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Why We Rebuilt Microsoft Office From Scratch for AI Agents - Yijia Liang, Compound AI

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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 We Rebuilt Microsoft Office From Scratch for AI Agents - Yijia Liang, Compound AI

Why We Rebuilt Microsoft Office From Scratch for AI Agents - Yijia Liang, Compound AI

Yijia Liang, founder of Compound and former finance analyst, explains why personal agents succeeded and what finance needs beyond them: multiplayer agents with their own inboxes, a headless Office engine rebuilt with agents, and agent swarms that navigate massive data rooms and models while keeping every output grounded in citations.

Most of Your Docs Readers Are Now AI Agents - Hahnbee Lee, Mintlify & Lincoln Murr, Coinbase

Most of Your Docs Readers Are Now AI Agents - Hahnbee Lee, Mintlify & Lincoln Murr, Coinbase

Hahnbee Lee of Mintlify and Lincoln Murr of Coinbase discuss what changes when AI agents become the main users of software products. They cover why documentation now needs to be optimized for context windows and accuracy rather than design, how Coinbase is exposing its business through APIs, and how the x402 payment standard and crypto microtransactions let agents pay for services and gain greater autonomy.

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.

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

Playground in Prod - Optimising Agents in Production Environments — Samuel Colvin, Pydantic

Playground in Prod - Optimising Agents in Production Environments — Samuel Colvin, Pydantic

Samuel Colvin, creator of Pydantic, demonstrates a hands-on workflow for continuously optimizing AI agents in production. The session covers using Logfire for running evaluations, GEPA (Genetic Pareto) for autonomously evolving better prompts, and managed variables to deploy these improvements to live services without redeployment.

Language-Agnostic Detection of Bugs in Zero-Knowledge Proof Programs

Language-Agnostic Detection of Bugs in Zero-Knowledge Proof Programs

A summary of a talk on a new language-agnostic approach using abstract interpretation to find critical vulnerabilities in Zero-Knowledge Proof (ZKP) programs by modeling and detecting mismatches between prover computations and verifier constraints.

Vibe Engineering Effect Apps — Michael Arnaldi, Effectful

Vibe Engineering Effect Apps — Michael Arnaldi, Effectful

A practical guide on improving LLM coding agent performance by giving them direct access to a library's source code. The session demonstrates cloning the Effect repository to extract patterns and guide the agent in building a type-safe application from scratch.

Everything You Need To Know About Agent Observability — Danny Gollapalli and Ben Hylak, Raindrop

Everything You Need To Know About Agent Observability — Danny Gollapalli and Ben Hylak, Raindrop

Agent failures are unlike traditional software failures. This workshop provides a practical framework for monitoring production agents, moving beyond evals to real-world observability by using explicit signals (errors, latency) and implicit signals (user frustration, refusals, self-diagnostics) to catch regressions and understand agent behavior.

Beyond the Basics: Production Serverless Patterns for Extreme Scale • Janak Agarwal • GOTO 2025

Beyond the Basics: Production Serverless Patterns for Extreme Scale • Janak Agarwal • GOTO 2025

This presentation by Janak Agarwal from AWS provides a deep dive into scaling serverless applications for mission-critical, high-traffic workloads. It explores AWS Lambda's rapid scaling capabilities for handling extreme traffic bursts and introduces advanced patterns like Provisioned Concurrency for cost optimization during steady-state operations.

AI That Designs Its Own Chips: Ricursive's Anna Goldie and Azalia Mirhoseini

AI That Designs Its Own Chips: Ricursive's Anna Goldie and Azalia Mirhoseini

Co-founders of Ricursive Intelligence, Anna Goldie and Azalia Mirhoseini, outline their thesis that AI should design the chips that train AI. They detail their three-phase plan to first accelerate chip design with AI tools 100,000x faster than current software, then become a 'design-less' platform for custom silicon, and finally achieve vertical integration by building their own chips and models.

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