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Open Source Security in the Age of 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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Open Source Security in the Age of AI

Open Source Security in the Age of AI

AI is accelerating the discovery of open-source vulnerabilities, but finding flaws is only the beginning. Remediating, testing, validating, and deploying fixes safely across critical infrastructure is where the real bottleneck lies, and securing open source at AI speed requires upstream contributions and coordinated efforts like Lightwell and Akrites.

Why a Tenured Professor Went Back to School for AI (with John Renne)

Why a Tenured Professor Went Back to School for AI (with John Renne)

John Renne, a tenured professor of real estate and urban planning at Tulane University, explains how returning to school for a master's in AI transformed his research on transit-oriented development, from analyzing 4,500 U.S. train stations to a global database of over 110,000, and how computer vision can now rate how attractive a street is. He also covers teaching vibe coding to urban planning undergraduates and making the case for AI-led oral exams over AI detectors.

Why Are We Still Governing AI Agents Like Software? - Tim Tyler, Gravitee

Why Are We Still Governing AI Agents Like Software? - Tim Tyler, Gravitee

Tim Tyler of Gravitee introduces the Agent Accountability framework, which treats AI agents as a digital workforce governed through three stages: Assignment (identity, team, and a named sponsor), Alignment (role, mission, and least-privilege authority), and Administration (oversight scorecards, guardian agents, and graduated recourse when agents drift off course).

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

The Agentic Automation Revolution • Don Syme • Bug Bash EU 2026

The Agentic Automation Revolution • Don Syme • Bug Bash EU 2026

Don Syme, designer of F# and GitHub Next engineer, explains how AI agents are moving from individual productivity tools toward continuous, collaborative automation inside repositories. He introduces continuous AI and agentic ops, GitHub Agentic Workflows with their guardrails, and the factory and farm metaphors for managing software production.

AI in NYC: How Tenex Builds an AI-Native Engineering Team — Alex Lieberman & CJ Hess

AI in NYC: How Tenex Builds an AI-Native Engineering Team — Alex Lieberman & CJ Hess

Alex Lieberman and CJ Hess of Tenex discuss how an AI-native engineering consultancy organizes its work around outcomes, not hours. They cover story-point compensation, the MetaHarness workflow that keeps plans and decisions in Git, token economics and model choices, the gap between enterprise AI rollouts and production agents, why vertical AI and mid-tier SaaS may or may not survive, and what this means for jobs and New York's AI scene.

Same GPU, 50% More Output: Why the Operator Matters — Oliver Kennon, SemiAnalysis

Same GPU, 50% More Output: Why the Operator Matters — Oliver Kennon, SemiAnalysis

Oliver Kennon of SemiAnalysis argues that GPU hours are an input rather than the product, and that the operator determines how much useful intelligence those hours produce. The talk covers serving stacks, cluster security, ClusterMAX operator ratings, rental price versus production cost, relative cost-curve positioning, and what compute grades and futures can and cannot hedge.

A Bank Built Its Own Coding Agent. It Took Off — Felipe Felisola Caso & Francesco Galletta, Monzo

A Bank Built Its Own Coding Agent. It Took Off — Felipe Felisola Caso & Francesco Galletta, Monzo

Felipe Felisola Caso and Francesco Galletta from Monzo explain how an internal Slack agent grew from a viral emoji trigger into a teammate authoring over 10% of merged PRs and running more than 1,800 tasks a day. They attribute its speed to existing platform capabilities, an MCP gateway, a sandboxed orchestration system suited to a regulated bank, and an agent designed as a composable building block for incidents, on-call data quality and non-engineer contributions.

The Problem with Jev: Can You Trust AI Decisions?

The Problem with Jev: Can You Trust AI Decisions?

Jeff Crume examines Jev, a new AI approach that makes structured decisions rather than generating text, through a cybersecurity lens. He argues that confidence scores are not evidence, that AI decisions expand the attack surface, and that observability, decomposed reasoning, independent controls, and human oversight are needed before trusting them.

The World Wild Web & Bots With Buying Power

The World Wild Web & Bots With Buying Power

This episode asks whether a model described as dangerously capable is a real security problem or a marketing narrative. It uses the Astra sandbox incident as a starting point, then covers how agents are moving from chat into the browser to book, file, draw, and shop, and what that shift means for payments, advertising, and merchant data.

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