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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

He saved OpenAI, invented the “Like” button, and built Google Maps: Bret Taylor (Sierra)

He saved OpenAI, invented the “Like” button, and built Google Maps: Bret Taylor (Sierra)

Bret Taylor discusses the AI market's shift to autonomous agents and outcome-based pricing, the future of coding with AI, and strategic advice on GTM, pricing, and where to build in the new AI landscape. He shares career-defining lessons from Google, Facebook, and Salesforce.

No Priors Ep. 125 | With Senior White House Policy Advisor on AI Sriram Krishnan

No Priors Ep. 125 | With Senior White House Policy Advisor on AI Sriram Krishnan

Sriram Krishnan, Senior White House Policy Advisor on AI, outlines the America AI Action Plan, a strategy designed to ensure U.S. dominance in artificial intelligence. He discusses the three core pillars of the plan—infrastructure, innovation, and global standards—while also exploring the geopolitical race with China, the critical role of open-source models, and the need for America to own the full AI stack, from GPUs to applications.

The Unofficial Guide to Apple’s Private Cloud Compute - Jonathan Mortensen, CONFSEC

The Unofficial Guide to Apple’s Private Cloud Compute - Jonathan Mortensen, CONFSEC

A technical deep dive into Apple's Private Cloud Compute (PCC), exploring its novel architecture for running sensitive AI workloads with cryptographic privacy guarantees. The talk covers the core requirements, key components like remote attestation and transparency logs, and how these concepts can be applied by developers today.

How we hacked YC Spring 2025 batch’s AI agents — Rene Brandel, Casco

How we hacked YC Spring 2025 batch’s AI agents — Rene Brandel, Casco

A security analysis of YC AI agents reveals that the most critical vulnerabilities are not in the LLM itself, but in the surrounding infrastructure. This breakdown of a red teaming exercise, where 7 out of 16 agents were compromised, highlights three common and severe security flaws: cross-user data access (IDOR), remote code execution via insecure sandboxes, and server-side request forgery (SSRF).

Leah Belsky on how AI is transforming education — the OpenAI Podcast Ep. 4

Leah Belsky on how AI is transforming education — the OpenAI Podcast Ep. 4

OpenAI's Head of Education, Leah Belsky, and students Yabsera and Alaap discuss how AI, particularly ChatGPT and its new Study Mode, is transforming education. They cover global adoption, the shift from policing AI to integrating it, its role as a personal tutor for building confidence and skills, and the evolving nature of learning, work, and critical thinking in the age of AI.

Safety and security for code executing agents — Fouad Matin, OpenAI (Codex, Agent Robustness)

Safety and security for code executing agents — Fouad Matin, OpenAI (Codex, Agent Robustness)

Fouad Matin from OpenAI's Agent Robustness and Control team discusses the critical safety and security challenges of code-executing AI agents. He explores the shift from models that *can* execute code to defining what they *should* be allowed to do, presenting practical safeguards like sandboxing, network control, and human review, drawing from OpenAI's experience building Code Interpreter and the open-source Code Interpreter CLI.

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