Build Wiz AI Show

Context Engineering (CE) is the systematic process designed to bridge the cognitive gap between human intent and machine understanding by optimizing context collection, storage, management, and usage. We explore CE’s history, tracing its evolution over 20 years from the "primitive computation" of Era 1.0 to the current "agent-centric intelligence" of Era 2.0, driven by large la...

Agent Lightning introduces a revolutionary approach to optimizing AI agents by fully decoupling Reinforcement Learning (RL) training from agent execution. We dive into how this framework allows developers to apply powerful RL techniques—like the hierarchical LightningRL algorithm—to any existing agent, regardless of its underlying framework, with almost zero code modifications. Tune in to learn ho...

Breaking Agent Backbones: AI agents are being deployed at scale, but their security is challenged by non-deterministic behavior and novel vulnerabilities. This episode introduces the "threat snapshot" framework and the new b3 benchmark , which systematically isolate and evaluate security risks stemming from the backbone LLM. We reveal crucial findings: enhanced reasoning capabilities gen...

In this episode, OpenAI leaders share unprecedented transparency regarding their research goals, aiming for a fully automated AI researcher by March 2028 and discussing the rapid approach of superintelligence. They detail a new structure, featuring a nonprofit foundation that governs a Public Benefit Corporation, essential for attracting the resources needed for their colossal $1.4 trillion infras...

Welcome to the new era of coding collaboration: Agent HQ is here, establishing GitHub as the centralized home for developers and a fleet of AI coding agents . We explore how the fully-fledged GitHub Copilot agent, alongside partners like Claude and Codex, now operates with deeper context and the ability to execute and coordinate tasks across the developer workflow. Discover how innovations like Mi...

We delve into Jensen Huang's vision that Artificial Intelligence marks the New Industrial Revolution , positioning it as essential national infrastructure and America's next Apollo moment. We explore how NVIDIA's extreme co-design and Accelerated Computing enable new " AI Factories ," achieving 10X generational performance leaps to drive down the cost of generating intelligen...

The modern workplace often buries professionals under context switching and scattered technology, hindering the productivity gains promised by AI. This episode explores the three stages of working smarter: Block Distractions , Scale Yourself , and Get Results , focusing on how a unified AI platform removes friction. Discover how to move past busywork, amplify your natural curiosity, and channel yo...

Join Lance from LangChain and Pete from Manus as they dive deep into the crucial discipline of Context Engineering for building effective AI agents. This webinar explores the challenge of context explosion —where performance drops as long-running agents accumulate tool call observations—and the core themes used to combat it: offloading, reducing, retrieving, and isolating context . Pete shares fre...

Welcome to an essential discussion on Vibe Coding , the new paradigm where developers shift from writing code line-by-line to orchestrating and validating outputs from autonomous AI agents. We'll formalize Vibe Coding as an engineering discipline, exploring its foundations in Large Language Models, complex agent architectures (like planning and memory mechanisms), and integrated feedback loops...

Chip Huyen , author of AI Engineering and AI strategy expert from NVIDIA and Netflix, breaks down the technical basics of building successful AI products, covering pre-training, RAG, RLHF, and effective evaluation design. We tackle the growing AI "idea crisis" and the crucial gap between what builders think improves AI applications (like chasing the latest news) versus what actually work...

In the hype of ChatGPT Atlas, lets talk about the darkside of Browsing AI Agents

Welcome to the show, where we discuss DeepSeek-OCR and its investigation into using optical 2D mapping for contexts compression, addressing the computational challenges of quadratic scaling faced by Large Language Models. We explore the DeepEncoder, the core engine designed to achieve high compression ratios, delivering near-lossless OCR precision (approximately 97%) even at a 10× token reduction....

This episode explores Anthropic's revolutionary 'Skills,' a new way to implement Standard Operating Procedures (SOPs) for LLM agents, ensuring consistent, high-quality output for specialized tasks like Excel analysis and document formatting. We dive into how these portable folders contain instructions and executable code, allowing Claude to efficiently access deep, specialized expertis...

**SEAL, the Self-Adapting Language Model framework, is revolutionizing how LLMs learn by enabling them to generate their own finetuning data and update directives. We explore how these powerful models create "self-edits"—synthetic training data and optimization parameters—which are continuously refined through a reinforcement learning loop. Discover how this meta-learning approach allows...

Are expensive Large Language Model (LLM) fine-tuning methods holding back your specialized agents, demanding massive computational resources and data? We dive into Training-Free Group Relative Policy Optimization (Training-Free GRPO) , a novel non-parametric method that enhances LLM agent behavior by distilling semantic advantages from group rollouts into lightweight token priors, eliminating cost...

Join us for a deep dive with Greg Brockman on the future of AI, where he reveals the internal struggle ("pain and suffering") of managing compute scarcity and the immense physical infrastructure build required to scale systems like Sora 2. Brockman discusses the shift from viewing AGI as a destination to a continuous process , emphasizing that current scaling curves and algorithmic progr...

Tune in as we explore Agentic Context Engineering (ACE), a novel framework designed to overcome limitations like "brevity bias" and "context collapse" that plague traditional LLM context adaptation methods. ACE transforms model contexts into continuously evolving, structured "playbooks" by employing a modular process of generation, reflection, and curation. We discuss...

This episode explores the Tiny Recursive Model (TRM) , a novel approach that leverages a single, tiny network (as small as 7M parameters) to tackle hard puzzle tasks like Sudoku, Maze, and ARC-AGI. We investigate how this simplified, recursive reasoning strategy achieves significantly higher generalization and outperforms much larger models, including complex Large Language Models (LLMs) and the H...

Demystify Large Language Model (LLM) evaluation, breaking down the four main methods used to compare models: multiple-choice benchmarks, verifiers, leaderboards, and LLM judges. We offer a clear mental map of these techniques, distinguishing between benchmark-based and judgment-based approaches to help you interpret performance scores and measure progress in your own AI development. Discover the p...

OpenAI DevDay 2025 marked the start of the "agentic era" of software development, focusing on making it "easier to build with AI" and transitioning AI from a "chatbot" into a "doer". We break down the revolutionary AgentKit , featuring Agent Builder , a visual, drag-and-drop platform launched to help developers rapidly deploy multi-step AI agents from protot...

Join us as Turing Award recipient Yann LeCun, Chief Scientist at Meta, critiques the state of AI, arguing that current systems, including Large Language Models (LLMs), are nowhere near matching the learning efficiency observed in humans and animals. LeCun proposes a major architectural shift, advocating that AI must abandon generative models for training and instead focus on building internal &quo...

Are smart machines making us forget how to think? This episode dives into the quiet phenomenon of AI-induced skill erosion, where relying on intelligent systems creates an "illusion of mastery" while core competence fades. We explore the organizational implications of deskilling and discuss strategies, such as targeted auditing and better system design, needed to preserve expertise when...

AI agents represent a paradigm shift in software engineering, but moving a promising prototype to a production-ready system presents a new set of challenges for startups. This episode distills Google's technical guide, offering a systematic roadmap to build, govern, and scale reliable agentic systems using tools like the Agent Development Kit (ADK). Discover the AgentOps framework , a discipli...

In the race to build AI that can not just think, but work as an autonomous agent , the prevailing wisdom has been that more data is always better. This episode explores a radical new paradigm called LIMI (Less Is More for Intelligent Agency) , which challenges this scaling law by showing how sophisticated AI "agency" can emerge from just 78 strategically curated training samples focused...

This episode delves into the unprecedented speed of AI adoption, which has outpaced historical technologies like the internet and personal computers. While AI's impact on work is often the focus, new data reveals a surprising trend: non-work related conversations now account for over 70% of consumer chatbot usage , with tasks like seeking practical guidance and writing assistance dominating over c...