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How Anthropic Merges 30,000 Code Changes a Day - Manish Kapur, Sonar & Addy Osmani, Anthropic

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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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Why a One-Line Change Can Break Your Whole Stack - Cole Gannaway, Qodo

Why a One-Line Change Can Break Your Whole Stack - Cole Gannaway, Qodo

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How Anthropic Merges 30,000 Code Changes a Day - Manish Kapur, Sonar & Addy Osmani, Anthropic

How Anthropic Merges 30,000 Code Changes a Day - Manish Kapur, Sonar & Addy Osmani, Anthropic

Manish Kapur of Sonar and Addy Osmani of Anthropic discuss why coding agents are opening pull requests faster than teams can review them, how light and dark software factories differ, how Anthropic scaled to around 30,000 merged changes a day with Claude doing first-pass review backed by verification evidence, new State of Code survey data, and why every loop in the factory needs independent, multi-layered verification.

AI That Hunts Logic Bugs Before Attackers Do - Taylor Luttrell-Williams, Sonar

AI That Hunts Logic Bugs Before Attackers Do - Taylor Luttrell-Williams, Sonar

Sonar's Taylor Luttrell-Williams introduces SonarQube Hunter Agent, an AI security agent that finds intent-level flaws such as broken access control, business logic gaps, and authentication holes that pattern-based SAST cannot detect. The talk covers its multi-agent pipeline with adversarial validation, Sonar's join to the OpenAI Daybreak Cyber Partner Program, and a live demo that surfaced 46 issues in a Python app, including a self-promotion to admin and an MFA bypass.

Technology

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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.

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.

Recent Post

Your company brain will leak secrets: how we stopped it for big banks — Tanmai Gopal, PromptQL

Your company brain will leak secrets: how we stopped it for big banks — Tanmai Gopal, PromptQL

Tanmai Gopal, co-founder of PromptQL and creator of Hasura GraphQL, shares insights on building secure and effective 'company brains' for AI agents. He highlights the surprising metric of a continuously rising edit count as a sign of health, dismisses common pitfalls like siloed knowledge, and proposes a human-in-the-loop, unified wiki approach with granular access control and dynamic credential injection to prevent leaks and foster collaborative knowledge creation.

Paul Graham On Startups, Ambition, and Great Founders

Paul Graham On Startups, Ambition, and Great Founders

Paul Graham, co-founder of Y Combinator, provides insights into the evolution of startups, emphasizing the increasing ambition of founders tackling "frighteningly ambitious ideas." He reveals that fear of failure, not wealth, primarily motivates entrepreneurs and defines the crucial trait of a "formidable" founder. Graham also shares his unique perspective on the unexpected development trajectory of AI, describing AGI as a "jagged frontier," and reflects on YC's consistent core principles over 21 years.

Why AI Agents Could Finally Reinvent the Credit Card

Why AI Agents Could Finally Reinvent the Credit Card

Max Levchin and Alex Rampell dive into 25 years of fintech evolution, from the surprising persistence of the card interface and the origins of Affirm's unique 'real 0% APR' model and negative customer acquisition cost, to the future prospects of AI-driven agentic payments versus shopping.

Why AI Agents Fail

Why AI Agents Fail

Abi Aryan discusses the shift from MLOps to LLMOps, framing generative AI deployment as a distributed systems challenge. She highlights the critical role of FinOps due to high model costs, the need for custom tooling over generic agent frameworks, and the importance of a 'decision layer' for agent autonomy. Observability is re-conceptualized as anomaly detection, and she points to dynamic RL environments and strong data engineering as future research priorities and essential skills for AI engineers.

Redefining Chip Architecture with Arm CEO Rene Haas

Redefining Chip Architecture with Arm CEO Rene Haas

Arm CEO Rene Haas discusses Arm's evolution from IP licensing to producing physical chips, the transformative role of AI in accelerating chip design and verification, and the critical supply chain bottlenecks impacting data center build-out. He also covers SoftBank's strategic capital deployment, the immense opportunities for Arm in the burgeoning robotics sector, and emphasizes why power-efficient CPUs remain fundamental to all AI workloads, from data centers to edge devices.

AI Internals Are Weird — Tom McGrath

AI Internals Are Weird — Tom McGrath

Tom McGrath, Chief Scientist at Goodfire, delves into neural network interpretability, intentional design, and the geometry of learned concepts. The discussion covers how models learn modularity and algorithmic structures like arithmetic, the limitations of current interpretability tools like sparse autoencoders, and the implications for alignment, reward hacking, and multi-agent systems.

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