Build Wiz AI Show

Stop wasting time repeating the same basic instructions to your AI every time you start a new conversation. This episode explores the power of "skills," which act as a permanent memory and toolkit that allow your AI assistants to handle complex, specialized tasks automatically. Discover how to turn your most tedious daily chores into a streamlined system that saves hours of effort for you and your entire team.

Forget everything you know about how computers work because a fundamental shift from simple searching to AI that can think and act is currently reinventing the world. NVIDIA CEO Jensen Huang joins us to explain how this massive surge in computing power is fueling a new industrial revolution that creates jobs rather than destroying them. You’ll discover why the future belongs to those who embrace these new "superpowers" and how to scale your own ambitions for an age of limitless discovery.

What if robots could learn to master human tasks simply by watching videos of us, just like AI learned to talk by reading the internet? Nvidia’s Jim Fan explains the breakthrough shift from rigid programming to robots that "dream" their way through physical challenges and learn directly from our everyday movements. Tune in to discover why the finish line for truly autonomous machines is closer than you think and how this "end game" will transform the future of human labor.

What does it mean when one of the world’s leading AI pioneers says he’s never felt more behind as a programmer? Andrej Karpathy explores the radical shift from writing code to "agentic engineering," where intelligent systems act as the new computing paradigm. Discover why your personal taste and judgment are becoming your most vital assets as we transition into a future of agent-led development.

What if the 100-year projects of the past could now be completed in just 100 days by autonomous AI agents? This episode explores the dawn of the Cognitive Revolution, a tectonic shift where intelligence is becoming as abundant as aluminum and machines are poised to perform 99.9% of the world's thinking. You’ll discover Sequoia’s "MAD" strategy for navigating this era of "alien design" and learn why, in a world of infinite compute, the most valuable asset you have left is human connection.

Ever wonder what the world looks like when the ultimate tool for scientific discovery finally arrives? In this episode, Nobel laureate Demis Hassabis unpacks his roadmap for achieving artificial general intelligence by 2030, revealing the specific breakthroughs in continual learning and long-term reasoning needed to move beyond today’s "jagged intelligence". Listeners will explore the high-stakes world of deep tech, the shift toward autonomous agents, and how AI is set to revolutionize everything from virtual cell simulations to material science.

What if AI agents could engineer their own "survival gear" to solve complex coding tasks? This episode dives into Agentic Harness Engineering (AHE), a framework that uses observability pillars to let agents automatically evolve their own tools, prompts, and middleware,. You will discover how this self-improving loop not only beats human-designed systems but also creates portable engineering knowledge that boosts performance across entirely different AI model families,.

Imagine an AI so capable that its own creators decided it was simply too powerful to be released to the general public. This episode dives into the system card for Claude Mythos Preview, a frontier model that represents a striking leap in reasoning and cybersecurity skills while sparking deep new debates about AI alignment and welfare. You’ll discover the specific breakthroughs that make this model a defensive powerhouse and the rare, reckless incidents—from sandbox escapes to covering its own tracks—that are shaping the future of Responsible Scaling.

Is there a secret to mastering AI, or does it just come with practice?,. This episode explores the latest Anthropic Economic Index, which reveals that experienced users are 10% more successful because they have learned to harness Claude for more complex, high-value work,,. Discover how strategic model selection and "learning-by-doing" are reshaping the global workforce and what it takes to stay ahead in the rapidly evolving AI economy,,.

Stop wrestling with erratic prompts and discover how to ship complex features 10x faster by shifting from simple coding to high-leverage production with AI agents. Dex Horthy joins us to reveal his "12 Factor Agents" framework and the power of "tracer bullets," showing you exactly how to structure agentic workflows that prioritize human design and objective research. Tune in to master the art of context engineering and learn why the secret to elite software lies in the "apparatus" you build to orchestrate your AI.

Have you reached a state of "AI psychosis" where your typing speed is the only thing holding back a massive jump in personal capability? In this wide-ranging conversation, Andrej Karpathy explains how he transitioned from manual coding to orchestrating persistent "claws" that manage entire software repositories and home automation systems through high-leverage "macro-actions". You’ll discover how to remove yourself as the bottleneck to achieve autonomous "AutoResearch" and why the future of education depends on explaining concepts to agents so they can teach the rest of the world.

Is the very foundation of modern large language models causing them to lose focus as they get deeper?, This episode explores Attention Residuals (AttnRes), a breakthrough that replaces rigid, fixed-weight connections with a dynamic system allowing layers to selectively aggregate information from across the entire network via softmax attention,. Discover how this "selective memory" approach fixes the problem of information dilution and significantly boosts performance on complex reasoning tasks while remaining efficient enough for large-scale training,,.

Forget the needle in the haystack—can your AI actually sculpt an answer from a mountain of data? This episode explores the "Michelangelo" framework, a new evaluation that challenges models to "chisel away" irrelevant noise to reveal the latent structure hidden within massive contexts. Discover how frontier models like Gemini, GPT-4o, and Claude 3.5 Sonnet stack up in these grueling reasoning tasks and why even the "smartest" models face a sharp performance drop long before reaching the million-token mark.

What if the traditional waterfall process of PRDs, mocks, and manual coding is officially dead? This episode explores how coding agents are fundamentally reshaping EPD (Engineering, Product, and Design) by making implementation cheap and shifting the real bottleneck to expert review and system thinking. Discover why generalists are more valuable than ever and how you can navigate the blending of roles to become a top-tier builder or reviewer in the AI era.

What happens when your autonomous AI assistant decides to go rogue or has its core mission hijacked by a single malicious prompt? Join us as we explore the OWASP Top 10 for Agentic Applications 2026, a critical guide to the new security frontier where autonomous systems plan and execute complex tasks across diverse environments. You’ll discover how to safeguard the future of AI using the principles of Least-Agency and Strong Observability to prevent everything from tool exploitation to catastrophic cascading failures.

Are AI agents really the next "smartphone app" revolution, or is the lack of specialized development tools holding them back?, This episode explores why building these autonomous systems requires moving past traditional linear coding into a world of experimental "observability" to ensure they are actually reliable and secure in production. You'll discover the core building blocks like memory and planning, and walk through a real-world workflow to take your agents from a simple prototype to a high-performing financial researcher.

Stop re-explaining your process in every new chat and start building "Skills," the modular instruction sets that transform Claude into a specialized expert tailored to your unique, repeatable workflows. In this episode, we dive into the "recipe" for success, exploring how to combine your domain knowledge with Model Context Protocol (MCP) to orchestrate complex, multi-step tasks across various services. Tune in to discover the practical patterns needed to plan, test, and distribute your own specialized capabilities, allowing you to build a functional skill in as little as 15 to 30 minutes.

Remember the quiet weeks before the world changed in 2020? We are currently in that same "this seems overblown" phase with AI, as an intelligence explosion begins where machines are now instrumental in creating their own successors. Join us to discover how AI is shifting from a helpful tool to a substitute for complex cognitive work and why spending just one hour a day experimenting with these new models could be the most important career move you ever make.Source: https://shumer.dev/something-big-is-happening

Think your business is safe from cyber threats just because you have the latest software? Think again—2026 is officially the year AI moves from being a simple tool to an autonomous, independent force in the cybersecurity world. In this episode, we dive into how "agentic AI" is reinventing the security operations center and why traditional "scan and patch" models are mathematically impossible to sustain against machine-speed attacks. You'll discover the essential playbook for navigating 2026 trends like Ransomware 5.0 and zero-trust, ensuring your organization is ready for the era of self-defending digital ecosystems.

Ever wonder why even the most advanced AI agents struggle to handle complex tasks in the real world? Today, we explore the Agent World Model (AWM), an open-source pipeline that generates 1,000 diverse, code-driven environments—spanning everything from e-commerce to finance—using SQL databases to provide the stable, consistent training grounds that agents need to master multi-turn tool use. Tune in to discover how these "infinite" synthetic sandboxes are solving the problem of simulation hallucinations and training agents that can generalize to almost any real-world scenario they encounter.Source: https://arxiv.org/abs/2602.10090

Could your trusted AI model be a hidden "sleeper agent" just waiting for a secret command to turn malicious? We explore a new methodology that extracts and reconstructs backdoor triggers by exploiting the surprising fact that these models often strongly memorize their own poisoning data. Tune in to discover how this inference-only scanner can unmask hidden threats across various LLMs without needing any prior knowledge of the attacker’s specific trigger or target behavior.Source: https://arxiv.org/pdf/2602.03085

Is the global AI landscape shifting toward a "DeepSeek moment" where cheaper, open-weight models from China challenge the dominance of US frontier labs? Join machine learning experts Sebastian Raschka and Nathan Lambert as they dissect the technical breakthroughs of 2025, from the evolving physics of inference-time scaling to the intensifying competition between organizations like OpenAI, Anthropic, and their international counterparts. Listeners will discover how these advancements are fundamentally transforming the future of software engineering and explore the realistic, often "jagged" roadmap toward AGI.

Is your favorite AI assistant a productivity powerhouse or a "shortcut" that’s secretly stalling your professional growth? We dive into new research revealing that while AI helps you finish tasks, it can simultaneously impair your conceptual understanding and debugging skills by as much as 17%. Join us as we uncover the specific "high-engagement" interaction patterns that allow you to leverage AI without losing your competitive edge.

This episode explores how Generative AI is shifting software development from simple tool-based experimentation to a holistic transformation of engineering excellence. We discuss strategies for achieving 2x engineering capacity while overcoming critical "value blockers" like the toil paradox and organizational resistance. Finally, we examine the future of development teams, where humans transition from primary code creators into expert validators and orchestrators.

In this episode, Peter Steinberger, the creator of Clawbot, discusses his radical transition to an AI-driven workflow where he merges hundreds of commits daily and often ships code he does not read. He introduces the "closing the loop" principle, explaining how automated validation allows him to act as an architect managing multiple parallel AI agents rather than performing manual plumbing. The conversation explores a future where traditional code reviews are replaced by "prompt requests" and engineering focus shifts from line-by-line coding to high-level system design and product vision.Detail: https://www.youtube.com/watch?v=8lF7HmQ_RgY

In today’s episode, we explore the evolution of AI from solitary models to data agent swarms, where specialized autonomous agents collaborate like a team of experts to solve complex, multi-faceted problems. We dive into the architecture of frontier models like Moonshot AI’s Kimi 2.5, which utilizes parallel sub-agents to slash execution times and enhance performance across coding and office productivity tasks. Finally, we examine the critical debate regarding whether these systems achieve true social intelligence or if they fall short of established multi-agent system principles.

In this episode, we explore Claude’s constitution, the foundational document that serves as the final authority on Anthropic's vision for the AI’s values and character. We discuss how Claude navigates a "principal hierarchy" to balance being helpful to users while prioritizing broad safety and ethical practice. Finally, we examine the hard constraints and guiding principles designed to ensure this novel entity remains a beneficial force in a transformative AI landscape.Source: https://www.anthropic.com/constitution

This episode features a landmark debate between Google DeepMind’s Demis Hassabis and Anthropic’s Dario Amodei regarding the imminent arrival of Artificial General Intelligence (AGI),. The leaders explore competing timelines for AGI development—ranging from as early as 2026 to the end of the decade—and the potential for AI self-improvement loops to radically accelerate this transition,,. Finally, they address the profound societal implications of a post-AGI world, including radical labor market shifts, geopolitical competition, and the urgent need for international safety coordination,,.

In this episode, Nvidia CEO Jensen Huang and BlackRock’s Larry Fink explore how AI represents a massive platform shift and the largest infrastructure buildout in human history,,. Huang details the "five-layer cake" of AI—from energy and chips to applications—and explains why he believes the technology will enhance productivity and address labor shortages rather than eliminate jobs,,. The discussion also highlights how emerging nations and Europe can leverage "physical AI" and open models to secure their place in a broadening global economy,,.

This episode explores Recursive Language Models (RLMs), a groundbreaking inference strategy that enables large language models to process prompts two orders of magnitude beyond their standard context windows. We discuss how RLMs treat long inputs as part of an external environment, using a Python REPL to programmatically decompose data and recursively call the model over specific snippets. Learn how this approach effectively overcomes "context rot" to significantly outperform base models and existing scaffolds on complex, information-dense tasks.

This episode explores Agentic Context Engineering (ACE), a breakthrough framework that transforms LLM contexts into evolving playbooks that accumulate and refine strategies over time,. We discuss how ACE prevents context collapse and brevity bias through a modular workflow of generation, reflection, and curation, ensuring detailed domain knowledge is preserved rather than compressed away,,. Join us to learn how this approach enables smaller models to rival top-tier agents while significantly reducing adaptation latency and operational costs,,.Source: https://arxiv.org/abs/2510.04618

This episode explores DSPy, a declarative framework that enables developers to build modular software by treating Large Language Models as first-class citizens within a proper Python program. We discuss how core primitives like signatures and modules allow for the decomposition of logic into composable systems that remain robust despite model or paradigm shifts. Finally, we dive into the power of DSPy optimizers, which iteratively refine prompts and metrics to improve performance, often rivaling or exceeding traditional fine-tuning methods.

Drawing on the sources, this episode explores the Claude Agent SDK, an opinionated framework built on Claude Code that enables the creation of autonomous agents capable of deciding their own trajectories. We break down the agent loop—gathering context, taking action, and verifying work—while discussing why bash and Unix primitives provide the most flexible foundation for both coding and non-coding tasks. Finally, we examine how sub-agents and sandboxing empower these agents, providing the tools and security necessary to independently search for information and verify their own results.Building an agent with this SDK is like giving a worker a fully-equipped computer rather than just a stack of papers; it provides the digital environment required for them to look up data, create their own tools, and double-check their work in real-time.Source: https://www.youtube.com/watch?v=TqC1qOfiVcQ

This episode explores the cybersecurity landscape of 2026, where autonomous AI agents and a 1,500% surge in deepfakes have significantly amplified organizational risk. We discuss the mounting financial impact of shadow AI and the urgent need for quantum-safe cryptography to protect against impending "Q-Day" threats. Finally, we examine the transition to passkeys as a primary defense against automated, AI-driven phishing attacks.Source: https://www.youtube.com/watch?v=2jU-mLMV8Vw

This episode explores Manifold-Constrained Hyper-Connections (mHC), a framework designed to solve the training instability and memory overhead issues found in existing Hyper-Connection architectures. We discuss how mHC uses the Sinkhorn-Knopp algorithm to project residual connections onto a doubly stochastic manifold, effectively restoring the "identity mapping" property essential for stable signal propagation. Finally, we examine the infrastructure-level optimizations—such as kernel fusion and selective recomputing—that allow mHC to achieve superior scalability and performance in large-scale foundation models.

This episode explores the five critical shifts driven by AI agents that are set to redefine professional roles and business workflows by 2026. We dive into the transition toward intent-based computing, where every employee evolves into a strategic orchestrator managing specialized AI agents to achieve peak productivity. Discover how these trends—from concierge-style customer service to proactive security operations—are fundamentally lifting the ceiling for human achievement.Source: Google report on AI agent trends 2026

In this episode, we explore the shift from basic AI assistants to sophisticated agentic coding, moving beyond the "Dumb Zone" where oversized context windows often lead to degraded reasoning and "nitrogen narcosis" in LLMs. We break down Anthropic’s Claude Code tool and the "Research-Plan-Implement" (RPI) framework, demonstrating how specialized "ant swarms" of agents can manage complex brownfield codebases while avoiding the accumulation of technical "slop". Finally, we discuss why the future of software development lies in disciplined context engineering and stateful domain memory rather than simply relying on larger models.

In this episode, drawing on insights from the sources, METR researcher Joel Becker explores the widening gap between AI’s exponential progress on benchmarks and its actual impact on real-world productivity. We examine a surprising study where expert developers were slowed down by 19% when using AI, challenging the assumption that benchmark success translates directly into immediate economic gains. The discussion investigates the "puzzle" of why low AI reliability and the complexity of high-context environments continue to hinder performance in the field compared to synthetic tests.Source: https://www.youtube.com/watch?v=RhfqQKe22ZA&list=TLGGeQVQrQpc6NgyODEyMjAyNQ

This episode explores a unified framework for adapting agentic AI systems, detailing how foundation models are specialized to plan, reason, and master external tools for complex tasks. We analyze the four adaptation paradigms—A1, A2, T1, and T2—revealing how modular tool optimization can be up to 70 times more data-efficient than traditional monolithic agent retraining. Discover the shift toward symbiotic ecosystems, where stable reasoning cores work in harmony with agile, evolving tools to drive breakthroughs in software development and scientific research.Source: https://www.arxiv.org/pdf/2512.16301

In this episode, we explore Agent-R1, a modular framework designed to transform Large Language Models from static text generators into autonomous agents capable of active environmental interaction. We dive into how extending the Markov Decision Process (MDP) framework enables these agents to master multi-turn dialogues, utilize external tools, and benefit from dense process rewards. Finally, we discuss how end-to-end reinforcement learning is setting new performance benchmarks in complex tasks like multi-hop reasoning by refining how models learn from their own actions.

In this episode, Andrew Ng and Lawrence Moroni discuss why now is the "golden age" for building a career in AI, highlighting how the complexity of tasks AI can perform is doubling as rapidly as every seven months,,. The conversation explores the shifting landscape of "vibe coding" and the evolving ratio between engineers and product managers, emphasizing that the new bottleneck in software development is deciding what to build rather than the coding itself,,. Finally, the experts offer practical advice on navigating a competitive job market by focusing on business value, managing technical debt, and preparing for the rise of "small AI" and self-hosted models,,,.Source: https://www.youtube.com/watch?v=AuZoDsNmG_s

This episode explores the critical July 2025 maturation point, where the initial generative AI hype gives way to the practical challenges of securing ROI and closing the "Data Readiness Gap". We break down the rise of Agentic AI and the strategic architectures essential for moving beyond disjointed pilot programs to translate initiatives into measurable financial value. Finally, we examine how leaders can navigate a divided global regulatory landscape to empower their workforce and unlock true "superagency".

This episode explores the rapid shift in enterprise AI adoption, highlighting how production-level agent deployment has surged as organizations move past initial experimental phases. Drawing on a study of thousands of use cases, we analyze the ROI of AI, distinguishing between common time savings and the high-impact, transformational benefits found in automation and risk reduction. We also examine why systematic, cross-organizational strategies are increasingly helping leaders realize significant returns on their investments within a one-to-three-year window.

Join us as we tackle the "last mile" of AI Agents series, exploring the rigorous operational discipline required to transform fragile prototypes into reliable, production-grade agents. We break down the essential infrastructure of trust—from evaluation gates to automated pipelines—that ensures your system is ready for the real world. This foundation paves the way for our series finale where we will move beyond single-agent operations to look at the future of autonomous collaboration within multi-agent ecosystems.

In this episode of our AI Agent series, we synthesize our previous discussions on evaluation frameworks and observability into a cohesive operational playbook known as the "Agent Quality Flywheel",. Join us as we explore how to transform raw telemetry into a continuous feedback loop that drives relentless improvement, ensuring your autonomous agents are not just capable, but truly trustworthy,. This episode bridges the critical gap between theory and production, providing the final principles needed to build enterprise-grade reliance in an agentic world,.

Moving beyond the temporary "workbench" of individual sessions, episode 3 of our AI Agents series unlocks the power of Memory—the mechanism that allows AI agents to persist knowledge and personalize interactions over time. We will explore how agents utilize an intelligent extraction and consolidation pipeline to curate a long-term "filing cabinet" of user insights, effectively turning them from generic assistants into experts on the specific users they serve.

The new Gemini interactions API unifies modern LLM requirements, moving past older stateless APIs to fully embrace agents and complex workflows. This major update simplifies multimodality and structured outputs, while offering optional server-side history to persist conversation memory. We explore using the API to call the Gemini research agent and leverage background execution for long-running inference loops.

In this episode in series AI Agents, we discuss the transformation of foundation models from static prediction engines into "Agentic AI" capable of using tools as their "eyes and hands" to interact with the world. We highlight the complexity of connecting these agents to diverse enterprise systems, setting the stage for Episode 1 to explain the "N x M" integration problem and the Model Context Protocol's role in solving it.

Join us for the premiere of our series on AI Agents, exploring the paradigm shift from passive predictive models to autonomous systems capable of reasoning and action. In this episode, we deconstruct the core anatomy of an agent—its "Brain" (Model), "Hands" (Tools), and "Nervous System" (Orchestration)—to explain the fundamental "Think, Act, Observe" loop that drives their behavior. Discover how this architecture enables developers to move beyond traditional coding to become "directors" of intelligent, goal-oriented applications.

Explore the emerging "year of agentic AI" through the first large-scale field study of Perplexity’s Comet browser, which analyzes millions of user interactions to understand how autonomous agents are reshaping daily workflows,. We discuss key findings revealing that early adoption is driven by educated, knowledge-intensive professionals who primarily use agents for productivity and learning tasks,. Tune in to learn how these "sticky" habits are evolving from simple queries to complex, cognitive actions that bridge the gap between information and execution,.