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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

Beam: The Great American Open Model with ReflectionAI Co-Founder and CEO Misha Laskin

Beam: The Great American Open Model with ReflectionAI Co-Founder and CEO Misha Laskin

Misha Laskin, co-founder and CEO of Reflection AI, discusses building frontier open-weight models, the launch of the 500B-parameter Beam reasoning model, the economics of renting versus owning intelligence, and why he believes open models will capture most token demand. The conversation covers RL-driven scaling efficiency, Chinese open model ecosystems and their geopolitical implications, and the safety case for openness, including cybersecurity as a lens for defense and offense. He closes with optimism about AI-accelerated science and the evolving research workforce.

High-Performance Engineering: Forward Deployed at Aston Martin F1

High-Performance Engineering: Forward Deployed at Aston Martin F1

A session on CoreWeave's forward deployed engineering partnership with the Aston Martin Aramco F1 Team, covering why Formula 1 is an unforgiving environment for AI, and three projects: a unified data platform for aerodynamic and telemetry data, Delta to Map, a small transformer model that reduces the gap between wind tunnel predictions and track measurements, and Radio Insights, a fine-tuned speech-to-text and agentic system that makes sense of 40 live radio channels. The discussion extracts lessons applicable to any team moving AI from experiment to production under fixed deadlines.

Beyond Faster Simulation: Hitting NASA's 2030 CFD Benchmark and the Infrastructure for Physics AI

Beyond Faster Simulation: Hitting NASA's 2030 CFD Benchmark and the Infrastructure for Physics AI

nTop and CoreWeave completed 10,000 LES CFD simulations in about 32 hours, meeting NASA's 2030 CFD Vision goal four years early. The approach combines signed distance field geometry, which cannot break under parametric variation, with a GPU-native Lattice Boltzmann solver that removes the meshing step entirely. The talk also covers the 14 TB dataset generated and the path toward physics AI surrogates trained in implicit latent space.

Building on the Codex Harness — Dominik Kundel, OpenAI

Building on the Codex Harness — Dominik Kundel, OpenAI

Dominik Kundel from OpenAI's Codex team explains the Codex App Server, the open-source JSON-RPC protocol that wraps the Codex harness and powers the Codex app, IDE extensions, and third-party clients. He covers live protocol events, more than 120 client messages, ChatGPT sign-in, developer instructions, deferred dynamic tools, shipping and binding generation, the Python SDK, and a demo of Codex running inside Doom.

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.

Stop Prompting — Greg Pstrucha, Sentry

Stop Prompting — Greg Pstrucha, Sentry

Greg Pstrucha of Sentry argues that instead of repeatedly re-prompting coding agents, teams should codify their recurring corrections as deterministic checks. He covers why agents forget and why custom lint rules are now cheap, how to mine transcripts and PR reviews for rules, real Sentry examples that keep OpenAPI schemas and agent skills in sync, written policies and a Grumpy Engineer review loop for qualitative judgment, and why agents game metrics like test coverage.

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