Machine learning engineering

Oct 13, 2026

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.

Sep 19, 2026

Weight Folding, CUDA Streams, and the Bug That Made My Model Speak Backwards — Filip Makraduli

This talk introduces FlashNorm, an optimization for transformer RMS norm layers. It details how algebraic tricks like weight folding, deferred division, and dropped pre-normalization can significantly speed up inference by mitigating GPU inefficiencies. The speaker also shares critical insights from implementing parallel CUDA streams, including debugging a subtle race condition caused by implicit stream joins, and advocates for open inference engines for deploying kernel-level research.

Aug 13, 2026

When to Build Your Own Agent Harness | Harrison Chase, LangChain

Harrison Chase, co-founder of LangChain, delves into the critical role of the 'harness' in LLM agents, explaining how it orchestrates models and context. He covers customizing harnesses with middleware and sub-agents, the trade-offs between off-the-shelf and custom solutions for in- and out-of-distribution tasks, and the importance of evaluations and observability for continuous agent improvement. The discussion culminates in the 'data flywheel' concept and the `LangSmith Engine` for automating agent intelligence through iterative refinement.

Jul 30, 2026

First Steps Toward Automated AI Research — Richard Socher, CEO Recursive AI

Richard Socher introduces the "Eureka machine," a concept for automating scientific research and invention through AI. Inspired by open-ended evolution and Karl Popper's philosophy of science, he argues that AI can compress the timeline of scientific discovery, overcoming human-centric bottlenecks. The machine relies on four pillars (knowledge, data, simulations, physical labs) orchestrated by an agent swarm, requiring a rethinking of existing infrastructure. Recursive Self-Improvement (RSI), where AI improves its own code and addresses its shortcomings, is presented as the path forward, with early proof points in model optimization, training speed, and GPU kernel efficiency.

Jul 22, 2026

Your Moat Is Your Data Model — Mike Phipps, Gates Foundation

Mike Phipps from the Gates Foundation details how they built a Strategic Intelligence Platform (SIP) using a Neo4j knowledge graph to serve AI agents. He argues that the true "moat" in an AI-commoditized world lies in an organization's unique data model and tacit knowledge, not in generic AI tools. The platform unifies 25 years of siloed grantmaking data, integrating structured and unstructured information through a rigorous curation pipeline, and is refined via continuous retrieval evaluations to ensure alignment with organizational reporting standards.

Jul 22, 2026

Active Graph Agent Runtime (BabyAGI 4) — Yohei Nakajima, Untapped Capital

Yohei Nakajima, creator of BabyAGI, introduces ActiveGraph, a novel agent architecture that builds around an immutable event log rather than the LLM. This log-centric approach enables auditable agents with native replay, rollback, and self-improvement capabilities, drawing inspiration from blackboard systems and Kafka.