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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

Assisted Algorithms

Assisted Algorithms

Yuri Gurevich argues that algorithms are inherently deterministic and that probabilistic and quantum algorithms work by delegating non-algorithmic steps such as coin flips and quantum measurements to external oracles or assistants. He presents a theory of assisted algorithms with revised abstract state machine postulates, using a primality test and Grover's search as running examples.

How to Know When an AI Agent Can Work Unsupervised — Evan Kormos, Coinbase

How to Know When an AI Agent Can Work Unsupervised — Evan Kormos, Coinbase

Evan Kormos of Coinbase presents a practical framework for bringing AI agents into policy-driven, regulated workflows. He models processes as DAGs with loops inside each node, argues that a loop is only as trustworthy as its check, and shows how evals and traces create a new confidence mechanism. Agents then move through supervised, sampled, and exception-only stages.

Aegon: Self-Auditable Key Transparency

Aegon: Self-Auditable Key Transparency

This talk presents Aegon, a self-auditable key transparency scheme designed for global-scale encrypted messaging. It builds on Iron Dict's polynomial commitment approach to remove per-epoch global work, shards the dictionary for horizontal scaling, and uses proof caching to bound storage. The result is constant-size audit proofs that a lightweight client can verify itself over an entire history, rather than relying on external auditors.

Smarter Models Killed the Harness. Here's What's Left — Jediah Katz, SpaceXAI

Smarter Models Killed the Harness. Here's What's Left — Jediah Katz, SpaceXAI

Jedidiah Katz, longtime agent-harness engineer at Cursor and now at SpaceX, argues that fancy harnesses no longer add coding intelligence because models absorb those skills through a data flywheel. He contends the harness matters more than ever: it unlocks new frontiers like Grokbot and owns infrastructure, trust and security, and efficiency. He closes with predictions, including that 90% of agent work will run off the laptop within six months.

Why a $1M Startup Wasn’t Worth Building in the Age of AI

Why a $1M Startup Wasn’t Worth Building in the Age of AI

Jean-Denis Greze, Town's CEO and former Plaid CTO, explains why he shut down a nearly $1M-revenue AI tax business for SMBs and pivoted to Town, an AI assistant that works from email and calendar. The conversation with a16z's Alex Rampell covers founder decision-making under time pressure, product-market fit, the consumer-grade enterprise thesis, token allocation as a business model, neutrality versus frontier labs, horizontal versus vertical strategy, uneven AI diffusion, and how agents may reshape commerce and advertising.

The Model Swap Workshop: GPT-5.5 vs Kimi vs DeepSeek vs Mistral

The Model Swap Workshop: GPT-5.5 vs Kimi vs DeepSeek vs Mistral

Pamela Fox runs a guided workshop comparing GPT-5.5, Kimi K2.6, DeepSeek V4 Flash, and Mistral Large 3 on Microsoft Foundry, showing that benchmarks cannot predict how a model will behave in a specific application. The talk covers counting, RAG groundedness, vision, multilingual failures, tool calling, parallel tool calls in agents, string-check and LLM-as-a-judge evals, Microsoft's ASSERT tool for generating test sets, and DSPy prompt optimization that lets a cheap model satisfy a spec.

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