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The Prompt Is Still a Punch Card - Ted Johnson, JoinIn AI

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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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The Prompt Is Still a Punch Card - Ted Johnson, JoinIn AI

The Prompt Is Still a Punch Card - Ted Johnson, JoinIn AI

Ted Johnson argues that current AI interfaces, particularly prompting, operate on an outdated "batch processing" protocol akin to punch cards. Despite advanced LLM capabilities, this interface design forces humans to adapt to machines, hindering natural interaction. He advocates for a shift towards human-compatible interfaces where AI actively participates in real-time conversation, leveraging its intelligence to remove user burdens and amplify human potential.

How Nuclear Will Unlock Energy Abundance with Valar Atomics Founder Isaiah Taylor

How Nuclear Will Unlock Energy Abundance with Valar Atomics Founder Isaiah Taylor

Valar Atomics founder Isaiah Taylor discusses how his company is rapidly advancing nuclear energy through hardware iteration, manufacturing, and a unique regulatory pathway. He explains their intrinsic safety philosophy, the strategic importance of vertical integration for speed and cost reduction, and their venture-backed approach to building gigasites. Taylor highlights how the demand for AI compute is a major driver for nuclear, showcasing their direct powering of an NVIDIA Blackwell chip, and casts a compelling vision for a future of "hyper-technoindustrialism" enabled by abundant, cheap atomic energy.

The Platform Engineer’s Handbook • Ajay Chankramath & Kaspar von Grünberg • GOTO 2026

The Platform Engineer’s Handbook • Ajay Chankramath & Kaspar von Grünberg • GOTO 2026

This conversation with Ajay Chankramath, author of 'The Platform Engineer’s Handbook,' delves into why practical, code-first guidance is essential for building Internal Developer Platforms. He argues that developer adoption failures stem from a "product discipline gap," not a technology one, emphasizing developer experience as a first-class outcome. The discussion covers the book's arc from foundations to enterprise-grade features and its focus on 100% open-source, vendor-agnostic tooling. Crucially, it highlights how agentic AI raises the stakes for platform engineering, requiring new IDP layers for agent context, memory, and guardrails, asserting that these must be built, owned, and operated internally for safe and productive AI adoption.

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

The Semantic Layer and AI Agents // David Jayatillake // MLOps Podcast #343

The Semantic Layer and AI Agents // David Jayatillake // MLOps Podcast #343

David Jayatillake, VP of AI at Cube.dev, discusses the critical role of a headless, open-source semantic layer in the modern data stack. He argues against proprietary, BI-tool-specific semantic layers that create vendor lock-in and advocates for a decoupled approach. The conversation explores how AI agents can automate the entire data pipeline—from ingestion and transformation to generating and querying the semantic layer—and compares the functionalities of semantic layers and feature stores, highlighting the crucial difference of temporality.

Building Planetary-Scale Data Systems with Venice • Felix GV & Olimpiu Pop • GOTO 2026

Building Planetary-Scale Data Systems with Venice • Felix GV & Olimpiu Pop • GOTO 2026

Félix GV, an architect of LinkedIn's Venice database, discusses its unbundled, planetary-scale architecture. He covers how components like Kafka and RocksDB form independent distributed systems, details their rigorous chaos engineering practices, explains CAP theorem trade-offs in multi-region deployments, and explores the experimental integration of DuckDB for SQL-based analytics.

If You Can't See Inside, How Do You Know It's THINKING? [Dr. Jeff Beck]

If You Can't See Inside, How Do You Know It's THINKING? [Dr. Jeff Beck]

Dr. Jeff Beck explores the philosophical and technical definitions of agency, arguing that the distinction between an agent and an object lies in computational sophistication, particularly the capacity for planning and counterfactual reasoning. The conversation provides a deep dive into Energy-Based Models (EBMs), Yann LeCun's JEPA for learning in latent space, and a pragmatic approach to AI safety centered on inverse reinforcement learning rather than fears of rogue superintelligence.

Architecting Self-Healing Enterprise Operations: AI + DevSecOps | Akshay Mittal | SW Engineer | 4K|E

Architecting Self-Healing Enterprise Operations: AI + DevSecOps | Akshay Mittal | SW Engineer | 4K|E

Explore the shift from reactive to predictive DevSecOps with Akshay Mittal. This discussion covers how AI-Augmented DevSecOps and Agentic Workflows are creating self-healing systems, the critical role of Explainable AI (XAI), and a four-layer architecture for building scalable, enterprise-grade AI solutions.

The Future of AI Molecular Discovery

The Future of AI Molecular Discovery

Professor Ellen Zhong discusses the shift from viewing proteins as static objects to dynamic molecular machines. She explores how cryo-electron microscopy (cryo-EM) combined with machine learning creates complex inverse problems to reveal protein motion, moving beyond the "solved" problem of static structure prediction and toward a future of AI-driven scientific discovery.

LLM vs. SLM vs. FM: Choosing the Right AI Model

LLM vs. SLM vs. FM: Choosing the Right AI Model

A guide to understanding the differences between Large Language Models (LLMs), Small Language Models (SLMs), and Frontier Models (FMs). Learn the unique strengths of each model type and see practical use cases for document classification, customer support, and incident response to help you choose the right model for your AI project.

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