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

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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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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.

The PHP bug threatening the web. Plus, who's auditing AI?

The PHP bug threatening the web. Plus, who's auditing AI?

Security Intelligence panelists Nick, Don and Jim unpack three stories: two decades-old PHP flaws (PHP2Shell) that let attackers take over servers without logging in and were found by AI before patches landed, Anthropic's decision to give outside evaluators employee-like access and whether rival frontier labs should audit each other, and 333 Tor exit relays hijacked through a reused password. The discussion centers on visibility, patching under legacy constraints, secure by design, and the limits of trust in AI and anonymity networks.

Velocity Through Rigour: Then & Now • Cate Huston • Bug Bash EU 2026

Velocity Through Rigour: Then & Now • Cate Huston • Bug Bash EU 2026

Cate Huston, CTO at Twill and author of The Engineering Leader, shares how she rebuilt a no-code product into a production platform with a two-person team and agents, and argues that the management playbook for scaling people still applies to scaling AI agents. She covers the shift from f(n) to f(n × a) team scaling, the four management layers, guardrails and the roughly 30 percent of output she believes they require, shift-left practices, CI as a cost and quality lever, and why rigour is what turns speed into lasting velocity.

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

Building the Safety Layer for AI Agents

Building the Safety Layer for AI Agents

Raindrop is building the safety layer for AI agents, detecting and preventing production failures like hallucinations and failed tool calls. The company addresses the paradox that as agents become more capable, they introduce greater complexity and more ways for things to go wrong. Their new Raindrop Simulations product catches critical failures in CI before they ever reach production, democratizing a capability previously limited to large AI labs.

OpenAI researcher on agent swarms & recursive self-improvement

OpenAI researcher on agent swarms & recursive self-improvement

Noam Brown discusses OpenAI's multi-agent system, which solved a Millennium Prize Problem using 10,000 agents. He explores the implications of this massive parallelization for AI capabilities, the rapid progress in mathematics, and the accelerating pace of AI development. A significant portion of the conversation is dedicated to the critical challenges of AI alignment, particularly in light of incidents like the 'Hugging Face' event, and how to ensure models remain aligned during Recursive Self-Improvement (RSI) as their capabilities and operational horizons expand rapidly.

The End of TCP for AI Clusters — John Ousterhout, Stanford

The End of TCP for AI Clusters — John Ousterhout, Stanford

John Ousterhout argues that AI workloads are shifting from large data transfers to small, frequent coordination messages, making network latency (especially tail latency) a critical bottleneck. He explains why traditional protocols like TCP and RDMA are ill-suited due to sender-side congestion control and their byte-stream model. Ousterhout then introduces Homa, a new protocol designed from scratch with message-based communication, receiver-side congestion control, and switch priority queue utilization, demonstrating significant improvements in tail latency for AI applications.

Databricks’ Ali Ghodsi Never Wanted to Be CEO. Now He’s Among the Best

Databricks’ Ali Ghodsi Never Wanted to Be CEO. Now He’s Among the Best

Ali Ghodsi, CEO of Databricks, shares his unconventional rise to leadership, his unique CEO playbook centered on extreme focus on bottlenecks, and how he built Databricks' commercial engine. He details the strategy against Snowflake, the controversial creation of the Lakehouse category, and his views on conflict-averse leadership. Ghodsi also discusses AI's impact on enterprise and organizational design, offering insights from his personal coding experience and operating system.

OpenAI Built Its Own Chip in 9 Months

OpenAI Built Its Own Chip in 9 Months

Richard Ho, VP of Hardware at OpenAI, details the development of 'Jalapeño,' OpenAI's first custom AI accelerator. The chip aims to drastically lower inference costs and empower full stack control, achieving a remarkable 9-month tape-out by leveraging AI in chip design. Jalapeño's blank-slate architecture brings memory and compute closer, delivering both high throughput and low latency, outperforming competitors even without speculative decoding, and proving that AI can supercharge expert productivity in complex engineering fields.

The Watchdogs of AGI — Rune Kvist of AI Underwriting Company

The Watchdogs of AGI — Rune Kvist of AI Underwriting Company

AIUC co-founder Rune Kvist discusses their $40M Series A, emphasizing that AI adoption's biggest constraint is trust, not capability. He details AIUC-1, their quarterly-updated standard for agent security and reliability, and their expansion into model certification to bridge the trust gap between frontier labs and governments. Kvist highlights the critical role of standards and insurance (partnering with Lloyds of London) in quantifying and mitigating AI risks, from agent liability to national security concerns, and preparing for future challenges in robotics and multi-agent systems.

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