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What Is AI Code Refactoring? Agentic AI & Safe Code Changes

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Machine Learning

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Frontier results, on device - RL Nabors, Arize

Frontier results, on device - RL Nabors, Arize

RL Nabors discusses the significant costs associated with using frontier AI models, covering security, latency, and financial implications. She introduces a framework for right-sizing AI solutions by leveraging smaller, task-specific models and Small Language Models (SLMs). The framework details how to prove task feasibility, establish success criteria with golden datasets, conduct capability evaluations (using tools like Phoenix), and select the most appropriate "Small And Good Enough" (SAGE) model. Nabors further demonstrates how prompt engineering, particularly few-shot prompting, and post-processing can close performance gaps with larger models, while advocating for continuous regression evaluations to maintain performance integrity. The overarching message is to "prototype big, deploy small" to optimize AI deployments.

Research to Reality: Bringing Frontier ML Research to Production - Vaidas Razgaitis, Higharc

Research to Reality: Bringing Frontier ML Research to Production - Vaidas Razgaitis, Higharc

Vaidas Razgaitis, Senior Research Engineer at Higharc, shares three tactical tips to accelerate the transition of novel AI/ML research into production-ready features. He emphasizes addressing the critical handoff challenge between ML researchers and software engineers through structured documentation (Research Prototype Taxonomy Document), a well-organized monorepo utilizing decoupled microservices, and a systematic approach to code decomposition and PR review. These strategies aim to improve legibility, maintainability, and delivery speed for ML-driven products.

Uncertainty-Guided Data Augmentation for Engineers | Deep Dive - Yongmin Kwon

Uncertainty-Guided Data Augmentation for Engineers | Deep Dive - Yongmin Kwon

This session details a data-efficient method for training engineering surrogate models by using uncertainty quantification (UQ) to guide geometric data augmentation. Instead of random deformations, the approach lets the deep ensemble model identify its own knowledge gaps (epistemic uncertainty), then uses Free-Form Deformation (FFD) to generate new shapes specifically in those uncertain regions. This ensures every expensive simulation run yields maximally informative data, significantly improving model accuracy for a fixed computational budget across domains like structural mechanics and aerodynamics.

Artificial Intelligence

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What Is AI Code Refactoring? Agentic AI & Safe Code Changes

What Is AI Code Refactoring? Agentic AI & Safe Code Changes

This video explores AI code refactoring, differentiating between inline and autonomous agentic approaches. It highlights how AI can leverage pattern recognition for tasks like improving readability or reducing duplication, thereby addressing technical debt. A key focus is on the safety guardrails, detailing a multi-step, human-in-the-loop process involving planning, searching, reporting, human approval, patching, and verification through testing, ensuring AI-driven changes are safe for production and can integrate into CI/CD pipelines.

AI Gave You A Promotion: Why AI Isn’t Replacing Jobs

AI Gave You A Promotion: Why AI Isn’t Replacing Jobs

AI isn't replacing jobs, but profoundly changing them. Jeff Crume introduces a 'diamond' model to explain how AI shifts job roles to higher-value work, leading to a 'promotion' for employees. This transformation, driven by Jevons Paradox, demands new skills like flexibility, curiosity, and critical thinking, ultimately fostering overall growth and innovation in the workforce.

a16z Goes Global: Why American Tech Must Lead the World

a16z Goes Global: Why American Tech Must Lead the World

This discussion explores a16z's expanding international strategy, emphasizing technology's pivotal role in economic growth and national security. The panel delves into why America's tech leadership is crucial globally, how AI is redefining government-private sector relationships, and the drive for countries to adopt frontier technologies while building local innovation ecosystems. Key topics include AI infrastructure, cybersecurity, defense tech, global startup expansion, and the elements of enduring tech ecosystems, highlighting trusted partnerships and the importance of Western technology.

Technology

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Are Your Tests Slowing You Down? • Trisha Gee • GOTO 2025

Are Your Tests Slowing You Down? • Trisha Gee • GOTO 2025

Trisha Gee delivers a compelling talk on Developer Productivity Engineering (DPE) for testing, dissecting common pain points in writing, troubleshooting, and running tests. She advocates for strategic use of IDEs, advanced tooling like build caches and predictive test selection (leveraging ML), and a disciplined approach to test design to overcome these challenges, emphasizing that good tests serve as crucial living documentation.

The new post-quantum cryptography executive order. Plus: What is Q-Day, really?

The new post-quantum cryptography executive order. Plus: What is Q-Day, really?

This episode delves into Q-Day, the anticipated future when quantum computers can break public key cryptography, and the U.S. Executive Order accelerating the transition to post-quantum cryptography. Experts discuss why Q-Day is a gradual process rather than a sudden event, the critical importance of "crypto-agility" as a long-term strategy, and the necessity for organizations to begin immediate discovery and planning to secure data against "collect now, decrypt later" threats. The discussion also touches upon the broader, transformative benefits of quantum computing beyond just security.

Plenary Talk 3​: Challenges and research opportunities for global hyperscale services

Plenary Talk 3​: Challenges and research opportunities for global hyperscale services

Jim Kleewein's talk outlines the immense challenges and critical research opportunities in building and operating global hyperscale services like Microsoft 365 and Azure. He emphasizes that at this scale, traditional approaches fail, necessitating a "new golden age of applied research" across areas like continuous availability, data management, security, and sustainability. Kleewein also discusses AI's powerful but limited role, stressing the ongoing need for human expertise, and highlights the ethical imperative to prevent failures that can have life-or-death consequences.

Recent Post

Designing AI Agents for the Complex Realities of Healthcare

Designing AI Agents for the Complex Realities of Healthcare

Dr. Sarah Gebauer presents a clinical framework for deploying AI agents in healthcare, drawing a powerful analogy between AI agents and medical residents. She outlines the critical risks, validation strategies, and post-deployment monitoring required to make agents useful, safe, and credible in high-stakes clinical environments.

Sub-Population Identification of Multi-morbidity in Sub-Saharan African Populations

Sub-Population Identification of Multi-morbidity in Sub-Saharan African Populations

This talk explores a data science approach to identifying high-risk subpopulations for multimorbidity in sub-Saharan Africa, contrasting traditional hypothesis-driven analysis with a discovery-driven method that automatically uncovers non-obvious patterns in health data.

Why experts writing AI evals is creating the fastest-growing companies in history | Brendan Foody

Why experts writing AI evals is creating the fastest-growing companies in history | Brendan Foody

Brendan Foody, CEO of Mercor, discusses the critical role of AI evaluations (evals) in model improvement, detailing how his company achieved unprecedented growth by supplying high-skilled experts to top AI labs. He explores the shift to Reinforcement Learning from AI Feedback (RLAIF), the future of work in an AI-driven economy, and why he believes the path to AGI is paved with better evals, not just more data.

Upwork's Radical Bet on Reinforcement Learning: Building RLEF from Scratch | Andrew Rabinovich (CTO)

Upwork's Radical Bet on Reinforcement Learning: Building RLEF from Scratch | Andrew Rabinovich (CTO)

Andrew Rabinovich, CTO and Head of AI at Upwork, details their strategy for building AI agents for digital work. He introduces a custom reinforcement learning approach called RLEF (Reinforcement Learning from Experience), explains why digital work marketplaces are ideal training grounds, and shares his vision for a future where AI delivers finished projects, orchestrated by a meta-agent named Uma.

No Priors Ep. 132 | With Decagon CEO and Co-Founder Jesse Zhang

No Priors Ep. 132 | With Decagon CEO and Co-Founder Jesse Zhang

Jesse Zhang, co-founder and CEO of Decagon, discusses how their AI agents are revolutionizing customer service for large enterprises by replacing mundane human labor. He covers their go-to-market strategy, the importance of a hardworking in-office culture, his journey as a second-time founder, and the future of an agentic world where AIs interact on behalf of companies and consumers.

Production monitoring for AI applications using W&B Weave

Production monitoring for AI applications using W&B Weave

Learn how W&B Weave's online evaluations enable real-time monitoring of AI applications in production, allowing teams to track performance, catch failures, and iterate on quality over time using LLM-as-a-judge scores.

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