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Your AI Agent Starts Every Session Like a New Hire - Killian Carlsen-Phelan, Sonar

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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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Voice AI Where One Wrong Question Breaks the Rules - Shiv Indap, Upstart & Brooke Hopkins, Coval

Voice AI Where One Wrong Question Breaks the Rules - Shiv Indap, Upstart & Brooke Hopkins, Coval

Shiv Indap of Upstart and Brooke Hopkins of Coval walk through automating loan verification calls in a heavily regulated lending environment. They cover the compliance traps that shape the agent's behavior, moving human review beyond a Google Sheet, and raising LLM judge agreement with human reviewers from 33% to 92%. The talk then lays out a continuous loop of live monitoring, human review queues, simulation-based testing of fixes, and self-healing agents, with humans signing off on every change.

Building ChatGPT for Finance — Joseph Kim, OpenAI's Head of Financial Services

Building ChatGPT for Finance — Joseph Kim, OpenAI's Head of Financial Services

Joseph Kim of OpenAI discusses building ChatGPT for Financial Services on a general-purpose model: upstream financial reasoning training, bundling market data after MCP reliability tests, plugin primitives startups can reuse, human-in-the-loop review, training judgment and taste via reward models, and the difficulty of building financial evals.

How to Decide What Your AI Agent Should Remember - Wojciech Pluta, Oracle

How to Decide What Your AI Agent Should Remember - Wojciech Pluta, Oracle

Wojciech Pluta, head of technical marketing for Oracle AI Database, argues that RAG gives agents a read path while memory gives them a write path, and that the write path is where business value lives. He explains memory promotion, a pipeline that admits, rejects, or escalates candidate memories, and demonstrates it with a mortgage agent that remembers preferences across sessions while refusing unsafe rules. The talk also covers short-term, long-term, and shared memory, why databases are a strong storage layer, and the context card that keeps prompts lean.

Technology

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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.

Exploits Now Beat Patches: Open Source Security in the AI Era - Patrick Smyth, Chainguard

Exploits Now Beat Patches: Open Source Security in the AI Era - Patrick Smyth, Chainguard

Patrick Smyth of Chainguard examines how frontier AI has compressed the time between vulnerability disclosure and exploitation, leaving 30-day remediation cycles obsolete. The talk covers AI-assisted attacks, waves of supply chain malware, four attack surfaces spanning malware, known CVEs, zero days, and side doors like CI/CD, developer laptops, and agent skills, and practical defenses including cooldown periods, faster CVE remediation, and signing artifacts with SLSA and Sigstore.

Recent Post

Level Up: Choosing The Technical Leadership Path • Patrick Kua • GOTO 2025

Level Up: Choosing The Technical Leadership Path • Patrick Kua • GOTO 2025

Patrick Kua explores the essence of technical leadership, emphasizing its critical role in overcoming organizational misalignment and accidental complexity, drawing from real-world examples. He introduces a "Trident Model" for career growth, differentiating technical leadership from individual contribution and management. The talk highlights compelling reasons for engineers to embrace leadership, such as shaping team environments, influencing product direction, and solving complex "people-system" problems. Finally, Kua provides a practical five-step recipe for aspiring leaders to start demonstrating impact through ownership, sponsorship, and delivering tangible value.

Has AI Finally Cracked Time Series Forecasting?

Has AI Finally Cracked Time Series Forecasting?

Ameet Talwalkar, CMU professor and Datadog's Chief Scientist, traces the journey of time series foundation models from early skepticism to their current impact. He details Datadog's Toto V1 and V2, highlighting breakthroughs in zero-shot performance, scaling, and the crucial role of data mix. The discussion extends to the vision of 'world models' for observability, integrating diverse data for self-healing software systems, and concludes with insights on open-weights models and AI's transformative effect on computer science and academic research.

Computer-Use 2.0: Agents Just Got Multi-Cursor — Francesco Bonacci, Cua

Computer-Use 2.0: Agents Just Got Multi-Cursor — Francesco Bonacci, Cua

This talk introduces cua driver, an open-source tool enabling AI agents to interact with computer GUIs in the background across macOS, Windows, and Linux by leveraging accessibility APIs. It details CUABench, a robust evaluation framework with over 130 verifiable tasks designed to benchmark and ensure the trustworthiness of computer-using agents, revealing current limitations in complex tasks like circuit design. Finally, it presents cua fleet, an infrastructure solution that optimizes GPU utilization and reduces costs for RL training of these agents by dynamically managing sandbox environments.

Recursive Model Improvement — Lee Robinson, Cursor, SpaceXAI

Recursive Model Improvement — Lee Robinson, Cursor, SpaceXAI

Lee Robinson of Cursor outlines a comprehensive strategy for recursive AI model improvement, centered on a two-loop training framework. He details how Cursor enhances both user-feedback-driven outer loops and high-quality evaluation inner loops, introducing novel methods like textual feedback and addressing reward hacking. The discussion extends to scaling compute infrastructure through partnerships with SpaceX, Colossus, and Terafab, and leveraging agent-based automation to streamline research and foster a future where models continuously train and improve themselves.

Simon Willison in conversation with Cat Wu & Thariq Shihipar, Anthropic

Simon Willison in conversation with Cat Wu & Thariq Shihipar, Anthropic

A Q&A with Anthropic's Cat Wu and Thariq Shihipar on how Claude Code and agentic AI are fundamentally changing software development, from workflow shifts and engineering norms to safety, model trust, and team collaboration. The discussion covers the rapid evolution of coding agents, the rise of proactive agents like Claude Tag, new approaches to code review and system prompt optimization, and Anthropic's robust safety measures including Auto Mode.

Omnigent: Composition, Control, and Collaboration for AI Agents

Omnigent: Composition, Control, and Collaboration for AI Agents

Denny Lee discusses the industry's shift to meta-harnesses like Omnigent, which enables hot-swappable AI models and agents, illustrated by his personal project of using debating agents to plan a matcha farm in Taiwan. He highlights how "tokenomics" is replaying the CapEx-to-OpEx cost shift, emphasizing the need for developer visibility, central governance, and auto-model selection to manage AI spend. The conversation also touches on the importance of databases for agent memory and accountability in AI-assisted workflows.

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