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Why Humans Can't Keep Up With AI-Written Code - Tariq Shaukat, 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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Why Humans Can't Keep Up With AI-Written Code - Tariq Shaukat, Sonar

Why Humans Can't Keep Up With AI-Written Code - Tariq Shaukat, Sonar

Sonar CEO Tariq Shaukat argues that agents have made code abundant but confidence scarce, and draws on Toyota's quality-first factory principles to show how to build software factories enterprises can trust. He covers five hard problems that break agentic development and presents Sonar's code confidence formula: intent, guide rails, verification, and evidence.

From AI Slop to Software Factories ft. Dexter Horthy

From AI Slop to Software Factories ft. Dexter Horthy

A tandem bike ride through Amsterdam turns into a wide-ranging conversation with Dexter Horthy, co-founder of HumanLayer, on code no one understands, computer use, MCP design, browser agents, chat interfaces, and the infrastructure every team needs for agent-based software factories.

Crusoe CEO Chase Lochmiller: Turning Stranded Energy Into Intelligence

Crusoe CEO Chase Lochmiller: Turning Stranded Energy Into Intelligence

Chase Lochmiller, founder and CEO of Crusoe, discusses building a vertically integrated AI infrastructure company spanning energy, data centers, manufacturing, and managed AI cloud. Topics include leading a complex organization, the Bitcoin-to-AI pivot, energy-first compute, executive coaching, firing decisions, community pushback on data centers, and lessons from climbing Everest.

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

Codex, Behind the Harness — Dominik Kundel, OpenAI

Codex, Behind the Harness — Dominik Kundel, OpenAI

Once GPT 5.3 Codex Spark achieved 1000 tokens/sec on Cerebras, network latency superseded inference as the bottleneck for agents. This talk details how the Codex harness addresses this and other agentic challenges through innovations like WebSocket mode for stateful context, deferred tools for efficient context construction, robust sandboxing (Seatbelt, Bubblewrap, custom Windows solution), and an auto-review subagent to mitigate approval fatigue while ensuring security. It also covers structured actions via 'apply patch' for file edits, shell tools for system interaction, and sophisticated long-horizon goal management, with most distinct features exposed through the open Responses API.

Kavak's Playbook for Rebuilding a Company Around AI

Kavak's Playbook for Rebuilding a Company Around AI

Alejandro Maza Ayala, Chief Product & AI Officer at Kavak, details how the used-car marketplace transformed into an AI-native company. He explains the 'agent-per-customer' architecture, where individual agents handle 96% of customer interactions and 95% of transactions, outperforming human teams in sales (2.1x better conversion) and even acting as an 'AI CEO' that boosted profits by 50% in an experimental city. The discussion covers the need to redesign company structures, the importance of robust evaluations, and how a 'Jedi Academy' trains all employees, from executives to mechanics, to build and collaborate with AI agents. Ayala argues for 'creative destruction,' suggesting that true AI leverage comes from rebuilding organizations from the ground up, rather than incremental adoption, presenting a massive opportunity for new founders.

5 Best Practices for Building AI Agent Skills

5 Best Practices for Building AI Agent Skills

This video outlines five essential best practices for developing reliable, secure, and effective AI agent skills. It covers optimizing skill triggering through descriptive metadata, leveraging real-world domain expertise over generic LLM output, managing context windows efficiently by writing lean skills and using progressive disclosure, implementing deterministic logic with scripts for fragile operations, and critically vetting all skills for security vulnerabilities before deployment. These practices are crucial for professionals building robust agentic systems.

Multiplayer agentic engineering — Arjun Singh, Superconductor

Multiplayer agentic engineering — Arjun Singh, Superconductor

Arjun Singh, co-founder of Superconductor, discusses the six crucial lessons learned from integrating AI agents into their software development workflow. He emphasizes the importance of building "multiplayer agentic engineering" systems where human teams and AI agents collaborate seamlessly, focusing on model agnosticism, pervasive agent interfaces, transparent work visibility, automated signal-to-code conversion (like their "meeting bot"), secure isolated cloud environments, and internal code-base benchmarking to optimize cost, quality, and speed.

Guide, Verify, Solve — Anirban Chatterjee, Sonar

Guide, Verify, Solve — Anirban Chatterjee, Sonar

Anirban Chatterjee discusses the emergence of "verification debt" in AI-driven software development, where AI-generated code introduces persistent quality issues despite initial productivity gains. He proposes a **zero-trust, multi-layered verification** approach, integrating it into an "Agent-Centric Development Cycle" (ACDC) to ensure code quality, security, and compliance by empowering AI agents with real-time feedback and remediation capabilities through tools like SonarQube, Gitarr, and Sonar Vortex.

Velocity Sickness: What Happens When Your Whole Team Gets 10x Faster — Matt Dailey, Ref.

Velocity Sickness: What Happens When Your Whole Team Gets 10x Faster — Matt Dailey, Ref.

Matt Dailey introduces "velocity sickness" – the stress of increased AI output without impact. He proposes shifting from ephemeral chat-based agent interactions to durable, shared documents as the "decision layer" to separate planning from implementation, enabling teams to own their code and prioritize ideas effectively.

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