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Agents That Self Improve While You Sleep - Fuad Ali, Arize AI

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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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How a Trading Firm Ships New AI Models the Same Day - Noyan Tokgozoglu, Optiver

How a Trading Firm Ships New AI Models the Same Day - Noyan Tokgozoglu, Optiver

A look at how Optiver, a proprietary trading firm, runs an on-prem AI platform where every model, agent, and harness decision has to show results in P&L within hours. Topics include the inference gateway, same-day adoption of frontier models, the meta harness design, and efforts to bring AI spend back under control.

Agents That Self Improve While You Sleep - Fuad Ali, Arize AI

Agents That Self Improve While You Sleep - Fuad Ali, Arize AI

A workshop on building a self-improvement loop for AI agents: tracing production traces, using an always-on agent to file deduplicated issues, turning failures into evals and regression datasets, proving fixes with experiments, and keeping humans at the highest-leverage review points.

How to Build Voice Agents That Don't Fall Apart When You Interrupt - Jesse Hall, LiveKit

How to Build Voice Agents That Don't Fall Apart When You Interrupt - Jesse Hall, LiveKit

Jesse Hall of LiveKit explains how to build voice agents for financial services that feel human rather than robotic. The talk covers why model leaderboards mislead for real-time conversations, why time to first audio and time to first sentence matter more than first byte or first token, and how async tool calls keep a conversation flowing during slow back-end work. It also covers audio-aware turn detection, the roughly 1.5-second latency budget, and benchmarking your own stack with simulated callers.

Technology

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What will computer chips look like in 2045?

What will computer chips look like in 2045?

Intel's leadership walks through how advanced process nodes are brought to volume, from yield learning and the lessons of 10nm to the shift from planar transistors to gate-all-around, High-NA EUV, backside power, and CFET. The discussion widens to the chip-to-chip bottleneck that hybrid bonding targets, the supply limits on EUV tooling as AI compute demand grows, and the economics that make foundry scale decisive.

Former Intel CEO: Why This is the Best Time to Build Hardware

Former Intel CEO: Why This is the Best Time to Build Hardware

Pat Gelsinger joins a16z's Raghu Raghuram and Guido Appenzeller to discuss how AI is reshaping chip design, why design speed alone will not fix semiconductor bottlenecks, and why power and energy capacity may ultimately limit AI growth. The conversation covers HBM and memory innovation, stacking limits, the shift from copper to optics, consolidation among AI chip startups, and what a VMware for AI agents might look like.

Built Together: Inside the Dell + CoreWeave Engineering Partnership Powering Next-Gen AI

Built Together: Inside the Dell + CoreWeave Engineering Partnership Powering Next-Gen AI

A candid panel between Dell and CoreWeave leaders on co-engineering GB200, GB300, and Vera Rubin rack-scale AI platforms. The discussion covers why early access and stabilization matter in the AI arms race, how the industry moved from server-level to rack-as-computer architecture, and how joint development, standardized Redfish tooling, and shifting diagnostics left have enabled scale. It closes with where rack-scale infrastructure is heading toward pods, denser heterogeneous systems, and the need for automation in liquid cooling and power.

Recent Post

How a Trading Firm Ships New AI Models the Same Day - Noyan Tokgozoglu, Optiver

How a Trading Firm Ships New AI Models the Same Day - Noyan Tokgozoglu, Optiver

A look at how Optiver, a proprietary trading firm, runs an on-prem AI platform where every model, agent, and harness decision has to show results in P&L within hours. Topics include the inference gateway, same-day adoption of frontier models, the meta harness design, and efforts to bring AI spend back under control.

Agents That Self Improve While You Sleep - Fuad Ali, Arize AI

Agents That Self Improve While You Sleep - Fuad Ali, Arize AI

A workshop on building a self-improvement loop for AI agents: tracing production traces, using an always-on agent to file deduplicated issues, turning failures into evals and regression datasets, proving fixes with experiments, and keeping humans at the highest-leverage review points.

How to Build Voice Agents That Don't Fall Apart When You Interrupt - Jesse Hall, LiveKit

How to Build Voice Agents That Don't Fall Apart When You Interrupt - Jesse Hall, LiveKit

Jesse Hall of LiveKit explains how to build voice agents for financial services that feel human rather than robotic. The talk covers why model leaderboards mislead for real-time conversations, why time to first audio and time to first sentence matter more than first byte or first token, and how async tool calls keep a conversation flowing during slow back-end work. It also covers audio-aware turn detection, the roughly 1.5-second latency budget, and benchmarking your own stack with simulated callers.

How to Write Your First AI Eval - SallyAnn DeLucia, Arize AI

How to Write Your First AI Eval - SallyAnn DeLucia, Arize AI

A hands-on workshop from Arize AI on moving from vibe checks to real LLM evals. It covers what an eval is and how it differs from a unit test, the main eval types, how to read agent traces, how to write concrete pass/fail criteria, and how to build and iterate on an LLM-as-a-judge in Arize's prompt playground.

What Does an AI Engineer Do Now? - Aparna Dhinakaran, Arize AI

What Does an AI Engineer Do Now? - Aparna Dhinakaran, Arize AI

Aparna Dhinakaran of Arize AI argues that the AI engineer's job is shifting from writing features to building the loop that builds them: evals that review non-deterministic work, telemetry that agents consume, and always-on agents that act on it. She uses Arize's Signal agent as a case study, covering triggers, sandboxes, durable state, model and harness independence, and evaluating the evaluators.

Can Your AI Analyst Keep a Secret? - Aaron Linsky, Bridgewater & Nikhil Benesch, turbopuffer

Can Your AI Analyst Keep a Secret? - Aaron Linsky, Bridgewater & Nikhil Benesch, turbopuffer

Aaron Linsky of Bridgewater Associates and Nikhil Benesch of turbopuffer explain how permission-aware retrieval over a 100-million-document research vault broke a conventional search setup, and why native filtering over a clustered vector index is needed to keep search fast without sacrificing recall.

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