Vanishing Gradients

There are a lot of reasons why we should do AI evals. For many companies doing AI evals is the way to build the feedback loop into the product development lifecycle. So it is like your compass. We’re using AI evals as a compass to guide product development and also product iteration. And also, many times we need evals to function as the pass or fail gate in release decisions. Whether this product is good enough for release or whether it is good enough for experiment, evals are also used in that.

Stella Wenxing Liu, Head of Applied Science at ASU, and Eddie Landesberg, Staff Data Scientist at Google, join Hugo to talk about why AI evaluation is evolving from “vibe checks” into a rigorous, multi-disciplinary science and how causal inference will take AI evals to the next level in 2026.

Vanishing Gradients is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

They Discuss:

* Team-Centric AI Evals, integrating product managers, data scientists, and SMEs under a “benevolent dictator” (or not!) to ensure comprehensive and effective evaluation;

* Custom Evaluation Metrics, moving beyond generic vendor metrics to analyze raw data and identify specific failure modes, avoiding generic product outcomes;

* AI as Policy Evaluation, framing AI evaluation as a causal inference problem to estimate counterfactual performance of new “policies” (prompts, models) and predict online AB test outcomes;

* Clear Product Constraints, defining what an AI product should not do with strict guardrails to prevent misuse, control costs, and avoid brand dilution;

* Calibrated LLM Judges, statistically aligning LLM-as-a-judge with human experts using causal inference to ensure valid proxies for human welfare and business objectives;

* Essential Data Curiosity, fostering a culture of manual data inspection to build intuition before relying on automated error analysis or agents, ensuring effective system design;

* Statistical AI Evaluation, shifting from unit-test thinking to non-deterministic distributions, using confidence intervals and power analysis to discern genuine improvements from statistical noise;

* Proactive Regulatory Compliance, developing rigorous, defensible internal evaluation standards now to gain a competitive advantage as vague AI regulations move towards enforced compliance;

* Human-Centric Benchmarking, grounding AI systems in human judgment and user values, moving beyond automated scores to build resilient and differentiated AI.

You can also find the full episode on Spotify, Apple Podcasts, and YouTube.

You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!

👉 Stella has just started teaching a cohort of her AI Evals and Analytics Playbook course starting this week. She’s kindly giving listeners of Vanishing Gradients 30% off with this link.👈

Our flagship course Building AI Applications just wrapped its final cohort but we’re cooking up something new. If you want to be first to hear about it (and help shape what we build), drop your thoughts here.

LINKS

* Stella Wenxing Liu on LinkedIn

* Eddie Landesberg on LinkedIn

* Stella’s AI Evals & Analytics Playbook course on Maven (30% community discount)

* CJE (Causal Judge Evaluation) package by Eddie

* Trillion Dollar Coach

* Goodhart’s Law

* Upcoming Events on Luma

* Vanishing Gradients on YouTube

* Watch the podcast video on YouTube

How You Can Support Vanishing Gradients

Vanishing Gradients is a podcast, workshop series, blog, and newsletter focused on what you can build with AI right now. Over 70 episodes with expert practitioners from Google DeepMind, Netflix, Stanford, and elsewhere. Hundreds of hours of free, hands-on workshops. All independent, all free.

If you want to help keep it going:

* Become a paid subscriber, from $8/month

* Share this with a builder who’d find it useful

* Subscribe to our YouTube channel.

Thanks for reading Vanishing Gradients! This post is public so feel free to share it.

Katharine Jarmul, Privacy in ML/AI Expert & Author of Practical Data Privacy, joins Hugo to unpack why most AI privacy advice is theater: and what technical privacy actually looks like when you’re shipping LLMs, agents, and multimodal systems into the real world.

In this episode, we dig into how to build defensible systems in an era of AI agents and multimodal models: why system prompts (and your entire agent harness!) should be considered public by default, and why “privacy observability” is as critical as data observability for anyone building with LLMs today. Multimodal is what changes the threat model: identifiers hide in images, audio, and metadata, not just text, and the old anonymization playbook doesn’t cover it.

Vanishing Gradients is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

We Discuss:

* No Convenience Tax, you don’t have to trade privacy for utility: high-utility AI products can be privacy-preserving through technical controls like privacy routing and input sanitization;

* Public Prompts and Harnesses: assume any instruction or secret in a system prompt or agent harness will be exfiltrated; don’t put sensitive info there in the first place;

* Privacy Observability, tag and track data flows so information is used only for its original intended purpose: catch design flaws before they become legal problems;

* Technical Privacy, implement mathematical and statistical constraints directly into ML systems and data flows so privacy is measurable and enforceable, not aspirational;

* Tiered Guardrails, a three-layer approach: deterministic filters for hard rules, algorithmic models for nuanced classification, and internal alignment training for behavioral baselines;

* Federated Learning Is Not Privacy, model updates in FL leak sensitive data on their own: you must layer differential privacy or encrypted computation on top, or you’re reverse-engineerable;

* Anonymization Spectrum, navigate the “grayscale” of privacy in multimodal AI, balancing data utility and individual risk as identifiers hide in non-obvious places;

* Privacy Champions, embed privacy accountability directly into development by training and incentivizing engineers inside product teams;

* Red Teaming as Ritual, your goal is to attack yourself: practice thinking like an attacker, and turn privacy testing into an organization-wide creative ritual rather than a siloed security task.

You can also find the full episode on Spotify, Apple Podcasts, and YouTube.

You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!

👉 Katharine is teaching her next cohort of Practical AI Privacy starting April 20. She’s kindly giving readers of Vanishing Gradients 10% off. Use this link. I’ll be taking it so hope to see you there!👈

Our flagship course Building AI Applications just wrapped its final cohort but we’re cooking up something new. If you want to be first to hear about it (and help shape what we build), drop your thoughts here.

LINKS

* Practical AI Privacy course on Maven (10% off with code build-with-privacy)

* Katharine Jarmul on LinkedIn

* Probably Private — Katharine’s website & newsletter

* Practical Data Privacy (Katharine’s book)

* Let’s Build an AI Privacy Router — Lightning Lesson

* Practical AI Privacy: Agents & Local LLMs (newsletter issue)

* A Deep Dive into Memorization in Deep Learning (kjamistan blog)

* Microsoft Presidio

* Llama Guard 3 8B on Hugging Face

* Nicholas Carlini

* From Magic to Malware: How OpenClaws Agent Skills Become an Attack Surface (1Password)

* Owning Ethics (Metcalf, Moss, boyd — Data & Society)

* Hugo on guardrails in LLM applications

* Upcoming Events on Luma

* Vanishing Gradients on YouTube

* Watch the podcast video on YouTube

How You Can Support Vanishing Gradients

Vanishing Gradients is a podcast, workshop series, blog, and newsletter focused on what you can build with AI right now. Over 70 episodes with expert practitioners from Google DeepMind, Netflix, Stanford, and elsewhere. Hundreds of hours of free, hands-on workshops. All independent, all free.

If you want to help keep it going:

* Become a paid subscriber, from $8/month

* Share this with a builder who’d find it useful

* Subscribe to our YouTube channel.

Thanks for reading Vanishing Gradients! This post is public so feel free to share it.

If you take a model release as an anchor point, let’s say Nemotron 3 or Qwen 3.5, you can go in both directions: You can either plug them into an agent and play around with that, or you can look, okay, what does the model look like under the hood? What are the ingredients? What type of attention mechanism do they use? What are currently research techniques that could make that even better in the next generation of models? What can we swap out, basically? And I’m interested in both of these!

Sebastian Raschka, Independent AI Researcher and author of Build a Large Language Model from Scratch, joins Hugo to talk about what’s changed in AI architecture, from post-training to hybrid models, and why understanding what’s under the hood matters more than ever for developers building in the agentic era. Sebastian’s upcoming book, Build a Reasoning Model from Scratch, currently available for pre-order on Amazon and in early access on Manning!

Vanishing Gradients is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

We Discuss:

* Ed Tech for Agents: should we design educational content specifically for agentic systems, or is there a better approach?

* Inference Scaling is the new frontier, driving “gold-level” performance during generation via parallel sampling and internal meta-judges;

* Hybrid Architectures from Qwen 3.5 and Nemotron 3 scale almost linearly, making long-context agentic workflows significantly more affordable and performant;

* Multi-head Latent Attention (MLA), developed by DeepSeek, wins the KV cache war by drastically reducing memory overhead without performance hits;

* Agent Harnesses need to be continuously simplified as frontier models are post-trained on agent trajectories. Teams that don’t strip back their scaffolding risk the harness getting in the way of a more capable model.

* “AI Psychosis”: the cognitive load of supervising self-supervising agents, and why we’re all conducting an orchestra we were never trained to conduct;

* Sebastian’s AI Stack: a surprisingly simple setup (Mac mini, Codex, Ollama) with a ~20-item QA checklist, delegating the boring work to preserve energy for creative development;

* Fine-tuning is now an economic decision, optimizing costs and latency for high-volume tasks where long system prompts outweigh a one-time training run;

* Process Reward Models (PRMs) are the next frontier, verifying intermediate reasoning steps to solve “hallucination in the middle” for complex math and code tasks;

* “Implementation Does Not Lie”: Sebastian’s layer-by-layer verification philosophy, comparing from-scratch builds against HuggingFace references to catch details invisible in papers;

* Architecture Details dictate inference stack choices; nuances like RMSNorm stability or RoPE flavors are critical for optimal performance and troubleshooting;

* The Distillation Loop drives open-weight parity, enabling specialized, “frontier-class” models by “pre-digesting” frontier outputs without multi-million dollar training risks.

You can also find the full episode on Spotify, Apple Podcasts, and YouTube.

You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!

Our flagship course Building AI Applications just wrapped its final cohort but we’re cooking up something new. If you want to be first to hear about it (and help shape what we build), drop your thoughts here.

Links and Resources

* Build a Reasoning Model (From Scratch): Sebastian’s new book, currently available for pre-order on Amazon and in early access on Manning. You’ll learn how reasoning LLMs actually work by starting with a pre-trained base LLM and adding reasoning capabilities step by step in code. A hands-on follow-up to Build a Large Language Model from Scratch.

* LLM Architecture Gallery: Sebastian’s collection of architecture figures and fact sheets from his blog posts, updated with each major model release. A go-to visual reference for comparing what’s changed under the hood across model generations.

* Sebastian Raschka on LinkedIn

* Sebastian’s website

* Ahead of AI (Sebastian’s Substack)

* Build a Large Language Model from Scratch

* PinchBench: OpenClaw Benchmark Leaderboard

* DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning

* Gated Delta Networks: Improving Mamba2 with Delta Rule (ICLR 2025)

* DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

* Hugging Face Model Hub

* Upcoming Events on Luma

* Vanishing Gradients on YouTube

A Bit More on Agent Harnesses

* Components of A Coding Agent by Sebastian

* How To Build An Agent that Builds its own Harness by Hugo and Ivan Leo (DeepMind, ex-Manus)

* Build Your Own Deep Research Agent with Hugo & Ivan Leo (Google DeepMind, ex-Manus): In this livestream, you’ll learn how to build a production-grade agent harness from scratch in pure Python;

* AI Agent Harness, 3 Principles for Context Engineering, and the Bitter Lesson Revisited with Lance Martin (Anthropic), Duncan Gilchrist (Delphina), and Hugo

* The Post-Coding Era: What Happens When AI Writes the System? with Nicholas Moy (Google DeepMind), Duncan Gilchrist (Delphina), and Hugo

* What is an Agent Harness? from What 300+ Engineers from Netflix, Amazon, and Instacart Asked About AI Engineering.

How You Can Support Vanishing Gradients

Vanishing Gradients is a podcast, workshop series, blog, and newsletter focused on what you can build with AI right now. Over 70 episodes with expert practitioners from Google DeepMind, Netflix, Stanford, and elsewhere. Hundreds of hours of free, hands-on workshops. All independent, all free.

If you want to help keep it going:

* Become a paid subscriber, from $8/month

* Share this with a builder who’d find it useful

* Subscribe to our YouTube channel.

Thanks for reading Vanishing Gradients! This post is public so feel free to share it.

I often see what I would consider to be b******t evals, especially in data, like write this dumb SQL. Almost every one of these dumb SQL questions that I’ve seen for benchmarks are just so either obviously easy or overwhelmingly adversarial. They just, they don’t feel valuable as a data scientist, it’s something that you probably would never ask a real data scientist to do. So I went out my way to create real ones. Let me read one to you.

Bryan Bischof, Head of AI at Theory Ventures, joins Hugo to talk about what happened when 150 people spent six hours using AI agents to answer real data science questions across SQL tables, log files, and 750,000 PDFs.

They Discuss:

* Failure Funnels, pinpoint where agent reasoning breaks down using causal-chain binary evaluations instead of vague 1-5 scales;

* Median Score: 23 out of 65, what happened when world-class engineers turned agents loose on real data work, and why general-purpose coding agents with human prodding beat fancy frameworks;

* Zero-Cost Submissions Kill Trust, without a penalty for wrong answers, agents hill-climb to correct submissions through brute force instead of building confidence;

* Data Science is “Zooming”, moving beyond binary decisions to iterative problem framing, refining “does our inventory suck?” into a tractable hypothesis;

* MCP as Semantic Layer, model your organization’s proprietary knowledge once and distribute it to whatever LLM interface your team prefers;

* The Subagent vs. Tool Debate, a distinction that adds cognitive load without hiding complexity;

* Self-Orchestration Gap, agents don’t yet realize they should trigger specialized extraction frameworks like DocETL instead of reading 750K PDFs one by one;

* The Future of Evals, from vibe checks to objective functions and continuous user feedback that lets systems converge on reliability.

You can also find the full episode on Spotify, Apple Podcasts, and YouTube.

You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!

👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Our final cohort has started. Registration is still open. All sessions are recorded so don’t worry about having missed any. Here is a 25% discount code for readers. 👈

LINKS

* Bryan Bischof on Twitter/X

* Bryan Bischof on LinkedIn

* Theory Ventures

* The Hunt for a Trustworthy Data Agent (blog post)

* America’s Next Top Modeler GitHub repo

* Hamel’s evals FAQ: How do I evaluate agentic workflows?

* DocETL

* LLM Judges and AI Agents at Scale (Hugo’s podcast with Shreya Shankar)

* When Your Metrics Are Lying (Cimo Labs)

* Lessons from a Year of Building with LLMs (livestream on YouTube)

* Bryan Bischof: The Map is Not the Territory (YouTube)

* Upcoming Events on Luma

* Vanishing Gradients on YouTube

* Watch the podcast video on YouTube

👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Our final cohort has started. Registration is still open. All sessions are recorded so don’t worry about having missed any. Here is a 25% discount code for readers. 👈

Our thesis is that AI is still just engineering… those people who tell us for fun and profit, that somehow AI is so, so profound, so new, so different from anything that’s gone before that it somehow eclipses the need for good engineering practice are wrong. We need that good engineering practice still, and for the most part, most things are not new. But there are some things that have become more important with AI. One of those is durability.

Samuel Colvin, Creator of Pydantic AI, joins Hugo to talk about applying battle-tested software engineering principles to build durable and reliable AI agents.

They Discuss:

* Production agents require engineering-grade reliability: Unlike messy coding agents, production agents need high constraint, reliability, and the ability to perform hundreds of tasks without drifting into unusual behavior;

* Agents are the new “quantum” of AI software: Modern architecture uses discrete “agentlets”: small, specialized building blocks stitched together for sub-tasks within larger, durable systems;

* Stop building “chocolate teapot” execution frameworks: Ditch rudimentary snapshotting; use battle-tested durable execution engines like Temporal for robust retry logic and state management;

* AI observability will be a native feature: In five years, AI observability will be integrated, with token counts and prompt traces becoming standard features of all observability platforms;

* Split agents into deterministic workflows and stochastic activities: Ensure true durability by isolating deterministic workflow logic from stochastic activities (IO, LLM calls) to cache results and prevent redundant model calls;

* Type safety is essential for enterprise agents: Sacrificing type safety for flexible graphs leads to unmaintainable software; professional AI engineering demands strict type definitions for parallel node execution and state recovery;

* Standardize on OpenTelemetry for portability: Use OpenTelemetry (OTel) to ensure agent traces and logs are portable, preventing vendor lock-in and integrating seamlessly into existing enterprise monitoring.

You can also find the full episode on Spotify, Apple Podcasts, and YouTube.

You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!

👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Here is a 25% discount code for listeners. 👈

LINKS

* Samuel Colvin on LinkedIn

* Pydantic

* Pydantic Stack Demo repo

* Deep research example code

* Temporal

* DBOS (Postgres alternative to Temporal)

* Upcoming Events on Luma

* Vanishing Gradients on YouTube

* Watch the podcast video on YouTube

👉Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Our final cohort starts March 10, 2026. Here is a 25% discount code for listeners.👈

https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgfs

There’s a company who spent almost $50,000 because an agent went into an infinite loop and they forgot about it for a month.

It had no failures and I guess no one was monitoring these costs. It’s nice that people do write about that in the database as well. After it happened, they said: watch out for infinite loops. Watch out for cascading tool failures. Watch out for silent failures where the agent reports it has succeeded when it didn’t!

We Discuss:

* Why the most successful teams are ripping out and rebuilding their agent systems every few weeks as models improve, and why over-engineering now creates technical debt you can’t afford later;

* The $50,000 infinite loop disaster and why “silent failures” are the biggest risk in production: agents confidently report success while spiraling into expensive mistakes;

* How ELIOS built emergency voice agents with sub-400ms response times by aggressively throwing away context every few seconds, and why these extreme patterns are becoming standard practice;

* Why DoorDash uses a three-tier agent architecture (manager, progress tracker, and specialists) with a persistent workspace that lets agents collaborate across hours or days;

* Why simple text files and markdown are emerging as the best “continual learning” layer: human-readable memory that persists across sessions without fine-tuning models;

* The 100-to-1 problem: for every useful output, tool-calling agents generate 100 tokens of noise, and the three tactics (reduce, offload, isolate) teams use to manage it;

* Why companies are choosing Gemini Flash for document processing and Opus for long reasoning chains, and how to match models to your actual usage patterns;

* The debate over vector databases versus simple grep and cat, and why giving agents standard command-line tools often beats complex APIs;

* What “re-architect” as a job title reveals about the shift from 70% scaffolding / 30% model to 90% model / 10% scaffolding, and why knowing when to rip things out is the may be the most important skill today.

You can also find the full episode on Spotify, Apple Podcasts, and YouTube.

You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!

👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Our final cohort starts March 10, 2026. Here is a 25% discount code for readers. 👈

Show Notes Links

* Alex Strick van Linschoten on LinkedIn

* Alex Strick van Linschoten on Twitter/X

* LLMOps Database

* LLMOps Database Dataset on Hugging Face

* Hugo’s MCP Server for LLMOps Database

* Alex’s Blog: What 1,200+ Production Deployments Reveal About LLMOps in 2025

* Previous Episode: Practical Lessons from 750 Real-World LLM Deployments

* Previous Episode: Tales from 400 LLM Deployments

* Context Rot Research by Chroma

* Hugo’s Post: AI Agent Harness - 3 Principles for Context Engineering

* Hugo’s Post: The Rise of Agentic Search

* Episode with Nick Moy: The Post-Coding Era

* Hugo’s Personal Podcast Prep Skill Gist

* Claude Tool Search Documentation

* Gastown on GitHub (Steve Yegge)

* Welcome to Gastown by Steve Yegge

* ZenML - Open Source MLOps & LLMOps Framework

* Upcoming Events on Luma

* Vanishing Gradients on YouTube

* Watch the podcast livestream on YouTube

* Join the final cohort of our Building AI Applications course in March, 2026 (25% off for listeners)

👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Our final cohort starts March 10, 2026. Here is a 25% discount code for readers. 👈

> It’s the agent writing the code. And it’s the development loop of writing the code, building testing, write the code, build test and iterating. And so I do think we’ll see for many types of software, a shift away from Python towards other programming languages. I think Go is probably the best language for those like other types of software projects. And like I said, I haven’t written a line of Go code in my life.

– Wes McKinney (creator of pandas Principal Architect at Posit),

Wes McKinney, Marcel Kornacker, and Alison Hill join Hugo to talk about the architectural shift for multimodal AI, the rise of “agent ergonomics,” and the evolving role of developers in an AI-generated future.

We Discuss:

* Agent Ergonomics: Optimize for agent iteration speed, shifting from human coding to fast test environments, potentially favoring languages like Go;

* Adversarial Code Review: Deploy diverse AI models to peer-review agent-generated code, catching subtle bugs humans miss;

* Multimodal Data Verbs: Make operations like resizing and rotating native to your database to eliminate data-plumbing bottlenecks;

* Taste as Differentiator: Value “taste”—the ability to curate and refine the best output from countless AI-generated options—over sheer execution speed;

* 100x Software Volume: Embrace ephemeral, just-in-time software; prioritize aggressive generation and adversarial testing over careful planning for quality.

You can also find the full episode on Spotify, Apple Podcasts, and YouTube.

You can also interact directly with the transcript of the workshop & fireside chat here in NotebookLM: If you do so, let us know anything you find in the comments!

👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Here is a discount code for readers. 👈

This was a fireside chat at the end of a livestreamed workshop we did on building multimodal AI systems with Pixeltable. Check out the full workshop below (all code here on Github):

Links and Resources

* Wes McKinney on LinkedIn

* Marcel Kornacker on LinkedIn

* Alison Hill on LinkedIn

* Spicy Takes

* Palmer Penguins

* Pixeltable

* Posit

* Positron

* Building Multimodal AI Systems Workshop Repository

* Pixeltable Docs: LLM Tool Calling with MCP Servers

* Pixeltable Docs: Working with Pydantic

* Upcoming Events on Luma

* Vanishing Gradients on YouTube

* Watch the podcast video on YouTube

* Join the final cohort of our Building AI Applications course in March, 2026 (25% off for listeners)

https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgfs

What people said during the workshop

“I think the interface looks amazing/simple. Strong work! 🦾” — @goldentribe

“This is quite amazing. Watching this I felt the same way when I first leant pandas, NumPy and scikit and how well i was able to manipulate and wrangle data. PixelTable feels seamless and looks as good as those legendary frameworks but for Multimodal Data.” — @vinod7

“This is all extremely cool to see, I love the API and the approach.” — @steveb4191

“Thanks so much, Hugo! That was very insightful! Great work Alison and Marcel!” — @vinod7

“Just wrapped up watching a replay of the Pixeltable workshop. So cool!! Love the notebooks and working examples. The important parts were covered and worked beautifully 🕺” — @therobbrennan

👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Here is a discount code for readers. 👈

The best way to build a horrible search product? Don’t ever measure anything against what a user wants.

Search veterans Doug Turnbull (Led Search at Reddit + Shopify; Wrote Relevant Search + AI Powered Search) and John Berryman (Early Engineer on Github Copilot; Author of Relevant Search + Prompt Engineering for LLMs), join Hugo to talk about how to build Agentic Search Applications.

We Discuss:

* The evolution of information retrieval as it moves from traditional keyword search toward “agentic search“ and what this means for builders.

* John’s five-level maturity model (you can prototype today!) for AI adoption, moving from Trad Search to conversational AI to asynchronous research assistants that reason about result quality.

* The Agentic Search Builders Playbook, including why and how you should “hand-roll” your own agentic loops to maintain control;

* The importance of “revealed preferences” that LLM-judges often miss (evaluations must use real clickstream data to capture “revealed preferences” that semantic relevance alone cannot infer)

* Patterns and Anti-Patterns for Agentic Search Applications

* Learning and teaching Search in the Age of Agents

You can find the full episode on Spotify, Apple Podcasts, and YouTube.

You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!

👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Here is a discount code for readers. 👈

Doug and Hugo are also doing a free lightning lesson on Feb 20 about How To Build Your First Agentic Search Application! You’ll walk away with a framework & code to build your first agentic search app. Register here to join live or get the recording after.

Links and Resources

Guests

* Arcturus Labs (John’s website)

* Software Doug (Doug’s website)

* John Berryman on LinkedIn

* Doug Turnbull on LinkedIn

Books

* Relevant Search by Doug Turnbull & John Berryman (Manning)

* AI-Powered Search by Doug Turnbull (Manning)

* Prompt Engineering for LLMs by John Berryman (O’Reilly)

Blog Posts

* Incremental AI Adoption for E-commerce by John Berryman

* Roaming RAG – RAG without the Vector Database by John Berryman

* Agents Turn Simple Keyword Search into Compelling Search Experiences by Doug Turnbull

* A Simple Agentic Loop with Just Python Functions by Doug Turnbull

* Agentic Code Generation to Optimize a Search Reranker by Doug Turnbull

* LLM Judges Aren’t the Shortcut You Think by Doug Turnbul (Hugo’s 5 minute video below)

* Malleable Software by Ink & Switch (inc. Geoffrey Lit)

* Patterns and Anti-Patterns for Building with AI by Hugo Bowne-Anderson

Other Resources

* The Rise of Agentic Search, a recent VG Podcast with Jeff Huber

* Karpathy on Cognitive Core LLMs

* Cheat at Search with Agents course by Doug Turnbull (use code: vanishinggradients for $200 off)

* Upcoming Events on Luma

* Vanishing Gradients on YouTube

* Watch the podcast video on YouTube

* Join the final cohort of our Building AI Applications course in Q1, 2026 (25% off for listeners)

Timestamps (for YouTube livestream)

00:00 How to Build Agentic Search & Retrieval Systems

02:48 Defining Search and AI

03:26 Evolution of Search Technologies08:46 Search in E-commerce and Other Domains

12:15 Combining Search and AI: RAG and LLMs

23:50 User Intent and Search Optimization

29:47 Levels of AI Integration in Search

32:25 Exploring the Complexity of Search in Various Domains

33:49 The Evolution and Impact of Agentic Search

34:07 Defining Terms: RAG and Agentic Search

34:52 The Research Loop and Tool Interaction

35:55 Formal Protocols and Structured Outputs

38:39 Building Agentic Search Experiences: Tips and Advice

41:50 The Importance of Empathy in AI and Search Development

54:30 The Role of UX in Search Applications

01:01:15 Future of Search: Malleable User Interfaces

01:02:38 Exploring Malleable Software

01:04:20 The Coordination Challenge in Software Development

01:05:23 The Impact of Claude Code & Claude Cowork

01:06:22 The Future of Knowledge Work with AI

01:12:39 Evaluating Search Algorithms with AI

01:15:15 The Role of Agents in Search Optimization

01:29:55 Teaching AI and Search Techniques

01:34:25 Final Thoughts and Farewell

👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Here is a discount code for readers. 👈

https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgpod

 This is continual learning, right? Everyone has been talking about continual learning as the next challenge in AI. Actually, it’s solved. Just tell it to keep some notes somewhere. Sure, it’s not, it’s not machine learning, but in some ways it is because when it will load this text file again, it will influence what it does … And it works so well: it’s easy to understand. It’s easy to inspect, it’s easy to evolve and modify!

Eleanor Berger and Isaac Flaath, the minds behind Elite AI Assisted Coding, join Hugo to talk about how to redefine software development through effective AI-assisted coding, leveraging “specification-first” approaches and advanced agentic workflows.

We Discuss:

* Markdown learning loops: Use simple agents.md files for agents to self-update rules and persist context, creating inspectable, low-cost learning;

* Intent-first development: As AI commoditizes syntax, defining clear specs and what makes a result “good” becomes the core, durable developer skill;

* Effortless documentation: Leverage LLMs to distill messy “brain dumps” or walks-and-talks into structured project specifications, offloading context faster;

* Modular agent skills: Transition from MCP servers to simple markdown-based “skills” with YAML and scripts, allowing progressive disclosure of tool details;

* Scheduled async agents: Break the chat-based productivity ceiling by using GitHub Actions or Cron jobs for agents to work on issues, shifting humans to reviewers;

* Automated tech debt audits: Deploy background agents to identify duplicate code, architectural drift, or missing test coverage, leveraging AI to police AI-induced messiness;

* Explicit knowledge culture: AI agents eliminate “cafeteria chat” by forcing explicit, machine-readable documentation, solving the perennial problem of lost institutional knowledge;

* Tiered model strategy: Optimize token spend by using high-tier “reasoning” models (e.g., Opus) for planning and low-cost, high-speed models (e.g., Flash) for execution;

* Ephemeral software specs: With near-zero generation costs, software shifts from static products to dynamic, regenerated code based on a permanent, underlying specification.

You can also find the full episode on Spotify, Apple Podcasts, and YouTube.

You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!

👉 Eleanor & Isaac are teaching their next cohort of their Elite AI Assisted Coding course starting this week. They’re kindly giving readers of Vanishing Gradients 25% off. Use this link.👈

👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Here is a discount code for readers. 👈

Show Notes

* Elite AI Assisted Coding Substack

* Eleanor Berger on LinkedIn

* Isaac Flaath on LinkedIn

* Elite AI Assisted Coding Course (Use the code HUGO for 25% off)

* How to Build an AI Agent with AI-Assisted Coding

* Eleanor/Isaac’s blog post “The SpecFlow Process for AI Coding”

* Eleanor’s growing list of (free) tutorials on Agent Skills

* Eleanor’s YouTube playlist on agent skills

* Eleanor’s blog post “Are (Agent) Skills the New Apps”

* Simon Willison’s blog post on skills/general computer automation/data journalism agents

* Eleanor/Isaac’s blog post about asynchronous client agents in GitHub actions

* Eleanor/Isaac’s blog post on agentic coding workflows with Hang Yu, Product Lead for Qoder @ Alibaba

* Upcoming Events on Luma

* Vanishing Gradients on YouTube

* Watch the podcast video on YouTube

* Join the final cohort of our Building AI Applications course in Q1, 2026 (25% off for listeners)

Timestamps (for YouTube livestream)

00:00 Introduction to Elite AI Assisted Coding

02:24 Starting a New AI Project: Best Practices

03:19 The Importance of Context in AI Projects

07:19 Specification-First Planning

12:01 Sharing Intent and Documentation

18:27 Living Documentation and Continual Learning

24:36 Choosing the Right Tools and Models

29:18 Managing Costs and Token Usage

40:16 Using Different Models for Different Tasks

43:41 Mastering One Model for Better Results

44:54 The Rise of Agent Skills in 2026

45:34 Understanding the Importance of Skills

47:18 Practical Applications of Agent Skills

01:11:43 Security Concerns with AI Agents

01:15:02 Collaborative AI-Assisted Coding

01:18:59 Future of AI-Assisted Coding

01:22:27 Key Takeaways for Effective AI-Assisted Coding

Live workshop with Eleanor, Isaac, & Hugo

We also recently did a 90-minute workshop on How to Build an AI Agent with AI-Assisted Coding.

We wrote a blog post on it for those who don’t have 90 minutes right now. Check it out here.

I then made a 4 min video about it all for those who don’t have time to read the blog post.

👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Here is a discount code for readers. 👈

https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vg-ei

Surprise. We don’t have agents. I actually went in and did an audit of all the LLM applications that we’ve developed internally. And if you were to take Anthropic’s definition of workflow versus agent, we don’t have agents. I would not classify any of our applications as agents. x

Eric Ma, who leads Research Data Science in the Data Science and AI group at Moderna, joins Hugo on moving past the hype of autonomous agents to build reliable, high-value workflows.

We discuss:

* Reliable Workflows: Prioritize rigid workflows over dynamic AI agents to ensure reliability and minimize stochasticity in production environments;

* Permission Mapping: The true challenge in regulated environments is security, specifically mapping permissions across source documents, vector stores, and model weights;

* Trace Log Risk: LLM execution traces pose a regulatory risk, inadvertently leaking restricted data like trade secrets or personal information;

* High-Value Data Work: LLMs excel at transforming archived documents and freeform forms into required formats, offloading significant “janitorial” work from scientists;

* “Non-LLM” First: Solve problems with simpler tools like Python or ML models before LLMs to ensure robustness and eliminate generative AI stochasticity;

* Contextual Evaluation: Tailor evaluation rigor to consequences; low-stakes tools can be “vibe-checked,” while patient safety outputs demand exhaustive error characterization;

* Serverless Biotech Backbone: Serverless infrastructure like Modal and reactive notebooks such as Marimo empowers biotech data scientists for rapid deployment without heavy infrastructure overhead.

You can also find the full episode on Spotify, Apple Podcasts, and YouTube.

You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!

👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Our final cohort is in Q1, 2206. Here is a 35% discount code for readers. 👈

https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgch

👉 Eric & Hugo have a free upcoming livestream workshop: Building Tools for Thinking with AI (register to join live or get the recording afterwards) 👈

Show notes

* Eric’s website

* Eric Ma on LinkedIn

* Eric’s blog

* Eric’s data science newsletter

* Building Effective AI Agents by the Anthropic team

* Wow, Marimo from Eric’s blog

* Wow, Modal from Eric’s blog

* Upcoming Events on Luma

* Watch the podcast video on YouTube

* Join the final cohort of our Building AI Applications course in Q1, 2026 (35% off for listeners)

Timestamps

00:00 Defining Agents and Workflows

02:04 Challenges in Regulated Environments

04:24 Eric Ma's Role at Moderna, Leading Research Data Science in the Data Science and AI Group

12:37 Document Reformatting and Automation

15:42 Data Security and Permission Mapping

20:05 Choosing the Right Model for Production

20:41 Evaluating Model Changes with Benchmarks

23:10 Vibe-Based Evaluation vs. Formal Testing

27:22 Security and Fine-Tuning in LLMs

28:45 Challenges and Future of Fine-Tuning

34:00 Security Layers and Information Leakage

37:48 Wrap-Up and Final Remarks

👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Our final cohort is in Q1, 2026. Here is a 35% discount code for readers. 👈

https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgch