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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

How Standard Kernel uses compound AI systems to optimize code and extract maximum GPU performance

How Standard Kernel uses compound AI systems to optimize code and extract maximum GPU performance

Chris from Standard Kernel explains how the company uses two autonomous optimization systems, one for whole workloads and one for individual CUDA kernels, to speed up AI inference. He walks through a roughly 2x vLLM speedup on Qwen with a CoreWeave team, details the kernel generation search and the correctness checks needed for frontier-model-written kernels, and presents case studies including 11x faster world models, faster time-series and LLM serving, and customer-driven requirements.

Brains vs Hands: How to Run AI Agents Safely in Production — Viren Baraiya

Brains vs Hands: How to Run AI Agents Safely in Production — Viren Baraiya

Viren Baraiya, CTO of Orkes and original creator of Netflix Conductor, explains why production agents behave like applications built from microservices. He argues for splitting the work between a non-deterministic LLM that plans and a deterministic, durable harness that executes, with approval gates, idempotent steps, and recorded side effects. He frames agent harnesses as late-bound sagas and demos an SRE remediation agent whose plan is compiled into a Conductor workflow.

We Mapped 115 Microservices for Our Coding Agents — Kamalakannan Nandagopal, Postman

We Mapped 115 Microservices for Our Coding Agents — Kamalakannan Nandagopal, Postman

Kamalakannan Nandagopal from Postman explains why coding agents struggle beyond a single repo and how an API context graph, grounded in code and production telemetry, gives agents system-wide awareness. The talk covers evals built from real PRs, wins on API discovery, redesign and impact assessment, a stale-graph failure, and a 21-page architecture review generated by the agent.

Beyond the Benchmark: How Parallel Web Systems Builds Reliable Web Agents

Beyond the Benchmark: How Parallel Web Systems Builds Reliable Web Agents

Parallel Web Systems engineer explains how the company moved beyond academic web-research benchmarks to a fully automated evaluation flywheel that samples production questions, generates reference answers with an oracle agent, judges with tool-equipped agents, and masks components to diagnose failures, along with the open challenges of evaluating subjective and infrastructure-dependent quality.

Turning Agent Memory Into Skills That Work — Will Lyon, Neo4j

Turning Agent Memory Into Skills That Work — Will Lyon, Neo4j

Neo4j's Will Lyon argues that embedding-based retrieval gives agents recall but not actionable knowledge. He presents a context graph model of agent memory with short-term, long-term, and reasoning memory, then shows how to distill grounded, governed skills from that graph using APE typed execution graphs, with grounding, coverage, and coherence checks, staleness alerts, and a live demo of the Neo4j Agent Memory Service building a patient-intake skill.

How Cline Embraces an Open-Weights Model Ecosystem for AI Agents

How Cline Embraces an Open-Weights Model Ecosystem for AI Agents

Cline's CEO explains why open-weight models are rapidly taking over coding-agent workloads, and why the agent harness, not raw model capability, determines reliability. The talk covers model-specific tuning and failure handling, context and token optimization, self-improving harness loops, an SDK-first and UI-agnostic architecture, interactive evaluation built from real-world usage data, and production inference challenges such as KV cache hit rates.

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