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If Your AI Scores 100%, Your Eval Is Wrong - Ramakrishnan Lokanathan, eslams.com

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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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If Your AI Scores 100%, Your Eval Is Wrong - Ramakrishnan Lokanathan, eslams.com

If Your AI Scores 100%, Your Eval Is Wrong - Ramakrishnan Lokanathan, eslams.com

Ramakrishnan Lokanathan argues that AI evals are broken because they are overcomplicated, and lays out a practical framework for building them for high-stakes applications: define the most ambitious version of success, test every step of a workflow, combine automated checks with human review and adversarial tests, guard against regressions, and turn real user failures into tests. He also introduces eSlams, an open-source package where models compete in games like chess, Go, and poker as a window into how they reason.

Your AI Agent Wants Your Data. Here's How to Lock It Down - Jeff Smith, Oracle

Your AI Agent Wants Your Data. Here's How to Lock It Down - Jeff Smith, Oracle

Oracle's Jeff Smith explains how to let agents query enterprise databases safely through an MCP server, covering reports exposed as vetted tools, the risks of natural language to SQL, performance tuning on read-only standbys, schema annotations, and deep data security enforced at the database layer.

Everyone Has the Same AI Models. Here's What Sets Agents Apart - Richmond Alake, Oracle

Everyone Has the Same AI Models. Here's What Sets Agents Apart - Richmond Alake, Oracle

Richmond Alake argues that after the control plane, agent architectures need an intelligence plane covering what an agent knows, remembers, is allowed to see, and how it improves. He draws on the WWII Midway codebreaking story to separate information from intelligence, then demos a governed agent using an ontology and semantic layer that answers correctly about 99% of the time with far fewer tokens than a naive agent. The talk also covers Oracle's agent memory package, deep data security, private inference, and fine-tuned decision models for memory routing.

Technology

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Why Founders Are Betting Big on Factories, Mines, and Energy

Why Founders Are Betting Big on Factories, Mines, and Energy

Erin Price-Wright and Connor Love unpack the AD50 and the rebuilding of America's industrial base, covering how defense demand is financing manufacturing capacity for commercial markets, how AI is reshaping factory economics, how a new generation of founders applies lessons from SpaceX, Tesla, Anduril, and Palantir to mining, energy, and manufacturing, and why private capital is now willing to fund physical infrastructure before government demand is guaranteed.

Mark Cuban’s Plan to Make Healthcare Affordable

Mark Cuban’s Plan to Make Healthcare Affordable

Mark Cuban and Julie Yoo discuss how transparency, direct contracting, and AI could reshape healthcare economics, from Cost Plus Drugs and modular pod manufacturing to personalized medicine, the future of physicians, and advice for founders who want to use AI to learn rather than to stop thinking.

Exploits Now Beat Patches: Open Source Security in the AI Era - Patrick Smyth, Chainguard

Exploits Now Beat Patches: Open Source Security in the AI Era - Patrick Smyth, Chainguard

Patrick Smyth of Chainguard examines how frontier AI has compressed the time between vulnerability disclosure and exploitation, leaving 30-day remediation cycles obsolete. The talk covers AI-assisted attacks, waves of supply chain malware, four attack surfaces spanning malware, known CVEs, zero days, and side doors like CI/CD, developer laptops, and agent skills, and practical defenses including cooldown periods, faster CVE remediation, and signing artifacts with SLSA and Sigstore.

Recent Post

The Moonshot Mindset with Adam Savage and Sergey Brin: The Moonshot Podcast

The Moonshot Mindset with Adam Savage and Sergey Brin: The Moonshot Podcast

Astro Teller, Captain of Moonshots at X, and Google co-founder Sergey Brin discuss the philosophy of radical innovation, X's unique culture of embracing failure and pressure testing ideas, and their reflections on past moonshots like Google Glass, Wing, and Waymo. They delve into the exponential nature of AI, its historical context, and the challenges of anticipating its future impact, while also highlighting crucial unsolved "white whale" moonshots for humanity, including education, battery technology, and redefining the intrinsic value of work beyond monetary compensation.

Task Fidelity Scaling Laws — Kobie Crawdord, Snorkel

Task Fidelity Scaling Laws — Kobie Crawdord, Snorkel

An experiment by Snorkel AI reveals that in agentic AI training, the quality of tasks is paramount. Using the same model and compute, fine-tuning on high-quality tasks yielded a 6% performance improvement, a 5x greater uplift compared to the 1% gain from low-quality tasks. The key difference lies in the nature of the tasks: high-quality tasks are genuinely harder, featuring more tool calls and cleaner failure modes that provide a meaningful learning signal. In contrast, low-quality tasks often fail due to ambiguity and environmental noise, hindering effective model improvement.

GitHub’s Agent Era: 14x Commits, 200M Developers, Copilot’s Next Act — Kyle Daigle

GitHub’s Agent Era: 14x Commits, 200M Developers, Copilot’s Next Act — Kyle Daigle

GitHub COO Kyle Daigle discusses the new era of AI agents from the inside. He covers how he uses AI for leadership, the shift from "mega-skills" to "micro-skills," and how GitHub is navigating a 14x growth in commits. The conversation goes deep on the evolution of Copilot, the future of PRs in an agent-driven world, the challenges of scaling, and Microsoft's vision for an ambient AI operating system.

Power agents with full context of your experiments and traces with W&B MCP server

Power agents with full context of your experiments and traces with W&B MCP server

The W&B Model Context Protocol (MCP) is a hosted endpoint that enables AI agents to intelligently interact with all Weights & Biases data, including runs, traces, evaluations, and reports. It features discovery tools for smart queries, automated analysis for comparing experiments and identifying regressions, and seamless integration with IDEs, coding agents, and chat interfaces like Mistral AI for streamlined ML workflows and on-the-go reporting.

He Raised $70M to Cure Every Disease With AI

He Raised $70M to Cure Every Disease With AI

Samuel Rodriques, founder of Edison Scientific, shares his journey from physics to building an AI scientist named Kosmos. He discusses how AI agents are already making novel discoveries, including a potential cure for blindness, and are poised to revolutionize drug discovery. The conversation dives into AI's strengths in high-throughput reasoning, the critical bottlenecks in clinical trials, proposed reforms for the US medical system, and whether human scientists will still be needed in an age of hyper-intelligent AI.

Frontier AI at Home — Alex Cheema, EXO Labs

Frontier AI at Home — Alex Cheema, EXO Labs

Alex Cheema from EXO Labs explores the path to a 100x improvement in the price-performance of running frontier AI models locally. The talk covers full-stack optimization strategies, including kernel fusion for a 30% performance boost, RDMA for scalable tensor parallelism, and a novel approach of splitting prefill and decode phases across heterogeneous hardware (e.g., an RTX GPU and Mac Studios) to significantly speed up large-prompt inference.

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