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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

Why Bigger Context Windows Won't Save Your Agent — Elizabeth Fuentes Leone, AWS

Why Bigger Context Windows Won't Save Your Agent — Elizabeth Fuentes Leone, AWS

Elizabeth Fuentes Leone, developer advocate at AWS, explains why agents break when tools return huge outputs, why bigger context windows do not fix the problem because models lose the middle of long contexts, and how four context engineering strategies (externalize, select, compress, isolate) can be implemented with the open-source Strands Agents framework.

AI Hackers Are Faster Than Your Pen Test — Eli Cohen, Snyk

AI Hackers Are Faster Than Your Pen Test — Eli Cohen, Snyk

Eli Cohen of Snyk argues that AI-generated code and AI-powered attackers have broken the traditional security model. He calls for continuous offensive security built on AI pentesting on every code change, agent red teaming, and a multi-agent system that validates exploits. He compares SAST, DAST, and human pentesting, and closes with four questions to ask any AI pentesting vendor.

Stop Overpaying for Intelligence | DevDay 2026

Stop Overpaying for Intelligence | DevDay 2026

A practical session on cutting AI costs without sacrificing quality. The speakers argue for measuring cost per completed task rather than per-token list prices, show how to right-size models with accuracy-versus-cost Pareto analysis, and walk through four levers: prompt caching, programmatic tool calling, reasoning effort, and batch or flex processing. Customer case studies from Perplexity, Notion, Clio, and Blitzy ground the advice.

Higgsfield - Building Agents That Provision Their Own Compute | DevDay 2026

Higgsfield - Building Agents That Provision Their Own Compute | DevDay 2026

Alex Mashrabov, CEO of Higgsfield, explains how agentic systems choose among models and provision their own GPU infrastructure to deliver consistent, multi-step creative media. He covers the trade-offs between speed, controllability, and dedicated deployment, and shares the 10-hour AI-generated live stream as an example of what becomes possible for small teams.

Stack Overflow - From Idea to Product | DevDay 2026

Stack Overflow - From Idea to Product | DevDay 2026

A VP of Product at Stack Overflow explains how a small team used OpenAI Codex to build two very different products: the enterprise knowledge intelligence layer Stack Internal and the experimental agent-first platform Stack Overflow for Agents. The discussion covers how to tune guardrails to product risk, how archetypes simplify multi-source enterprise ingestion, and how a community built for humans can be extended so agents both consume and contribute knowledge.

The Software Factory: From Bug Report to Production Code — Davis Palmie, Factory

The Software Factory: From Bug Report to Production Code — Davis Palmie, Factory

Davis Palmie of Factory explains the software factory, a system of agents that turns bug reports and user feedback into production code. He covers leadership concerns about cost, model choice, engineering roles, and org structure, argues that coding was never the bottleneck, and lays out principles for a factory: model agnosticism, sovereign deployment, and SDLC-wide integration. He also describes eight pillars of agent readiness, starting with narrow use cases like incident triage, and closes with why outcome metrics should replace token counts.

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