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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

Giving AI Agents Memory That Learns — Jake Broekhuizen, LangChain

Giving AI Agents Memory That Learns — Jake Broekhuizen, LangChain

Jake Broekhuizen of LangChain Labs argues that observability alone does not make agents improve. Using a financial services agent whose tone drifted from advisory to pushy, he shows how traces only become useful memory when a filtered lesson is written back as durable context the agent reads on its next run. The talk covers semantic, episodic, and procedural memory, the working versus long-term memory distinction, the read-write cycle, and how LangSmith and Context Hub support capture, analysis, and updates, along with lessons about keeping only signal, caching, and human review of procedural changes.

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.

AI Tokenomics Explained

AI Tokenomics Explained

A product and go-to-market lead at NVIDIA examines the economics of AI inference as agentic AI multiplies token demand. The talk covers matching premium versus efficient models to use cases, model routing, open models, sizing token demand, and extreme co-design across mixture-of-experts models, NVLink systems, KV caching, software stacks, and CPUs to maximize tokens per watt and minimize cost per token.

$450K TO TRAIN A FRONTIER AI MODEL?

$450K TO TRAIN A FRONTIER AI MODEL?

Thomson Reuters CTO Joel Hron explains why the company built Thomson, a domain-specific frontier model adapted from open-weight checkpoints like Qwen 3.5, using decades of proprietary legal and tax content and expert judgment. The conversation covers the training lifecycle from realignment and continual pre-training to agentic RL, the economics ($450K final run versus roughly $40M total), team structure, CoCounsel orchestration, verification challenges in legal and tax, and the future balance between open-weight and frontier models.

Rapidly Deploying Useful General-Purpose Robots at Walden Robotics

Rapidly Deploying Useful General-Purpose Robots at Walden Robotics

A talk from Walden on deployment-first physical AI: getting general-purpose robots into real manufacturing production, using remote assistance as a human-in-the-loop safety and learning mechanism, and building a data flywheel in which operational data drives continuous improvement of large behavior models.

AI Scientists Are Here: Autonomous Labs & Synthesis Superintelligence — Periodic Labs

AI Scientists Are Here: Autonomous Labs & Synthesis Superintelligence — Periodic Labs

Liam Fedus and Ekin Dogus Cubuk of Periodic Labs explain why scientific discovery requires physical experiments rather than more internet data alone, and how they are building autonomous labs where AI systems reason under noise and uncertainty. The conversation covers reinforcement learning grounded in experiments, the materials discovery loop from prediction to synthesis to characterization, the limits of DFT, why physics remains unsolved, negative results as training data, scaling autonomous labs, and the broader goal of synthesis superintelligence aimed at discovering new superconductors, magnets, and batteries.

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