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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 Brutal Truth About Biotech: Why $2B Per Drug Is Killing Innovation

The Brutal Truth About Biotech: Why $2B Per Drug Is Killing Innovation

Venture capitalists Elliot Hershberg and Lada Nuzhna analyze the structural crises facing the biotech industry, from Eroom's Law driving drug development costs to $2.5 billion, to China's rapid, low-cost clinical trials outmaneuvering US startups. They argue that for American biotech to survive, it must shift from competing on commoditized platforms to inventing entirely new modalities and medicines that are impossible to create without bleeding-edge tools like AI, targeting massive opportunities like aging.

AI Is Eating Logistics

AI Is Eating Logistics

Ryan Petersen, founder and CEO of Flexport, explains how AI and Machine Learning are being implemented to revolutionize the multi-trillion-dollar logistics industry. He details specific applications, from ML models that optimize container routing to LLM agents that automate communication, and discusses the cultural and strategic shifts required for a large company to embrace AI-driven, bottom-up innovation.

From Hardware Hacker to CTO: Building Teams That Scale • Meri Williams & Charles Humble

From Hardware Hacker to CTO: Building Teams That Scale • Meri Williams & Charles Humble

Experienced CTO Meri Williams shares hard-won insights on scaling technology teams, moving from a reluctant manager to a seasoned leader. The discussion covers practical wisdom on avoiding the 'Google trap' of inappropriately copying big tech, the high-leverage impact of investing in onboarding, and the critical importance of diversity in building products that serve everyone.

GPT-5.1 and Kimi K2: What ‘Thinking AI’ really means

GPT-5.1 and Kimi K2: What ‘Thinking AI’ really means

The experts discuss OpenAI's GPT-5.1, focusing on its shift towards conversational style and user experience over raw benchmarks. They contrast this with the release of Moonshot AI's Kimi K2 Thinking, a powerful open-source model that challenges the dominance of proprietary systems. Finally, they analyze the profound security, organizational, and cultural risks of Microsoft's plan to introduce 'agentic users' as autonomous employees in the enterprise.

ChatGPT Atlas and the next era of web browsing — the OpenAI Podcast Ep. 9

ChatGPT Atlas and the next era of web browsing — the OpenAI Podcast Ep. 9

OpenAI’s Ben Goodger and Darin Fisher discuss the creation of ChatGPT Atlas, an AI-native browser designed to transform the web experience. They explore how integrating ChatGPT at the core enables agentic capabilities, allowing the browser to take actions on the user's behalf, from summarizing content to automating complex tasks. The conversation covers the architectural decisions behind Atlas, the future of agentic internet traffic, and the vision for a more intuitive, natural language-driven way of interacting with computers.

Inside The Startup Launching AI Data Centers Into Space

Inside The Startup Launching AI Data Centers Into Space

Starcloud launched the first NVIDIA H100 GPU into orbit, a first step towards building massive AI data centers in space. Their vision is to leverage continuous solar power and zero-water cooling to overcome Earth's energy and resource limitations for large-scale compute.

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