanced relationship between centralization and autonomy, the practical implications of Conway's Law on software and communication structures, and how well-defined architectural boundaries can reduce the need for constant organizational alignment. Drawing on his experiences at Sabre and Nubank, Nygard provides insights into building resilient systems and effective, autonomous teams.

Accelerate Demand Gen with GenAI + SEO | Julian Goldie | Founder & CEO | Goldie Agency | 4K

Accelerate Demand Gen with GenAI + SEO | Julian Goldie | Founder & CEO | Goldie Agency | 4K

Julian Goldie, CEO of Goldie Agency, shares his journey from fearing AI's impact on his SEO business to leveraging it to "100X" his operations. He discusses the paradigm shift from traditional SEO to Generative Engine Optimization (GEO), where the goal is to train LLMs to recognize your brand's authority. Julian provides practical strategies for using AI to automate content creation, marketing, and customer service, highlighting how tools like Claude Opus, HeyGen, and Google AI Studio can outperform human efforts and enable solopreneurs to build highly profitable, scalable businesses.

AI year in review: Trends shaping 2026

AI year in review: Trends shaping 2026

In this special year-end episode, experts from the Mixture of Experts podcast review the biggest AI moments of 2025 and predict what's next for 2026. The discussion covers the rise of "super agents" and the battle for the user interface, open source's breakout year and its remaining challenges, the AI hardware supply crisis and the push for efficiency, and the future of modular, multimodal AI systems.

The Algorithm That IS The Scientific Method [Dr. Jeff Beck]

The Algorithm That IS The Scientific Method [Dr. Jeff Beck]

Dr. Jeff Beck argues that the future of AI lies not in scaling up large language models, but in building systems that mirror the brain's approach to understanding the world. He posits that true intelligence is grounded in physics and object-centered models, not language. Beck's vision involves creating AI composed of numerous small, modular models—much like a video game engine—that can be dynamically combined, updated through continual learning, and understand the world through causal relationships and forces. This approach, he claims, will solve key challenges in generalization, robotics, and alignment by enabling machines to 'know what they don't know' and reason about the physical world in a way that is fundamentally similar to humans.

Fundamentals of DevOps & Software Delivery • Yevgeniy "Jim" Brikman & Kief Morris

Fundamentals of DevOps & Software Delivery • Yevgeniy "Jim" Brikman & Kief Morris

Yevgeniy "Jim" Brikman, author of "Fundamentals of DevOps and Software Delivery," discusses his journey from app developer to DevOps advocate, triggered by LinkedIn's deployment crisis. The discussion with Kief Morris explores the practical definition of DevOps, the relationship between infrastructure as code and application orchestration, the necessity of frameworks over custom wrapper scripts, and emerging paradigms including infrastructure from code, infrastructure as graph models, and interactive runbooks.

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