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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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Stop Giving Your Agent Tools. Give It a Sandbox - Mikyo King, Arize AI

Stop Giving Your Agent Tools. Give It a Sandbox - Mikyo King, Arize AI

Mikyo King, head of open source at Arize AI, explains code mode, an agent architecture where the model searches for tools and composes them in a sandboxed programming environment instead of loading large tool catalogs into context. He covers why it works, how Arize built it into Phoenix with Bash, Python, SQL, and UI control, its hidden costs around secrets, permissions, durability, and double sandboxing, and Harbor evals showing 43% fewer tokens and 31% lower cost.

What a $900M Bank Error Teaches Us About AI Agents - John Feminella, Two Sigma

What a $900M Bank Error Teaches Us About AI Agents - John Feminella, Two Sigma

John Feminella of Two Sigma uses a 2020 Citibank payment error to show how AI agents can be given real autonomy through five composable controls: an agent registry, risk-based review, purpose-scoped tool access, shared evals, and classifiers that send humans only the approvals that matter.

The AI School Kids Love More Than Vacation | Alpha School's Joe Liemandt

The AI School Kids Love More Than Vacation | Alpha School's Joe Liemandt

Alpha School principal Joe Liemandt and Horowitz Andreessen Academy co-founder Gagan Biyani discuss how AI-powered learning could compress core academics into two hours a day, freeing the rest of school for teamwork, entrepreneurship, and real-world building, and what that model means for K-12 families, higher education, and the next generation of builders.

Technology

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Can Your AI Heat My House?

Can Your AI Heat My House?

A wide-ranging conversation on how AI inference is pushing cloud infrastructure, data centers, and hardware supply chains against physical limits, covering the rise of alt clouds, rack density and cooling, hardware lifecycles and circularity, talent shortages, heat reuse for communities, and what engineering leaders can do about resource-aware software.

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.

Recent Post

The Agent Works. Does Anyone Trust It? — Michele Grant, Leading at Scale

The Agent Works. Does Anyone Trust It? — Michele Grant, Leading at Scale

Michele Grant argues that an AI agent's technical accuracy is not enough to justify running a core business process. Using an illustrative M&A integration scenario, she lays out a framework built on measurable value against a baseline, three lines of accountability, a Human Works Standard, auditable evidence such as decision logs and model inventories, and staged autonomy governed by independent sampling and an autonomy scorecard.

The Index is never done

The Index is never done

Exa's Jon details how their company maintains a massive, continuously updated semantic index of 100 billion pages to serve AI agents. The talk covers Exa's unique incremental indexing system, a custom data processing framework called "Skeleton" that handles 100,000 pages per second, and "Pythia," an elastic, cost-aware GPU orchestration system for managing both real-time ingestion and large-scale index re-embeddings.

Deploying AI Inside a Bank Is Not an Engineering Problem — Paden Gayle, Applied AI Engineers

Deploying AI Inside a Bank Is Not an Engineering Problem — Paden Gayle, Applied AI Engineers

A bank's AI demo works but cannot ship due to non-engineering problems like security, data ownership, and risk approval. Paden Gayle outlines a method focusing on early stakeholder discovery, iterative workflow scoping, gradual autonomy, and a comprehensive evidence pack to build AI systems with a credible path to production, emphasizing that "discovery is the engineering."

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.

David Silver and Lasse Espeholt of Ineffable Intelligence: A Superlearner for the Age of Experience

David Silver and Lasse Espeholt of Ineffable Intelligence: A Superlearner for the Age of Experience

David Silver, co-founder of Ineffable Intelligence, outlines his bold vision for building a "superlearner"—an AI that discovers science, mathematics, and language autonomously, free from human data or explicit guidance. Drawing parallels to AlphaGo Zero's success, Silver argues that human-language reasoning is a ceiling, not a foundation, for advanced AI. He details their approach using complex simulated "crucibles" and simple rewards, aiming to develop the "science of superintelligence" in simulation for safety and accelerated progress. With a powerful GPU cluster and a small, elite team, Silver forecasts strong evidence within a year and superintelligence within three.

The Section That Gives Away AI-Written Docs (with Carly Taylor)

The Section That Gives Away AI-Written Docs (with Carly Taylor)

Carly Taylor, a prominent engineering leader and founder of Rebel Data Science, discusses strategies for thriving amidst rapid AI advancements, the importance of authenticity and identifying "AI slop," balancing innovation with operational maintenance, and overcoming fear through embracing discomfort. She also addresses the "glass cliff" phenomenon for women in leadership and how to effectively leverage AI without sacrificing critical thinking.

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