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When Running Your Own AI Models Beats Paying for APIs - Steven Latré, imec

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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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THE $800B COMPUTE TRAP

THE $800B COMPUTE TRAP

Ben Lorica and Evangelos Simoudis work through Anthropic's S-1 filing, weighing its striking growth and improving unit economics against compute commitments, falling prices from open-weight models, and dependence on coding. They then examine SpaceX's emerging neocloud business and close with a debate over proposed frontier AI regulation, from self-improvement limits to independent testing and incident disclosure.

When Running Your Own AI Models Beats Paying for APIs - Steven Latré, imec

When Running Your Own AI Models Beats Paying for APIs - Steven Latré, imec

Steven Latré, who leads AI at the semiconductor research center imec, presents a benchmarking study of what it really costs to move long-running coding agents from commercial APIs to self-hosted GPUs. Across thousands of SWE-bench Pro runs, the study finds that hardware costs rise in steps rather than smoothly, that concurrency limits matter more than raw accuracy, that break-even GPU utilization ranges from about 89% on small systems to about 15% on 8x B200 machines, and that harness choice can halve GPU time for the same results.

Voice AI Where One Wrong Question Breaks the Rules - Shiv Indap, Upstart & Brooke Hopkins, Coval

Voice AI Where One Wrong Question Breaks the Rules - Shiv Indap, Upstart & Brooke Hopkins, Coval

Shiv Indap of Upstart and Brooke Hopkins of Coval walk through automating loan verification calls in a heavily regulated lending environment. They cover the compliance traps that shape the agent's behavior, moving human review beyond a Google Sheet, and raising LLM judge agreement with human reviewers from 33% to 92%. The talk then lays out a continuous loop of live monitoring, human review queues, simulation-based testing of fixes, and self-healing agents, with humans signing off on every change.

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

The Golden Age of AI Engineering — Alexander Embiricos & Romain Huet & Peter Steinberger, OpenAI

The Golden Age of AI Engineering — Alexander Embiricos & Romain Huet & Peter Steinberger, OpenAI

OpenAI's Romain Huet and Alexander Embiricos, joined by Peter Steinberger, outline the explosive progress of Codex at Dev Day 2024. They highlight the shift from manual coding to managing autonomous agents, enabled by rapid model iteration (every 6 weeks), open-source developer tools, and optimizations for cost-effectiveness ($1/M input tokens) and blazing inference speed (750 tokens/sec). The discussion centers on empowering AI engineers, not replacing them, by evolving agent capabilities, fostering an open ecosystem, and addressing future challenges like seamless local/cloud task execution and human attention as the new bottleneck in agent orchestration.

Convergence Analysis for Fast High-Order ODE Solvers in Diffusion Probabilistic Models

Convergence Analysis for Fast High-Order ODE Solvers in Diffusion Probabilistic Models

This talk presents a rigorous convergence analysis for fast, high-order ODE solvers in diffusion probabilistic models. It quantifies the interplay between the L2 score matching error (from neural network training) and the discretization error of Runge-Kutta integrators, establishing total variation distance bounds that only require C^2 regularity for the learned score functions. Numerical experiments validate the theoretical rates and the C^2 assumption on benchmark datasets. This work provides a solid theoretical foundation for designing efficient and accurate deterministic diffusion samplers.

See How Customers Actually Use Your Product

See How Customers Actually Use Your Product

Founders often make the mistake of relying on aggregate user metrics like DAU, which obscure how individuals truly use a product. This video introduces the 'dot plot,' a powerful visualization tool mapping individual user activity over time. It reveals hidden patterns, helps identify retention issues, and pinpoints features driving engagement, offering deeper insights than traditional metrics and proving invaluable for both B2C and B2B product health analysis.

The Green Shift: Transitioning .NET Services Across Architectures • Sara Bergman • GOTO 2025

The Green Shift: Transitioning .NET Services Across Architectures • Sara Bergman • GOTO 2025

Sara Bergman presents a practical guide on migrating .NET services to ARM-based architectures to achieve significant energy efficiency, cost savings, and performance per watt. She details the advantages of RISC over CISC, offers real-world examples, and provides a step-by-step .NET migration journey, emphasizing the importance of staying current with .NET, leveraging compilers, and strategically integrating ARM into development and deployment pipelines.

Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production

Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production

This video tackles the overwhelming choice of agentic AI frameworks by categorizing projects into five types: linear workflows, autonomous multi-agent systems, role-based AI, production orchestration, and rapid prototyping. It details each type with examples and recommends specific frameworks like LangChain, AutoGen, and CrewAI, guiding developers to select the optimal tool based on their system design and real-world needs.

Travel Through the Lens of AI with with Booking.com CEO Glenn Fogel

Travel Through the Lens of AI with with Booking.com CEO Glenn Fogel

Booking Holdings CEO Glenn Fogel discusses his journey from Priceline's early struggles to leading a global travel giant. He details Booking's strategic adoption of AI for agentic travel planning and customer service, exemplified by Priceline's 'Penny,' and its significant capital investment in technology. Fogel emphasizes continuous innovation over "moats" and shares insights on AI's impact on job displacement, advocating for proactive employee upskilling to navigate the rapidly changing technological landscape.

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