Vanishing Gradients

Too many teams are building AI applications without truly understanding why their models fail. Instead of jumping straight to LLM evaluations, dashboards, or vibe checks, how do you actually fix a broken AI app?
In this episode, Hugo speaks with Hamel Husain, longtime ML engineer, open-source contributor, and consultant, about why debugging generative AI systems starts with looking at your data.
In this episode, we dive into:
Why “look at your data” is the best debugging advice no one follows.
How spreadsheet-based error analysis can uncover failure modes faster than complex dashboards.
The role of synthetic data in bootstrapping evaluation.
When to trust LLM judges—and when they’re misleading.
Why most AI dashboards measuring truthfulness, helpfulness, and conciseness are often a waste of time.
If you're building AI-powered applications, this episode will change how you approach debugging, iteration, and improving model performance in production.
LINKS
The podcast livestream on YouTube (https://youtube.com/live/Vz4--82M2_0?feature=share)
Hamel's blog (https://hamel.dev/)
Hamel on twitter (https://x.com/HamelHusain)
Hugo on twitter (https://x.com/hugobowne)
Vanishing Gradients on twitter (https://x.com/vanishingdata)
Vanishing Gradients on YouTube (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA)
Vanishing Gradients on Twitter (https://x.com/vanishingdata)
Vanishing Gradients on Lu.ma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)
Building LLM Application for Data Scientists and SWEs, Hugo course on Maven (use VG25 code for 25% off) (https://maven.com/s/course/d56067f338)
Hugo is also running a free lightning lesson next week on LLM Agents: When to Use Them (and When Not To) (https://maven.com/p/ed7a72/llm-agents-when-to-use-them-and-when-not-to?utm_medium=ll_share_link&utm_source=instructor)

Get full access to Vanishing Gradients at hugobowne.substack.com/subscribe

AI coding assistants are reshaping how developers write, debug, and maintain code—but who’s really in control? In this episode, Hugo speaks with Tyler Dunn, CEO and co-founder of Continue, an open-source AI-powered code assistant that gives developers more customization and flexibility in their workflows.
In this episode, we dive into:
- The trade-offs between proprietary vs. open-source AI coding assistants—why open-source might be the future.
- How structured workflows, modular AI, and customization help developers maintain control over their tools.
- The evolution of AI-powered coding, from autocomplete to intelligent code suggestions and beyond.
- Why the best developer experiences come from sensible defaults with room for deeper configuration.
- The future of LLM-based software engineering, where fine-tuning models on personal and team-level data could make AI coding assistants even more effective.
With companies increasingly integrating AI into development workflows, this conversation explores the real impact of these tools—and the importance of keeping developers in the driver's seat.
LINKS
The podcast livestream on YouTube (https://youtube.com/live/8QEgVCzm46U?feature=share)
Continue's website (https://www.continue.dev/)
Continue is hiring! (https://www.continue.dev/about-us)
amplified.dev: We believe in a future where developers are amplified, not automated (https://amplified.dev/)
Beyond Prompt and Pray, Building Reliable LLM-Powered Software in an Agentic World (https://www.oreilly.com/radar/beyond-prompt-and-pray/)
LLMOps Lessons Learned: Navigating the Wild West of Production LLMs 🚀 (https://www.zenml.io/blog/llmops-lessons-learned-navigating-the-wild-west-of-production-llms)
Building effective agents by Erik Schluntz and Barry Zhang, Anthropic (https://www.anthropic.com/research/building-effective-agents)
Ty on LinkedIn (https://www.linkedin.com/in/tylerjdunn/)
Hugo on twitter (https://x.com/hugobowne)
Vanishing Gradients on twitter (https://x.com/vanishingdata)
Vanishing Gradients on YouTube (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA)
Vanishing Gradients on Twitter (https://x.com/vanishingdata)
Vanishing Gradients on Lu.ma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)

Get full access to Vanishing Gradients at hugobowne.substack.com/subscribe

Hugo speaks with Alex Strick van Linschoten, Machine Learning Engineer at ZenML and creator of a comprehensive LLMOps database documenting over 400 deployments. Alex's extensive research into real-world LLM implementations gives him unique insight into what actually works—and what doesn't—when deploying AI agents in production.
In this episode, we dive into:
- The current state of AI agents in production, from successes to common failure modes
- Practical lessons learned from analyzing hundreds of real-world LLM deployments
- How companies like Anthropic, Klarna, and Dropbox are using patterns like ReAct, RAG, and microservices to build reliable systems
- The evolution of LLM capabilities, from expanding context windows to multimodal applications
- Why most companies still prefer structured workflows over fully autonomous agents
We also explore real-world case studies of production hurdles, including cascading failures, API misfires, and hallucination challenges. Alex shares concrete strategies for integrating LLMs into your pipelines while maintaining reliability and control.
Whether you're scaling agents or building LLM-powered systems, this episode offers practical insights for navigating the complex landscape of LLMOps in 2025.
LINKS
The podcast livestream on YouTube (https://youtube.com/live/-8Gr9fVVX9g?feature=share)
The LLMOps database (https://www.zenml.io/llmops-database)
All blog posts about the database (https://www.zenml.io/category/llmops)
Anthropic's Building effective agents essay (https://www.anthropic.com/research/building-effective-agents)
Alex on LinkedIn (https://www.linkedin.com/in/strickvl/)
Hugo on twitter (https://x.com/hugobowne)
Vanishing Gradients on twitter (https://x.com/vanishingdata)
Vanishing Gradients on YouTube (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA)
Vanishing Gradients on Twitter (https://x.com/vanishingdata)
Vanishing Gradients on Lu.ma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)

Get full access to Vanishing Gradients at hugobowne.substack.com/subscribe

In this episode of Vanishing Gradients, the tables turn as Hugo sits down with Alex Andorra, host of Learning Bayesian Statistics. Hugo shares his journey from mathematics to AI, reflecting on how Bayesian inference shapes his approach to data science, teaching, and building AI-powered applications.
They dive into the realities of deploying LLM applications, overcoming “proof-of-concept purgatory,” and why first principles and iteration are critical for success in AI. Whether you’re an educator, software engineer, or data scientist, this episode offers valuable insights into the intersection of AI, product development, and real-world deployment.
LINKS
The podcast on YouTube (https://www.youtube.com/watch?v=BRIYytbqtP0)
The original podcast episode (https://learnbayesstats.com/episode/122-learning-and-teaching-in-the-age-of-ai-hugo-bowne-anderson)
Alex Andorra on LinkedIn (https://www.linkedin.com/in/alex-andorra/)
Hugo on LinkedIn (https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/)
Hugo on twitter (https://x.com/hugobowne)
Vanishing Gradients on twitter (https://x.com/vanishingdata)
Hugo's "Building LLM Applications for Data Scientists and Software Engineers" course (https://maven.com/s/course/d56067f338)

Get full access to Vanishing Gradients at hugobowne.substack.com/subscribe

Hugo Bowne-Anderson hosts a panel discussion from the MLOps World and Generative AI Summit in Austin, exploring the long-term growth of AI by distinguishing real problem-solving from trend-based solutions. If you're navigating the evolving landscape of generative AI, productionizing models, or questioning the hype, this episode dives into the tough questions shaping the field.
The panel features:
- Ben Taylor (Jepson) (https://www.linkedin.com/in/jepsontaylor/) – CEO and Founder at VEOX Inc., with experience in AI exploration, genetic programming, and deep learning.
- Joe Reis (https://www.linkedin.com/in/josephreis/) – Co-founder of Ternary Data and author of Fundamentals of Data Engineering.
- Juan Sequeda (https://www.linkedin.com/in/juansequeda/) – Principal Scientist and Head of AI Lab at Data.World, known for his expertise in knowledge graphs and the semantic web.
The discussion unpacks essential topics such as:
- The shift from prompt engineering to goal engineering—letting AI iterate toward well-defined objectives.
- Whether generative AI is having an electricity moment or more of a blockchain trajectory.
- The combinatorial power of AI to explore new solutions, drawing parallels to AlphaZero redefining strategy games.
- The POC-to-production gap and why AI projects stall.
- Failure modes, hallucinations, and governance risks—and how to mitigate them.
- The disconnect between executive optimism and employee workload.
Hugo also mentions his upcoming workshop on escaping Proof-of-Concept Purgatory, which has evolved into a Maven course "Building LLM Applications for Data Scientists and Software Engineers" launching in January (https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?utm_campaign=8123d0&utm_medium=partner&utm_source=instructor). Vanishing Gradient listeners can get 25% off the course (use the code VG25), with $1,000 in Modal compute credits included.
A huge thanks to Dave Scharbach and the Toronto Machine Learning Society for organizing the conference and to the audience for their thoughtful questions.
As we head into the new year, this conversation offers a reality check amidst the growing AI agent hype.
LINKS
Hugo on twitter (https://x.com/hugobowne)
Hugo on LinkedIn (https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/)
Vanishing Gradients on twitter (https://x.com/vanishingdata)
"Building LLM Applications for Data Scientists and Software Engineers" course (https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?utm_campaign=8123d0&utm_medium=partner&utm_source=instructor).

Get full access to Vanishing Gradients at hugobowne.substack.com/subscribe

Hugo speaks with Charles Frye, Developer Advocate at Modal and someone who really knows GPUs inside and out. If you’re a data scientist, machine learning engineer, AI researcher, or just someone trying to make sense of hardware for LLMs and AI workflows, this episode is for you.
Charles and Hugo dive into the practical side of GPUs—from running inference on large models, to fine-tuning and even training from scratch. They unpack the real pain points developers face, like figuring out:
- How much VRAM you actually need.
- Why memory—not compute—ends up being the bottleneck.
- How to make quick, back-of-the-envelope calculations to size up hardware for your tasks.
- And where things like fine-tuning, quantization, and retrieval-augmented generation (RAG) fit into the mix.
One thing Hugo really appreciate is that Charles and the Modal team recently put together the GPU Glossary—a resource that breaks down GPU internals in a way that’s actually useful for developers. We reference it a few times throughout the episode, so check it out in the show notes below.
🔧 Charles also does a demo during the episode—some of it is visual, but we talk through the key points so you’ll still get value from the audio. If you’d like to see the demo in action, check out the livestream linked below.
This is the "Building LLM Applications for Data Scientists and Software Engineers" course that Hugo is teaching with Stefan Krawczyk (ex-StitchFix) in January (https://maven.com/s/course/d56067f338). Charles is giving a guest lecture at on hardware for LLMs, and Modal is giving all students $1K worth of compute credits (use the code VG25 for $200 off).
LINKS
The livestream on YouTube (https://www.youtube.com/live/INryb8Hjk3c?si=0cbb0-Nxem1P987d)
The GPU Glossary (https://modal.com/gpu-glossary) by the Modal team
What We’ve Learned From A Year of Building with LLMs (https://applied-llms.org/) by Charles and friends
Charles on twitter (https://x.com/charles_irl)
Hugo on twitter (https://x.com/hugobowne)
Vanishing Gradients on twitter (https://x.com/vanishingdata)

Get full access to Vanishing Gradients at hugobowne.substack.com/subscribe

Hugo speaks with Ravin Kumar,*Senior Research Data Scientist at Google Labs. Ravin’s career has taken him from building rockets at SpaceX to driving data science and technology at Sweetgreen, and now to advancing generative AI research and applications at Google Labs and DeepMind. His multidisciplinary experience gives him a rare perspective on building AI systems that combine technical rigor with practical utility.
In this episode, we dive into:
• Ravin’s fascinating career path, including the skills and mindsets needed to work effectively with AI and machine learning models at different stages of the pipeline.
• How to build generative AI systems that are scalable, reliable, and aligned with user needs.
• Real-world applications of generative AI, such as using open weight models such as Gemma to help a bakery streamline operations—an example of delivering tangible business value through AI.
• The critical role of UX in AI adoption, and how Ravin approaches designing tools like Notebook LM with the user journey in mind.
We also include a live demo where Ravin uses Notebook LM to analyze my website, extract insights, and even generate a podcast-style conversation about me. While some of the demo is visual, much can be appreciated through audio, and we’ve added a link to the video in the show notes for those who want to see it in action. We’ve also included the generated segment at the end of the episode for you to enjoy.
LINKS
The livestream on YouTube (https://www.youtube.com/live/ffS6NWqoo_k)
Google Labs (https://labs.google/)
Ravin's GenAI Handbook (https://ravinkumar.com/GenAiGuidebook/book_intro.html)
Breadboard: A library for prototyping generative AI applications (https://breadboard-ai.github.io/breadboard/)
As mentioned in the episode, Hugo is teaching a four-week course, Building LLM Applications for Data Scientists and SWEs, co-led with Stefan Krawczyk (Dagworks, ex-StitchFix). The course focuses on building scalable, production-grade generative AI systems, with hands-on sessions, $1,000+ in cloud credits, live Q&As, and guest lectures from industry experts.
Listeners of Vanishing Gradients can get 25% off the course using this special link (https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?promoCode=VG25) or by applying the code VG25 at checkout.

Get full access to Vanishing Gradients at hugobowne.substack.com/subscribe

Hugo speaks with Jason Liu, an independent AI consultant with experience at Meta and Stitch Fix. At Stitch Fix, Jason developed impactful AI systems, like a $50 million product similarity search and the widely adopted Flight recommendation framework. Now, he helps startups and enterprises design and deploy production-level AI applications, with a focus on retrieval-augmented generation (RAG) and scalable solutions.
This episode is a bit of an experiment. Instead of our usual technical deep dives, we’re focusing on the world of AI consulting and freelancing. We explore Jason’s consulting playbook, covering how he structures contracts to maximize value, strategies for moving from hourly billing to securing larger deals, and the mindset shift needed to align incentives with clients. We’ll also discuss the challenges of moving from deterministic software to probabilistic AI systems and even do a live role-playing session where Jason coaches me on client engagement and pricing pitfalls.
LINKS
The livestream on YouTube (https://youtube.com/live/9CFs06UDbGI?feature=share)
Jason's Upcoming course: AI Consultant Accelerator: From Expert to High-Demand Business (https://maven.com/indie-consulting/ai-consultant-accelerator?utm_campaign=9532cc&utm_medium=partner&utm_source=instructor)
Hugo's upcoming course: Building LLM Applications for Data Scientists and Software Engineers (https://maven.com/s/course/d56067f338)
Jason's website (https://jxnl.co/)
Jason's indie consulting newsletter (https://indieconsulting.podia.com/)
Your AI Product Needs Evals by Hamel Husain (https://hamel.dev/blog/posts/evals/)
What We’ve Learned From A Year of Building with LLMs (https://applied-llms.org/)
Dear Future AI Consultant by Jason (https://jxnl.co/writing/#dear-future-ai-consultant)
Alex Hormozi's books (https://www.acquisition.com/books)
The Burnout Society by Byung-Chul Han (https://www.sup.org/books/theory-and-philosophy/burnout-society)
Jason on Twitter (https://x.com/jxnlco)
Vanishing Gradients on Twitter (https://twitter.com/vanishingdata)
Hugo on Twitter (https://twitter.com/hugobowne)
Vanishing Gradients' lu.ma calendar (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)
Vanishing Gradients on YouTube (https://www.youtube.com/@vanishinggradients)

Get full access to Vanishing Gradients at hugobowne.substack.com/subscribe

Hugo speaks with three leading figures from the world of AI research: Sander Schulhoff, a recent University of Maryland graduate and lead contributor to the Learn Prompting initiative; Philip Resnik, professor at the University of Maryland, known for his pioneering work in computational linguistics; and Dennis Peskoff, a researcher from Princeton specializing in prompt engineering and its applications in the social sciences.
This is Part 2 of a special two-part episode, prompted—no pun intended—by these guys being part of a team, led by Sander, that wrote a 76-page survey analyzing prompting techniques, agents, and generative AI. The survey included contributors from OpenAI, Microsoft, the University of Maryland, Princeton, and more.
In this episode, we cover:
The Prompt Report: A comprehensive survey on prompting techniques, agents, and generative AI, including advanced evaluation methods for assessing these techniques.
Security Risks and Prompt Hacking: A detailed exploration of the security concerns surrounding prompt engineering, including Sander’s thoughts on its potential applications in cybersecurity and military contexts.
AI’s Impact Across Fields: A discussion on how generative AI is reshaping various domains, including the social sciences and security.
Multimodal AI: Updates on how large language models (LLMs) are expanding to interact with images, code, and music.
Case Study - Detecting Suicide Risk: A careful examination of how prompting techniques are being used in important areas like detecting suicide risk, showcasing the critical potential of AI in addressing sensitive, real-world challenges.
The episode concludes with a reflection on the evolving landscape of LLMs and multimodal AI, and what might be on the horizon.
If you haven’t yet, make sure to check out Part 1, where we discuss the history of NLP, prompt engineering techniques, and Sander’s development of the Learn Prompting initiative.
LINKS
The livestream on YouTube (https://youtube.com/live/FreXovgG-9A?feature=share)
The Prompt Report: A Systematic Survey of Prompting Techniques (https://arxiv.org/abs/2406.06608)
Learn Prompting: Your Guide to Communicating with AI (https://learnprompting.org/)
Vanishing Gradients on Twitter (https://twitter.com/vanishingdata)
Hugo on Twitter (https://twitter.com/hugobowne)
Vanishing Gradients' lu.ma calendar (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)
Vanishing Gradients on YouTube (https://www.youtube.com/@vanishinggradients)

Get full access to Vanishing Gradients at hugobowne.substack.com/subscribe

Hugo speaks with three leading figures from the world of AI research: Sander Schulhoff, a recent University of Maryland graduate and lead contributor to the Learn Prompting initiative; Philip Resnik, professor at the University of Maryland, known for his pioneering work in computational linguistics; and Dennis Peskoff, a researcher from Princeton specializing in prompt engineering and its applications in the social sciences.
This is Part 1 of a special two-part episode, prompted—no pun intended—by these guys being part of a team, led by Sander, that wrote a 76-page survey analyzing prompting techniques, agents, and generative AI. The survey included contributors from OpenAI, Microsoft, the University of Maryland, Princeton, and more.
In this first part,
* we’ll explore the critical role of prompt engineering,
* & diving into adversarial techniques like prompt hacking and
* the challenges of evaluating these techniques.
* we’ll examine the impact of few-shot learning and
* the groundbreaking taxonomy of prompting techniques from the Prompt Report.
Along the way,
* we’ll uncover the rich history of natural language processing (NLP) and AI, showing how modern prompting techniques evolved from early rule-based systems and statistical methods.
* we’ll also hear how Sander’s experimentation with GPT-3 for diplomatic tasks led him to develop Learn Prompting, and
* how Dennis highlights the accessibility of AI through prompting, which allows non-technical users to interact with AI without needing to code.
Finally, we’ll explore the future of multimodal AI, where LLMs interact with images, code, and even music creation. Make sure to tune in to Part 2, where we dive deeper into security risks, prompt hacking, and more.
LINKS
The livestream on YouTube (https://youtube.com/live/FreXovgG-9A?feature=share)
The Prompt Report: A Systematic Survey of Prompting Techniques (https://arxiv.org/abs/2406.06608)
Learn Prompting: Your Guide to Communicating with AI (https://learnprompting.org/)
Vanishing Gradients on Twitter (https://twitter.com/vanishingdata)
Hugo on Twitter (https://twitter.com/hugobowne)
Vanishing Gradients' lu.ma calendar (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)
Vanishing Gradients on YouTube (https://www.youtube.com/@vanishinggradients)

Get full access to Vanishing Gradients at hugobowne.substack.com/subscribe