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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

Magic Patterns CEO Reviews Startup Websites

Magic Patterns CEO Reviews Startup Websites

Alex Danilowicz, co-founder of Magic Patterns (YC W23), walks through the company's origin as an attempt to make design accessible to front-end engineers and non-designers, demos the new MagicOS canvas and its MCP integration with coding agents, and reviews several AI-built startup landing pages to explain what makes a site feel vibe coded and how to make AI-generated design more intentional.

Token Maxxing Is Over and Here Is What Comes Next

Token Maxxing Is Over and Here Is What Comes Next

Pepijn Senders, a principal engineer and former AI enablement lead at HelloFresh, explains how his team rebuilt a banking platform in six months with about 800,000 lines of code, mostly written by Sonnet, and why choosing a model that keeps developers involved matters more than raw autonomy. He covers measuring AI ROI through token spend versus output, keeping humans in control of code, the risks of agent recommendations, team refinement sessions, prompt word effects, and a meta harness built on Claude Code.

The Recommendation Engine for Commodity Traders — Anekha Sokhal, Moshi

The Recommendation Engine for Commodity Traders — Anekha Sokhal, Moshi

Anekha Sokhal, founder and CEO of Moshi, explains how her background in energy derivatives trading and machine learning research led to a real-time intelligence platform for financial traders. The discussion covers how Moshi structures public, alternative, and private data into a connected graph, why recommendation and UI design matter as much as modeling, how trusted sources and conflicting reports are handled, and the pressure on AI engineers to become the whole team.

AI Can Now Predict 85%+ of What You’ll Do Next | Simile Co-founder & CEO, Joon Sung Park

AI Can Now Predict 85%+ of What You’ll Do Next | Simile Co-founder & CEO, Joon Sung Park

Joon Sung Park, creator of generative agents and CEO of Simile, explains how foundation models trained on real human behavior can simulate people, from a 25-agent town that organized its own Valentine's Day party to 85%-plus accurate predictions of how real participants respond in surveys, behavioral games, and randomized trials.

The New Apex Predator: CrowdStrike CEO George Kurtz on Adversaries Armed With AI Agents

The New Apex Predator: CrowdStrike CEO George Kurtz on Adversaries Armed With AI Agents

George Kurtz, CEO of CrowdStrike, discusses how agentic AI has flattened the adversary pyramid, putting nation-state-grade tradecraft in the hands of low-skill attackers, and why open-weight models have become the tip of the spear. He covers CrowdStrike's Safe Mind lab, built with NVIDIA around red and blue security models trained on fifteen years of telemetry, the lessons from the Hugging Face incident, the case for defense dominance, and why he sees AI as a net creator of opportunity.

1,368 AI Failures and What They Teach About Evals — Vishal Srivastava & Tanmay Sah, New York Life

1,368 AI Failures and What They Teach About Evals — Vishal Srivastava & Tanmay Sah, New York Life

Vishal Srivastava and Tanmay Sah of New York Life analyze 1,368 public AI incidents to show what real failures reveal about how AI systems should be tested. They organize failures into three dimensions (technical strength, deployment controls, business impact) and 13 sub-dimensions covering about 98% of cases, find that weak deployment controls are most closely tied to harm, and present a materiality-based ranking of use cases, a failure-driven build-break-fix evaluation loop, and an airline chatbot refund case as an example.

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