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Make Your AI Agents Predictable With Graph Engineering - Zachary Blumenfeld, Neo4j

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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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Make Your AI Agents Predictable With Graph Engineering - Zachary Blumenfeld, Neo4j

Make Your AI Agents Predictable With Graph Engineering - Zachary Blumenfeld, Neo4j

A walkthrough of graph engineering, the practice of expressing agent logic as an explicit graph of nodes and routed state. It covers execution, context, and procedural graphs, how skills and decision traces can be compiled into portable graph-shaped skills, and a loan-approval demo showing gains in predictability, governance, and cost.

Your AI Agents Shouldn't Care Where They Run - Nikhil Harithas, Factory

Your AI Agents Shouldn't Care Where They Run - Nikhil Harithas, Factory

Factory's Nikhil Harithas explains how Droid, their coding agent, was rebuilt to run on any compute inside a customer's own cloud. The talk covers the Open Compute abstraction, the compute gateway and outbound-only relay design, bring-your-own-key model calls, snapshots and forking for agent swarms, and the operational hard parts of managing compute for long-running agents in regulated enterprises.

Better Search Took This AI Agent From 36% to 98% Accuracy - Aamir Shakir, Mixedbread

Better Search Took This AI Agent From 36% to 98% Accuracy - Aamir Shakir, Mixedbread

Aamir Shakir of Mixedbread argues that the main limit on agent performance is access to the right data, not model intelligence. He explains why OCR, chunking, single-vector embeddings, and vector databases lose information at each step, and presents a late-interaction, multimodal retrieval approach that embeds document pages visually, with a live demo on Intel earnings reports and a hedge fund case study that raised answer accuracy from 36% to 98%.

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

A Playground for AI Engineers

A Playground for AI Engineers

Paulo Vasconcellos from Hotmart details their journey of building "Agent as a Product", explaining how they blend classic ML models with LLMs for efficiency, evolve their MLOps platform for the generative AI era, and create real business value through AI-powered tutors and sales agents.

SW Design, Architecture & Clarity at Scale • Sam Newman, Jacqui Read & Simon Rohrer

SW Design, Architecture & Clarity at Scale • Sam Newman, Jacqui Read & Simon Rohrer

Experts Sam Newman, Jacqui Read, and Simon Rohrer explore the nuances of software design, its intersection with architecture, and the critical role of communication in scaling technical clarity. The discussion covers practical advice on implementing Architectural Decision Records (ADRs), the evolving role of the architect as a facilitator, and strategies for creating agile enterprise architectures.

Mainframe modernization explained: COBOL and AI

Mainframe modernization explained: COBOL and AI

Experts from IBM discuss the nuanced role of AI in mainframe modernization, the immense infrastructural and product challenges behind global AI adoption, and the critical need for a multi-layered, security-by-design framework for the safe deployment of AI agents.

Dylan Patel Explains the AI War While Cooking | In-Context Cooking

Dylan Patel Explains the AI War While Cooking | In-Context Cooking

Dylan Patel of SemiAnalysis discusses the AI arms race, highlighting the massive $200B+ hyperscaler capex, the true semiconductor bottlenecks shifting from data centers back to fabs, the geopolitical chess game surrounding Taiwan and TSMC, and Nvidia's strategic battle against vertical integration.

Hardware Realization and Implementation Security Evaluation of HQC, A NIST PQC Standard

Hardware Realization and Implementation Security Evaluation of HQC, A NIST PQC Standard

This talk by Sanjay Deshpande from Northwestern University explores the critical transition to Post-Quantum Cryptography (PQC) in response to the threat quantum computers pose to current public-key algorithms. It provides a deep dive into the Hamming Quasi-Cyclic (HQC) algorithm, a code-based candidate for NIST standardization. The session focuses on the challenges and innovations in creating efficient and secure hardware implementations of HQC, covering performance optimization for polynomial multiplication and countermeasures against side-channel attacks.

GeoMind: A Multi-Agent Framework for Geospatial Decision Support

GeoMind: A Multi-Agent Framework for Geospatial Decision Support

GeoMind is a multi-agent framework designed to empower non-technical users, such as disaster responders, to perform complex geospatial analysis using natural language. It bridges the gap between Large Language Models and advanced GIS workflows by employing a team of specialized AI agents that can query, join, and analyze multi-layered vector and raster data to provide timely, actionable insights during emergencies.

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