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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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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.

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.

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

The AI Memory Wall: Why LLMs & AI Accelerators Hit a Bottleneck

The AI Memory Wall: Why LLMs & AI Accelerators Hit a Bottleneck

A walkthrough of the memory wall, explaining why raw chip compute growth has not translated into proportional AI workload speedups. It covers arithmetic intensity, the roofline model and ridge points for the B200 GPU, why LLM decode is memory-bound while prefill is compute-bound, and how batching and continuous batching shift workloads toward higher efficiency.

Evals for High-Stakes AI Agents — Doneyli De Jesus & Annabell Schäfer, Langfuse

Evals for High-Stakes AI Agents — Doneyli De Jesus & Annabell Schäfer, Langfuse

A hands-on workshop from Langfuse on evaluating AI applications where mistakes are costly. Using a banking assistant as the running example, Annabell Schäfer and Doneyli De Jesus walk through the AI engineering loop: tracing with PII masking, building datasets, turning domain-expert comments into failure modes, mixing LLM-as-a-judge and code evaluators, and CI gates that block prompt changes when scores drop.

Teach Your Agent to Prove It's Done — Nick Nisi & Zack Proser, WorkOS

Teach Your Agent to Prove It's Done — Nick Nisi & Zack Proser, WorkOS

Nick Nisi and Zack Proser from WorkOS explore how to move "Beyond Done" with AI agents, ensuring their work is truly complete and reliable. They demonstrate building robust verification loops using custom CLIs, browser tools, and LLM judges, allowing agents to self-check and self-correct, preventing costly mistakes and accelerating development velocity for a technical audience.

An AI Agent Got Its User Banned From Resy — Keycard, JPMorgan Chase & AAuth

An AI Agent Got Its User Banned From Resy — Keycard, JPMorgan Chase & AAuth

A panel of experts discusses the challenges of granting autonomy to AI agents due to coarse-grained authorization systems. They advocate for intent-based runtime authorization, leveraging concepts like "missions" and "budgets" from AOTH, brain-hands architecture, and dynamic policy management. The discussion covers agentic commerce, accountability through tamper-resistant audit logs, and whether P(doom) is a real threat, emphasizing engineering ingenuity and societal learning from failures.

AI Safety Fact vs. Fiction | YC Paper Club

AI Safety Fact vs. Fiction | YC Paper Club

This episode of Paper Club addresses AI safety from a scientific perspective, contrasting it with recent media sensationalism. Stanford professors Clark Barrett and Mykel Kochenderfer discuss how automated reasoning can mathematically prove code security, how aircraft stress-testing methods are being adapted for language models, and the potential need for an NTSB-like agency for AI, highlighting the real-world engineering challenges and solutions for AI safety.

The AI Bill Is Too Damn High: Tokenomics 101 — Eugene Cheah, Featherless AI

The AI Bill Is Too Damn High: Tokenomics 101 — Eugene Cheah, Featherless AI

Eugene Cheah, CEO of Featherless AI, presents a four-step framework for cutting AI inference costs at scale: measure token spend by team and workload, right-size models through evals, classifiers, and fine-tuning, buy GPU capacity with an understanding of the speed-versus-cost tradeoff, and check whether organizational processes can absorb the extra output.

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