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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

Can AI Understand a Distressed Credit? - Harneet Kaur, Axim Intelligence

Can AI Understand a Distressed Credit? - Harneet Kaur, Axim Intelligence

Harneet Kaur, founder and CEO of Axim Intelligence and a former distressed credit investor, tests whether frontier models can reason about a live distressed situation, iHeartMedia. Two top models disagreed sharply on the odds of bankruptcy, but a layered reasoning process brought them to the same answer, raising the question of whether agreement is the same as being right.

Proving Alpha: How a Hedge Fund Measures AI's ROI - Hari Kumar, Contour Asset Management

Proving Alpha: How a Hedge Fund Measures AI's ROI - Hari Kumar, Contour Asset Management

How Contour Asset Management, a $6B fundamental long/short technology, media and telecom fund, measures the return on AI spend through live A/B tests of analyst research, a cost per useful insight metric, timing agents that resurface old ideas, and AI workflows across trading, operations and compliance.

Inside Arca's Long-Horizon Agents That Run the Business - Aman Singh, Arca

Inside Arca's Long-Horizon Agents That Run the Business - Aman Singh, Arca

Arca, an AI-native financial services firm, runs its client business through long-horizon agents that own goals end to end across days of stop-start work. Aman Singh, who leads AI there, explains the signal system, operational context, harness, persistent memory, and shared spaces that let agents pull in people at the right moment, and why steering agents becomes the way to steer the business.

How OpenAI Built ChatGPT for Bankers and Analysts — Joseph Kim, OpenAI

How OpenAI Built ChatGPT for Bankers and Analysts — Joseph Kim, OpenAI

Joseph Kim, who leads ChatGPT for Financial Services, explains the capability overhang, the gap between what models can do and the value professionals get from them at work. He covers the product built with Morgan Stanley and Evercore, why the team licensed and indexed financial data instead of relying on MCP connectors, how firm templates, charts, and citation viewers sit on the ChatGPT plugin platform, and his view of model progress, the threshold of usefulness, and three bets for finance in 2027.

Security and Privacy in the Underserved Majority World: Practices, Challenges, and Opportunities

Security and Privacy in the Underserved Majority World: Practices, Challenges, and Opportunities

Collins Munyendo presents studies from Kenya and across the majority world showing how economic pressure, shared computing resources, social dynamics, and language barriers challenge Western-centric security and privacy designs, and proposes privacy-by-design redesigns and privacy-enhancing technologies for data sharing developed with local communities.

Buying GPUs Should Work Like Buying Stocks — Carmen Li, Compute Exchange

Buying GPUs Should Work Like Buying Stocks — Carmen Li, Compute Exchange

Carmen Li, CEO of Compute Exchange and Silicon Data, makes the case for treating GPU procurement like exchange trading. She covers the neutral, verified marketplace model, its reserved, forward, token and secondary server products, and the RFQ routing system. She then examines Silicon Data's forward curves for H100 and B200, the market's expectation of rising prices, and how used GPUs are holding value well above standard depreciation.

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