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Vanishing Gradients: Daily Summaries Delivered
by Hugo Bowne-Anderson
A podcast for people who build with AI. Long-format conversations with people shaping the field about agents, evals, multimodal systems, data infrastructure, and the tools behind them. Guests include Jeremy Howard (fast.ai), Hamel Husain (Parlance Labs), Shreya Shankar (UC Berkeley), Wes McKinney (creator of pandas), Samuel Colvin (Pydantic) and more.
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- Sep 15, 20261h 52m
How to Build a Coding Agent with Nico (Amp Code)
“It’s an LLM, a loop, and enough tokens.”—Nicolay Gerold, Amp CodeSo why does a coding agent forget your instructions, keep reading tiny pieces of a file, or struggle with an edit another model handles easily?Nicolay Gerold (Amp) joins Hugo to take that loop apart and explain the harness around it: the software that executes tools, manages context, and lets you steer the agent’s work. Nico builds this machinery for Amp, a coding agent that works across software projects.“Every component in a harness is basically an assumption the model can’t do it on its own. This expires over time.”— Nicolay GeroldAmp’s experimental plan mode addressed models that were too eager to edit. Once they could reliably follow an instruction to plan without editing, the team removed the separate mode. Nico describes a similar change with file reading: stronger models could search, filter, and read files through Bash, reducing the need for a dedicated read tool.That gives builders two questions to ask when a model improves: which workarounds can you remove, and what more can you ask it to do? Nico describes pushing stronger models to delegate work and challenge what their sub-agents return. Better judgment makes that delegation more useful, while making some older controls unnecessary.We start with a small agent in Go, then explore Pi, an extensible coding agent, and the decisions behind longer-running work. Building your own makes failures easier to investigate: you can see which instructions survive a summary and whether the agent can inspect the logs and tests it needs. Nico also recommends experimenting with cheaper models, whose mistakes can expose knowledge you’ve left in your head instead of giving to the agent. That understanding stays useful even if you ultimately use someone else’s harness.You can also find the full episode on Spotify, Apple Podcasts, and YouTube.👉 Want to build an agent yourself? Join Build AI Agents from First Principles on September 24, 6–10pm EDT (September 25, 8am–noon Sydney). We’ll start with a single model call and build a working customer-service agent, adding tools and context as we go. You’ll leave with code you can adapt to your own work. Friends of Vanishing Gradients get 10% off with the code NICO10 here.👈“Usually you want to build features that the model is barely able to do right now, but through the increases in intelligence will be able to do easily in the future.”— Nico GeroldIn This Episode* Trace the core loop from a user message to a model’s tool request, the code that executes it, and the result fed into the next call.* Saved sessions, streaming, steering, and cancellation: what Pi adds around the loop, including what should happen to running tools when you hit stop.* Three sources of context, and how repository instructions, development-server logs, and filtered tool results help an agent do its job.* Compaction that loses your instructions, handoff that carries work into a fresh conversation, and why better summaries changed which features Amp needed.* Long plans nobody reads, growing token bills, and the difference between generating a plan and giving a human something useful to review.* Bash versus a dedicated read tool: combining searches and file reads in one call, and the tradeoffs around binary files, restrictions, and line numbers.* String replacement versus patches, and why a tool interface that feels familiar to a model can still lead it into the wrong call.* Recursive language models: using code to inspect tool outputs, where Nico found delegation benefits, and where the context savings disappointed him.* How Nico used agents to build extensions, learned from users’ failures, and turned those failures into evaluation cases… evals for agents!Resources* Nico’s workshop repository: the Go agent, topic explainers, and Pi and Amp extensions used throughout the workshop.* <a target="_blank" href="https://github.com/nicolaygerold/howtobuildacodingagent/blob/main/docs/01-
- Sep 4, 20261h 10m
The Rise of the AI Scientist
“If you feel that a product is hard to eval, or you feel like, ‘I don’t even know how to eval this,’ it’s a strong smell that your product isn’t good.”— Hamel Husain, on AI product designAn AI data agent tells you last quarter’s net revenue. It doesn’t show the metric definition, source tables, filters, query, intermediate calculations, or assumptions. You can’t trust the answer without asking a data scientist to reproduce it.Hamel Husain argues that the eval problem is evidence of bad product design. If the user can’t inspect the work well enough to decide whether the answer is right, another scoring pipeline won’t rescue the experience. The fix begins by exposing the evidence and checks a domain expert actually uses.Follow that problem far enough and you arrive at a broader argument: AI isn’t killing data science. It’s creating more noisy, black-box systems that need hypotheses, experimentation, search expertise, and judgment. Hamel suggests that the people doing this work may eventually be called AI scientists.This episode connects those two ideas. Building AI products people can verify and understanding whether those products work are becoming part of the same job.“AI has made data science way more valuable than ever before, because now you have way more data and way more noisy signals that you need to reason about and debug.”— Hamel Husain, on the rise of the AI scientistYou can also find the full episode on Spotify, Apple Podcasts, and YouTube.👉 Want to build and evaluate AI agents that work in production? Hamel Husain and Shreya Shankar’s AI Evals For Engineers & PMs begins Sep 6, 2026. The course takes you from instrumenting an agent and inspecting traces through validated evaluators, regression testing, red teaming, and improving accuracy, latency, and cost. Vanishing Gradients viewers save 25% with the code hugo-2026. 👈In This Episode* The data agent everyone is building, and why a net-revenue answer without definitions, calculations, provenance, or uncertainty only creates more work.* Hamel’s case that AI has made data science more valuable by producing more traces, more nondeterministic output, and more noisy systems to understand.* How agents can learn from human annotations, improve sampling, and help validate LLM judges without taking human understanding out of the loop.* “Forget evals. Inspect ten traces.” What teams learn by starting with real failures instead of an evaluation framework.* Three AI products redesigned around the expert’s actual process: verifying a financial number, reviewing a workers’ compensation case, and adapting a trusted lesson plan.* Why generic skills have an upper limit, when sharing the shape of a skill works better, and how Hamel turns browser actions into a reusable API.* Shared human-agent canvases, WebMCP, and notebook-like interfaces for preserving evidence, experiments, and state during long-running work.* Your agent has 5,000 trace dimensions. How do hypotheses, exploratory analysis, and dimensionality reduction reveal which signals matter?* RAG is search, the right retrieval metric depends on the product, and choosing that metric still requires human judgment.* What an AI scientist might actually do: form hypotheses, choose analytical tools, design experiments, inspect failures, and decide whether an AI system is working.Resources* “It’s Hard to Eval” Is a Product Smell* The Revenge of the Data Scientist* Hamel’s reverse-engineered-site skill* Hamel’s guide to evaluating agentic workflows* Automating repetitive work at OpenAI with Codex* <a target="_blank" href="ht
- Aug 3, 20261h 18m
If Developers Build on Chinese Open-Weight Models, Who Leads AI?
“It would be sad if local models were not an option and there were only proprietary models. It’s good to have alternatives. Competition is good for business.”— Sebastian Raschka, on open-weight AIKimi K3’s weights landed about an hour before Hugo Bowne-Anderson and Sebastian Raschka went live. Sebastian had already updated his architecture diagram. That speed captures his approach to the current model wave: wait until the weights exist, run the model in the harness where it will actually work, then inspect the architecture closely enough to understand what changed.The conversation arrived during a larger fight over who supplies the models underneath global software. Three days earlier, twenty-five companies including NVIDIA, Meta, Microsoft, Hugging Face, and IBM published Open Weights and American AI Leadership. Their argument closely matches Sebastian’s practical case for local models: open weights create competition, reduce dependence on a single provider, and let organizations choose a model at the right capability and cost.Update: Four days after we recorded, DeepSeek released V4 Flash 0731, a re-post-trained API model for agentic coding. Developers are already reporting that it can debug multi-project codebases and stay on task across very long contexts.You can find the full episode on Spotify, Apple Podcasts, and YouTube.👉 The next cohort of Master Agentic Data Science starts August 4. Hugo is teaching it with Thomas Wiecki and Luca Fiaschi from PyMC Labs. You will build agents that explore data, run predictive and causal workflows, challenge one another’s conclusions, and produce analysis humans can inspect and reproduce. Podcast listeners get 20% off with code MADSAGC20. Join the cohort. 👈In This Episode* Kimi K3’s weights landed about an hour before we went live. Sebastian had already updated his architecture diagram, then walked us through how Kimi Delta Attention and its mixture-of-experts design turn Kimi Linear’s research architecture into a production model.* When a local model is already good enough. Sebastian separates everyday coding and private workloads from problems where the strongest available model, extra reasoning time, and higher cost are justified.* Why the model and harness must be evaluated together. The same open-weight model behaves differently across Qwen Code, Claude Code, Codex, Pi, and other harnesses because context handling, compaction, tool conventions, and post-training all affect the result.* The harness should choose the model and its reasoning effort. Sebastian describes a router that uses the task, conversation history, context, and previous loops to make that decision automatically.* How long agent contexts are changing model architecture. Sebastian moves through grouped-query attention, multi-head latent attention, sparse attention, mixture-of-experts, DeltaNet, and Mamba-style hybrid layers.* The implementation does not lie. A from-scratch model can be checked against open reference weights tensor by tensor, and the first divergence exposes the missing RoPE scaling detail, RMSNorm variant, or architectural assumption that prose left out.* When fine-tuning earns its cost. A specialized task performed thousands of times a day creates different economics from a task performed eight times, so the decision depends on quality, volume, latency, and token cost.* Sebastian keeps the creative work and gives agents the tedious checks. He uses Codex and Claude to check links, enforce editorial conventions, and compare notebook code with the corresponding book manuscript.About Sebastian RaschkaSebastian Raschka is an independent AI researcher, author, and educator focused on large language models, reasoning models, deep learning, and practical machine-learning systems. He created Ahead of AI and is the author of Build a Rea
- Jul 25, 20261h 4m
Four Months Inside a Production AI Agent: What Real Users Changed
When ML/AI Engineer William Horton last joined me, Maven Assistant had reached its first external users the day before. The healthcare AI agent was available to 20 percent of Maven Clinic’s users, and the team had deliberately withheld answers about benefits. A wrong response could shape a decision involving $15,000 of fertility coverage, and the evals had not earned the right to ship it.Four months later, Maven Assistant is available to 100 percent of users, benefits answering is live, and weekly conversation volume has grown by roughly ten times. Real usage also overturned part of the roadmap. The team had invested heavily in provider search and appointment tools, but 50 to 60 percent of early conversations were basic health questions such as whether someone could eat tuna while pregnant.Production changed the engineering system too. An emergency guardrail told someone already in the ER to go to the ER. Zendesk content told people already using the Maven app to open the app. A newer model failed an upcoming-appointments eval because it correctly noticed that the mocked appointments were in the past.William explains how Maven turns those failures into deterministic tests, LLM judges, synthetic negatives, and manual review. He also walks through the move from Gemini Flash models toward newer OpenAI models, what GPT-5.6 and Fable mean for a production agent, why model upgrades can make old prompt instructions obsolete, and why open-weight models still have to justify their GPU, infrastructure, and engineering costs.“If anybody tells you that they’ve got their evaluations so good that they can just swap a model and know, with no manual review, that it’s going to be better, that person is probably lying, or they work at one of three places in the world.”— William Horton, Staff Machine Learning Engineer, Maven ClinicYou can also find the full episode on Spotify, Apple Podcasts, and YouTube.👉 Want to build agents from the ground up? Registration is open for Build AI Agents from First Principles, a live workshop on the loops, tools, context, harnesses, and engineering decisions behind useful AI agents. You’ll learn how to design agent systems from first principles, with enough structure to decide which harness patterns your product actually needs. Sign up today with code production10 for 10% off. 👈In This Episode* What changed between 20 percent and 100 percent rollout. Benefits answering cleared its release bar, Maven Assistant reached the remaining users, and weekly conversation volume grew by roughly ten times.* Why real usage beat the original roadmap. The complex provider and appointment agents received less traffic than expected, while 50 to 60 percent of early conversations were basic health questions.* How production failures enter the evaluation system. William estimates that the deterministic tool-use layer now involves more than 1,000 test scenarios, while clinical quality, empathy, and completeness still require judges, human calibration, and manual work.* When the model is right and the eval is broken. GPT-5.6 Terra rejected an “upcoming” appointment that had already happened, exposing a bad test fixture that the previous model had accepted.* What happens when GPT-5.6 or Fable arrives. A model swap can remove old prompt instructions, add new behavioral failures, change latency and cost, or reveal that yesterday’s harness is constraining a more capable model.* Why Maven moved beyond Gemini Flash 2.5. William discusses adopting newer OpenAI models, keeping real-time chat on smaller models and low reasoning settings, and changing the model without simultaneously rewriting the prompt.* The economics of open-weight models. A smaller self-hosted model still needs an always-on GPU, infrastructure, and engineering attention that could otherwise go into the product.* What William would rebuild today. Provider search and appointment booking probably belong in one agent, and model experiments should begin before the original choice hardens into the architecture.Start With the First EpisodeWilliam first joined Vanishing Gradients the day after Maven Assistant reached external users. In Building an Enterprise AI Agent for Healthcare, he explains the original architecture, how failures become regression cas
- Jul 17, 20261h 8m
Building an Enterprise AI Agent for Healthcare
Every capability in an agent needs its own evidence and release bar. A model-provider slip, an incorrect tool call, and a wrong fertility-benefits answer should not be held to the same pass rate.William Horton, Staff AI Engineer at Maven Clinic, joined us the day after Maven Assistant reached its first external users. The agent helps members inside Maven Clinic’s women’s and family healthcare platform find providers, manage appointments, navigate Maven, and get basic health information. William had spent much of launch day reading chat traces and turning the surprises into product decisions and tests.William shows how a production failure moves through Maven’s system: the trace becomes a regression case, code handles deterministic checks, and LLM judges cover behavior that cannot be reduced to exact outputs. Human labels calibrate those judges, while the consequence of a wrong answer determines whether the capability ships. You can apply the same release workflow to the agent you are building now.“For a lot of our tool-call evaluation, I’ll accept that it runs ten times and passes nine times. Going for that ten out of ten is just not worth the effort.”— William Horton, Staff AI Engineer, Maven ClinicYou can also find the full episode on Spotify, Apple Podcasts, and YouTube.👉 Want to build agents from the ground up? Registration is open for Build AI Agents from First Principles, a live workshop on the loops, tools, context, harnesses, and engineering decisions behind useful AI agents. You'll learn how to design agent systems from first principles, with enough structure to decide which harness patterns your product actually needs. Sign up today with vg-code for 10% off 👈In This Episode* The architecture behind Maven Assistant. A stronger lead agent routes requests to four narrower specialists for appointments, provider search, health questions, and Maven support. Hard guardrails run before dynamic routing.* Why an enterprise healthcare assistant only needs 15 to 20 tools. Maven divides a manageable toolset across its specialists instead of exposing one model to hundreds of choices. Existing APIs become safer agent tools, with user identity and other application state injected by code.* Turn failures into the cheapest reliable eval. A response claiming the agent was “made by Google” became a string check, tool calls are verified deterministically, and LLM judges handle clinical accuracy and other qualitative behavior.* Set release thresholds from the consequences. Nine passes in ten can be acceptable for a cheap failure. Maven withheld benefits answers that could influence tens of thousands of dollars and routes self-harm language directly to human support.* Let production change the product and the test set. Early chats changed the roadmap, became regression cases, exposed weaknesses in the judges, and supplied realistic opening messages for simulated users.Join the Four-Month Follow-UpThis episode was recorded live inside our Building AI Applications course the day after Maven Assistant reached its first external users. By the follow-up four months later, Maven will have a much larger body of real conversations. William will return to compare the launch assumptions with what members actually used, which evals changed, and how newer models altered the system.Register to join the livestream or receive the recording afterwards.Resources* Maven Clinic* Maven introduces Maven Intelligence* Google Agent Development Kit👉 Want to build agents from the ground up? Registration is open for Build AI Agents from First Principles, a live workshop on the
- Jul 8, 20261h 2m
What Claude Fable Means for Coding Agents
Nicolay Gerold works all day and night on AMP, one of the most interesting coding-agent harnesses out there.If you’re building with coding agents, this conversation will help you understand: * when to trust the model, * when to build harnesses around it,* which model is worth paying for, * which programming languages gives the agent better feedback, and * when to take the keyboard back.Coding-agent products are living inside a blender. Opus 4.8 to Fable changes what the model can be trusted with, eats a workflow, and suddenly the best product decision is to delete code.AMP had handoff because long agent threads used to get messy. Compaction would lose the plot, the model would make worse decisions, and the product needed a way to move the work somewhere cleaner. Then compaction got better. The model ate the feature. AMP killed it.Builders inherit the annoying product test: does this harness code help inspect, verify, recover, or merge model work, or is it just babysitting yesterday’s model?Nico and Hugo riff on why loop engineering is overrated (and when to use it), why Fable is the first model with real engineering taste, and why you should stop writing Python code today and start writing TypeScript and Rust for all your AI Engineering workflows.You can also find the full episode on Spotify, Apple Podcasts, and YouTube.👉 Want to build agents from the ground up? Registration is open for Build AI Agents from First Principles, a live workshop on the loops, tools, context, harnesses, and engineering decisions behind useful AI agents. You'll learn how to design agent systems from first principles, with enough structure to decide which harness patterns your product actually needs. Sign up today with vg-code for 10% off 👈In This Episode* Coding-agent harnesses today: compaction, sandboxes, review flows, and the features frontier models are starting to absorb.* Why AMP keeps deleting its own features when models get better.* The test for every harness feature: does it make the agent’s work easier to inspect, verify, or recover from?* Local agents, cloud sandboxes, and where each fits when bugs, issues, logs, or customer feedback turn into code changes.* Background agents without auto-merge fantasy: how useful work comes back as branches, checkouts, or review candidates.* Loop engineering in practice: tight loops with clear objectives, broad loops that create review overload, and where builders should draw the line.* When deterministic code beats an AI step, and when a single agent with the right tools can replace brittle orchestration.* The TikTok problem for coding: hundreds of agent threads, fragmented attention, and why loop engineering can become a trap.- The TikTok problem for coding: hundreds of agent threads, fragmented attention, and why loop engineering can become a trap.Resources* AMP* AMP Owner’s Manual* Nicolay Gerold’s Show Us Your Agent Skills dossier* Clio: Privacy-Preserving Insights into Real-World AI Use* TigerBeetle TigerStyle* How to Build A Coding Agent with Nico and Hugo Build AI Agents From First Principles👉 Want to build agents from the ground up? Registration is open for Build AI Agents from First Principles, a live workshop on the loops, tools, context, harnesses, and engineering decisions behind useful AI agents. You’ll learn how to design agent systems from first principles, with enough structure to decide which harness patterns your product actually needs. Sign up
- May 25, 20261h 4m
The Future of Agentic Data Science
So I think we’re really at a historical moment, and the opportunity is massive. Almost 15 years ago, we were promised that data science was going to be this incredible thing and create all this value for people. And I think nowadays it’s mostly viewed as a cost center in most companies. I think we can really now fulfill that original promise with agentic data science. Thomas Wiecki, Co-creator of PyMC and Founder at PyMC Labs, joins Hugo to talk about how agentic data science is finally fulfilling the promise of Decision Intelligence.We Discuss:* Decision Engines: Transform data science from a cost center providing cryptic answers into a real-time decision intelligence hub delivering actionable outcomes;* Tame the “Garden of Forking Paths”: Overcome human shortcuts by running parallel analyses to provide an honesty check, revealing the true robustness of business conclusions;* Multiplayer Data Science: Foster organizational learning by moving agents into team chats, democratizing “what-if” questions and reducing context-switching friction;* The Full Agentic Data Science Stack: Beyond harness and skills, the full stack includes orchestration for parallel analyses and a causal eval layer to measure actual outcome improvement;* Agentic Dashboards: Move beyond static BI; use chat interfaces to inquire into models and generate real-time, custom visualizations for specific follow-up questions;* Encode Professional Judgment as Skills: Elevate agent performance by encoding expert domain standards and high-fidelity workflows into specific Agent Skills, rather than relying on LLM pre-training;* Ground Decisions in Generative Processes: Prevent hallucinations by forcing agents to model underlying physical or behavioral processes, providing a programmatic guardrail aligned with market realities;* Scripted Causal-Bayesian Workflows: Their methodologically structured nature—from prior elicitation to posterior predictive checks—makes Causal-Bayesian workflows inherently automatable for agents;* Iterative Autonomy via Skills: Achieve autonomy iteratively: verify workflows with human oversight, then encode verifiable parts as skills to hand off trusted tasks;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 how to apply agentic engineering to the world of data science? Come build the future of Agentic Data Science with us in our upcoming course. It’s a live cohort with hands on exercises, capstones, and reusable agent skills, OSS code, and notebooks that will 10x your data science projects. Sign up here and use the code ADSVG10 for 10% off. Hit reply to enquire about group discounts.👈LINKS* Thomas Wiecki on LinkedIn* PyMC Labs* Open-Sourcing Decision Lab: Scaling AI Judgment in Data Science (PyMC Labs blog)* Decision AI Discord* Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results (Sage Journals)* The Agent Harness Reading List</
- May 20, 20261h 19m
Agent-Harness.ipynb*
One thing that I don’t like about Claude is that you get into this weird mental state: oh, I think I trust the model. Let’s do the slot machine. Hit click, which puts you in an inactive mode of thinking. Maybe it’s better to use a worse model….Vincent Warmerdam, senior data professional and prolific open-source maintainer (some packages with over a million downloads), now Engineer at marimo, joins Hugo to talk about how the Python notebook is evolving from a static scratchpad into a working agent harness, and what it takes to stay in the loop as a developer when agents are writing most of the code. This episode was originally a livestream Q&A with the Vanishing Gradients audience.We Discuss:* Shared Notebook Canvas: Notebooks act as a shared memory space where agents and humans co-exist, enabling real-time visual feedback by direct manipulation of global state and UI elements;* Speed-of-Thought Models: Faster, open-weight models like Kimi K2 enhance exploratory flow by keeping humans more alert to the code, unlike frontier models that can induce passive thinking;* Pi as a Harness: Vincent favors an agent harness where agents extend themselves rather than reach for MCP, and where hooks can rigidly constrain which files an agent is allowed to read or touch;* Why PRDs Don’t Fit Notebooks: Notebook work is fundamentally exploratory, so the discipline that works for shipping web apps does not transfer cleanly; the one exception is reproducing a paper;* Interactive Code Review: Interactive UIs (e.g., dragging integers) transform code into a physical object, incentivizing developers to actively review and understand agent logic;* Modular “Lego” Components: Provide agents with high-level, well-tested components (”Lego” code) instead of raw boilerplate, creating systems that are easier to debug and modulate;* Algorithm-Driven Visualization: Let the algorithm dictate the visualization needed, rather than choosing visualizations first, revealing the most interesting structures within the data;* Don’t Outsource the Thinking: Pen and paper architectural planning, walks away from the keyboard, and protecting calm remain the most effective ways to keep producing good ideas in the age of AI-generated software.* Agent Auto-Healing: A marimo-specific linter solved 60% of agent errors overnight by letting agents diagnose and fix their own “slop” without complex prompt engineering;* Incremental Generation: Avoid monolithic LLM outputs; generate code one to two cells at a time to prevent laziness and ensure human oversight and learning;Vincent closes on the idea that calm, not the latest frontier model, is the most underrated tool for building well, and that we should study LLM output the way chess players studied the engines that beat them.Vincent gives several live demos toward the end of the episode. He describes them well enough to follow on audio, but the visuals are worth seeing, so check out the YouTube version here.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 how to apply agentic engineering to the world of data science? Come build the future of Agentic Data Science with us in our upcoming course. It’s a live cohort with hands on exercises, capstones, and reusable agent skills, OSS code, and notebooks that will 10x your data science projects. Sign up here and use the code ADSVG10 for 10% off.👈Also join us for Ep. 3 of <st
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About Vanishing Gradients
A podcast for people who build with AI. Long-format conversations with people shaping the field about agents, evals, multimodal systems, data infrastructure, and the tools behind them. Guests include Jeremy Howard (fast.ai), Hamel Husain (Parlance Labs), Shreya Shankar (UC Berkeley), Wes McKinney (creator of pandas), Samuel Colvin (Pydantic) and more.
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