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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

Revolutionizing Cybersecurity and AI-Driven Science with OpenAI President Greg Brockman

Revolutionizing Cybersecurity and AI-Driven Science with OpenAI President Greg Brockman

Greg Brockman, OpenAI co-founder and president, discusses OpenAI's recent math results, in which roughly 400 open problems were resolved using AI agents, and why he believes AI can now generate genuinely new knowledge. The conversation covers AI oversight and communication techniques like debate and iterative amplification, the data bottlenecks that separate math from biology, the potential transformation of drug discovery and regulation, the proactive always-on Dots agent, the convergence of consumer and enterprise AI, why the best UI may be no UI, the cybersecurity 'defenders' window,' and why security spending should outstrip software spending while humans stay in control.

CoreWeave Fully Connected 2026 Keynote: Introducing CoreWeave Forge | Built for Adoption

CoreWeave Fully Connected 2026 Keynote: Introducing CoreWeave Forge | Built for Adoption

CoreWeave's Fully Connected 2026 Day 1 keynote centers on the AI loop of run, observe, curate, improve and evaluate, and the launch of CoreWeave Forge. It covers production agent operations, agent observability and RL rollouts, Vera Rubin NVL72 and the Vera CPU, and enterprise evaluation practices from Capital One, Caterpillar and Cognition.

Scalable Training of Diffusion Samplers via Fixed-point Score Matching

Scalable Training of Diffusion Samplers via Fixed-point Score Matching

Denis Blessing presents scalable training of diffusion samplers via fixed point score matching: a unifying framework for learning Boltzmann-type distributions from energy evaluations alone, with applications to amorphous materials, alloy phase diagrams, and reinforcement learning with flow policies.

Paving the Road to Production: Governed Enterprise AI Agents — Harish Gaggar, Credit Karma

Paving the Road to Production: Governed Enterprise AI Agents — Harish Gaggar, Credit Karma

Harish Gaggar of Intuit Credit Karma walks through an internal paved path for taking enterprise AI agents from experiments to production. The talk covers portal-based registration and provisioning, a centralized authenticated MCP gateway with custom reviewed MCP servers, gateway monitoring with OpenTelemetry, Tempo, Prometheus, and Grafana, LangGraph workflows with policy checks and human approval, durable memory through threads and checkpoints, agent tracking and evaluation records, PII filtering, and context optimization for token reduction.

Your AI Isn’t the Problem. Your Infrastructure Is. — Jiachen Jiang, Coder

Your AI Isn’t the Problem. Your Infrastructure Is. — Jiachen Jiang, Coder

Jiachen Jiang uses three agent security incidents, the Replit production database deletion, a relayed banking compromise involving Copilot, and the Nx supply chain attack, to argue that prompts cannot enforce boundaries. He maps each incident to six infrastructure controls: environmental separation, short-lived scoped credentials, identity-aligned access, model proxies, sandboxing, and independent audit logs.

Can Open Models Keep Up With OpenAI and Anthropic? | Nathan Lambert

Can Open Models Keep Up With OpenAI and Anthropic? | Nathan Lambert

Nathan Lambert, AI researcher and founder of the nonprofit Trillium, joins Lisha Li to examine how durable the gap is between frontier closed labs like OpenAI and Anthropic and open-weight models. The discussion covers compute, data, and talent as the drivers of model progress, how much distillation really matters for Chinese open labs, the evolution of post-training toward RL environments, and why open research on RL environments and recursive self-improvement is needed to demystify what frontier systems can and cannot do.

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