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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

Agents Need Receipts, Not More Tool Calls - Armanas Povilionis, Alithea Bio

Agents Need Receipts, Not More Tool Calls - Armanas Povilionis, Alithea Bio

Armanas Povilionis introduces Froglet, an open-source protocol addressing the critical need for verifiable agent-to-agent collaboration in AI workflows, especially in scientific research. Froglet enables agents to discover, transact with, and receive tamper-proof receipts for services across organizational boundaries, transforming them into 'executive chefs' managing distributed supply chains rather than isolated 'cooks' with more tools. The protocol streamlines interactions into a signed flow (Descriptor -> Offer -> Quote -> Deal -> Receipt) and integrates with diverse agent harnesses and execution environments, abstracting complex operations for LLM-driven agents.

Agents Need Feature Flags - Sachin Gupta

Agents Need Feature Flags - Sachin Gupta

AI teams are deploying advanced agent systems without the fundamental safety mechanisms (feature flags, canaries, kill switches) that web teams adopted over a decade ago. This oversight leads to critical incidents like data deletion and financial loss. This talk introduces six agent-specific feature flag types—for prompts, tools, models, memory, autonomy, and sub-agents—and outlines a practical playbook for secure AI deployment, emphasizing the critical role of a pre-wired kill switch to manage the high blast radius of AI agents.

Your Agents Need a Save Button - Hamza Tahir, ZenML

Your Agents Need a Save Button - Hamza Tahir, ZenML

Most AI agents lack a "save button" for persistent state, making debugging and optimization challenging. This talk introduces the concept of a durable runtime with checkpointing, allowing developers to replay agent executions, perform "what-if" analyses (e.g., swapping models, mocking tools), and conduct cohort-based evaluations using production data. The open-source tool Kitaru is demonstrated as a solution to build, debug, and improve agents at scale by capturing full execution state, not just traces.

The Great Loops Debate — Dex Horthy, Geoff Huntley, Ian Livingstone, Greg Pstrucha, @insecure-agents

The Great Loops Debate — Dex Horthy, Geoff Huntley, Ian Livingstone, Greg Pstrucha, @insecure-agents

An Oxford-style debate exploring the gap between the hype and practical reality of "loops" in AI/ML development. Experts discuss their history, optimal anatomy, future role in software factories, and challenges like security, economic viability, and the imperative for strong engineering discipline.

On AI and Knowledge — Pablo Castro, Distinguished Engineer & CVP for AI Knowledge, Microsoft

On AI and Knowledge — Pablo Castro, Distinguished Engineer & CVP for AI Knowledge, Microsoft

Pablo Castro explores the three pillars of AI knowledge—intrinsic, extrinsic, and learned—explaining how models leverage parametric memory, agents integrate with enterprise data through sophisticated RAG systems like Foundry IQ, and how continuous learning loops, facilitated by tools like the Agent Optimizer, enable self-improving agents, driving the next wave of AI applications.

Research to Reality with Google DeepMind — Benoit Schillings, Google DeepMind, VP of Technology

Research to Reality with Google DeepMind — Benoit Schillings, Google DeepMind, VP of Technology

Benoit Schillings, VP of Technology at Google DeepMind, explores the evolution of AI's role in software development, highlighting the transition from human-limited coding to an AI frontier where syntax generation is solved. He delves into the power of self-play for model training, the shifting economics of software engineering, and the imperative for active guardrails. Schillings also discusses the need for inductive architecture, advanced model planning, multimodal reasoning (as seen in Gemini), and the potential for AI to drive scientific breakthroughs in fields like chemistry and biology by uncovering patterns imperceptible to human bias.

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