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How Revolut Cut AI Coding Costs 90% With Self-Hosted Open Models - David Abitbol, Revolut

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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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How Revolut Cut AI Coding Costs 90% With Self-Hosted Open Models - David Abitbol, Revolut

How Revolut Cut AI Coding Costs 90% With Self-Hosted Open Models - David Abitbol, Revolut

David Abitbol, who leads AI operations at Revolut, shares what AI tools did to 2,000 engineers' coding time and lead time. The main finding is that self-hosting open-source GLM models delivered about 60-70% faster delivery at roughly a tenth of frontier-lab costs. He covers the GPU sizing, latency targets, tuning that made the same hardware 88% faster, and how an EU-regulated bank keeps the setup compliant.

Grokking Software Architecture • Matt Erman & Luca Mezzalira • GOTO 2026

Grokking Software Architecture • Matt Erman & Luca Mezzalira • GOTO 2026

Matt Erman, author of Grokking Software Architecture, joins Luca Mezzalira to discuss why engineering fundamentals matter more in the AI era, how architects can act as clarity engineers, a five-step architectural thinking process, Architecture Decision Records, and the risks of letting AI generate systems without human judgment.

We Trust AI Agents That Break Every Day - Shy Ruparel, Temporal

We Trust AI Agents That Break Every Day - Shy Ruparel, Temporal

Shy Ruparel of Temporal breaks down the four gaps keeping AI agents from production, drawing on survey data from 554 engineers, and demos a crash-resilient $2,500 payout workflow built on durable execution with a local model.

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

How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage) — Eyal Blum, Figma

How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage) — Eyal Blum, Figma

Figma's internal AI agent adoption journey faces challenges like reduced developer agency, skepticism from senior engineers, and communication inefficiency. Solutions include investing in verification, using a testing pyramid for agent review, prioritizing detailed planning over prompting, engaging skeptics to build AI safety roadmaps, and implementing attention-aware communication by clearly marking AI-generated content.

From AI-Assisted to AI-Native: Building a Frontier Development Team — Clare Liguori, AWS

From AI-Assisted to AI-Native: Building a Frontier Development Team — Clare Liguori, AWS

Clare Liguori, a Senior Principal Engineer at AWS, reveals how Amazon teams are achieving 4.5x to 10x productivity gains through 'frontier development' with AI coding assistants like Kuro. She emphasizes that success isn't about the tools, but about fundamentally changing how engineers work, outlining five core habits: investing in agent context, embracing an initial slowdown for long-term speedup, feeding agents comprehensive instructions instead of micromanaging, making design intent explicit before coding, and shifting left with local, deterministic testing. Liguori also addresses new challenges like burnout and decision-making becoming the primary bottleneck in highly accelerated development cycles.

AI Evals for Cross-Functional Teams — Nachiket Paranjape & Swaroop Chitlur Haridas, DoorDash

AI Evals for Cross-Functional Teams — Nachiket Paranjape & Swaroop Chitlur Haridas, DoorDash

DoorDash's GenAI platform team transformed evaluation from an engineering-centric task into a cross-functional workflow. By adopting an API-first strategy, they empowered non-engineers like Strategy & Operations to "vibe code" their own annotation UIs using coding agents and self-serve calibrate LLM judge prompts. This approach dramatically reduced annotation costs, accelerated iteration, and fostered broader organizational ownership of AI quality.

Building uReview, Uber’s Multi-Agent Code Review Engine — Will Bond & Ameya Ketkar, Uber

Building uReview, Uber’s Multi-Agent Code Review Engine — Will Bond & Ameya Ketkar, Uber

Uber's Will Bond and Ameya Ketkar present uReview, an in-house automated code review system developed to combat rising review times (3 to 9 hours for first review). They detail why Uber built it over buying—due to Phabricator, agentic SDLC integration, and distributed ownership—and its architecture, including comment deduplication. The presentation highlights their iterative approach using advanced observability (sentiment, addressal rate, agent trajectory) to tune model performance, acknowledging that "the model never knows that it is wrong." They discuss empowering hundreds of teams with custom review agents and skills, sharing impressive results: 25,000 comments weekly, 67% addressal rate, and 60% cost reduction. Finally, they explore the evolving role of human engineers in an agentic SDLC, predicting an "expanded outer loop" focused on architecture and domain expertise rather than direct code review.

Why Top Founders Are Racing Into AI Infrastructure

Why Top Founders Are Racing Into AI Infrastructure

a16z's new Machine Age Fund addresses the unprecedented demand for AI infrastructure, shifting the bottleneck from models to the foundational hardware. The discussion highlights surging Hyperscaler CapEx, component supply crunch extending to 2028, and the exponential compute needs driven by reasoning and agents. It explores how AI turns engineering problems into capital/compute challenges, identifying opportunities for new infrastructure companies in chips, power, and data center redesign, and the emergence of experienced "systems founders" to rebuild the computing stack for this new era.

CLAUDE.md, AGENTS.md, Skills, Hooks and Subagents: A Field Guide to Steering AI Agents (Ep. 1022)

CLAUDE.md, AGENTS.md, Skills, Hooks and Subagents: A Field Guide to Steering AI Agents (Ep. 1022)

Episode 1022 dissects the crucial aspect of effectively steering AI agents by determining the optimal placement of instructions to ensure reliability and cost-efficiency. It explores seven distinct methods for instruction delivery, contrasting instructions as probabilities with hooks as guarantees, and highlights the industry-wide adoption of standards like `agents.md` and the importance of human-crafted guidance for superior agent performance.

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