Episode 47: The Great Pacific Garbage Patch of Code Slop with Joe Reis
What if the cost of writing code dropped to zero — but the cost of understanding it skyrocketed?
In this episode, Hugo sits down with Joe Reis to unpack how AI tooling is reshaping the software development lifecycle — from experimentation and prototyping to deployment, maintainability, and everything in between.
Joe is the co-author of Fundamentals of Data Engineering and a longtime voice on the systems side of modern software. He’s also one of the sharpest critics of “vibe coding” — the emerging pattern of writing software by feel, with heavy reliance on LLMs and little regard for structure or quality.
We dive into:
• Why “vibe coding” is more than a meme — and what it says about how we build today
• How AI tools expand the surface area of software creation — for better and worse
• What happens to technical debt, testing, and security when generation outpaces understanding
• The changing definition of “production” in a world of ephemeral, internal, or just-good-enough tools
• How AI is flattening the learning curve — and threatening the talent pipeline
• Joe’s view on what real craftsmanship means in an age of disposable code
This conversation isn’t about doom, and it’s not about hype. It’s about mapping the real, messy terrain of what it means to build software today — and how to do it with care.
LINKS
* Joe's Practical Data Modeling Newsletter on Substack (https://practicaldatamodeling.substack.com/)
* Joe's Practical Data Modeling Server on Discord (https://discord.gg/HhSZVvWDBb)
* Vanishing Gradients YouTube Channel (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA)
* Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)
🎓 Want to go deeper?
Check out my course: Building LLM Applications for Data Scientists and Software Engineers.
Learn how to design, test, and deploy production-grade LLM systems — with observability, feedback loops, and structure built in.
This isn’t about vibes or fragile agents. It’s about making LLMs reliable, testable, and actually useful.
Includes over $2,500 in compute credits and guest lectures from experts at DeepMind, Moderna, and more.
Cohort starts April 7 — Use this link for a 10% discount (https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?promoCode=LLM10)
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1:19:12
Episode 46: Software Composition Is the New Vibe Coding
What if building software felt more like composing than coding?
In this episode, Hugo and Greg explore how LLMs are reshaping the way we think about software development—from deterministic programming to a more flexible, prompt-driven, and collaborative style of building. It’s not just hype or grift—it’s a real shift in how we express intent, reason about systems, and collaborate across roles.
Hugo speaks with Greg Ceccarelli—co-founder of SpecStory, former CPO at Pluralsight, and Director of Data Science at GitHub—about the rise of software composition and how it changes the way individuals and teams create with LLMs.
We dive into:
- Why software composition is emerging as a serious alternative to traditional coding
- The real difference between vibe coding and production-minded prototyping
- How LLMs are expanding who gets to build software—and how
- What changes when you focus on intent, not just code
- What Greg is building with SpecStory to support collaborative, traceable AI-native workflows
- The challenges (and joys) of debugging and exploring with agentic tools like Cursor and Claude
We’ve removed the visual demos from the audio—but you can catch our live-coded Chrome extension and JFK document explorer on YouTube. Links below.
JFK Docs Vibe Coding Demo (YouTube) (https://youtu.be/JpXCkuV58QE)
Chrome Extension Vibe Coding Demo (YouTube) (https://youtu.be/ESVKp37jDwc)
Meditations on Tech (Greg’s Substack) (https://www.meditationsontech.com/)
Simon Willison on Vibe Coding (https://simonwillison.net/2025/Mar/19/vibe-coding/)
Johnno Whitaker: On Vibe Coding (https://johnowhitaker.dev/essays/vibe_coding.html)
Tim O’Reilly – The End of Programming (https://www.oreilly.com/radar/the-end-of-programming-as-we-know-it/)
Vanishing Gradients YouTube Channel (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA)
Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)
Greg Ceccarelli on LinkedIn (https://www.linkedin.com/in/gregceccarelli/)
Greg’s Hacker News Post on GOOD (https://news.ycombinator.com/item?id=43557698)
SpecStory: GOOD – Git Companion for AI Workflows (https://github.com/specstoryai/getspecstory/blob/main/GOOD.md)
🎓 Want to go deeper?
Check out my course: Building LLM Applications for Data Scientists and Software Engineers.
Learn how to design, test, and deploy production-grade LLM systems — with observability, feedback loops, and structure built in.
This isn’t about vibes or fragile agents. It’s about making LLMs reliable, testable, and actually useful.
Includes over $2,500 in compute credits and guest lectures from experts at DeepMind, Moderna, and more.
Cohort starts April 7 — Use this link for a 10% discount (https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?promoCode=LLM10)
🔍 Want to help shape the future of SpecStory?
Greg and the team are looking for design partners for their new SpecStory Teams product—built for collaborative, AI-native software development.
If you're working with LLMs in a team setting and want to influence the next wave of developer tools, you can apply here:
👉 specstory.com/teams (https://specstory.com/teams)
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1:08:57
Episode 45: Your AI application is broken. Here’s what to do about it.
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)
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1:17:30
Episode 44: The Future of AI Coding Assistants: Who’s Really in Control?
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)
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1:34:11
Episode 43: Tales from 400+ LLM Deployments: Building Reliable AI Agents in Production
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)
A podcast about all things data, brought to you by data scientist Hugo Bowne-Anderson.
It's time for more critical conversations about the challenges in our industry in order to build better compasses for the solution space! To this end, this podcast will consist of long-format conversations between Hugo and other people who work broadly in the data science, machine learning, and AI spaces. We'll dive deep into all the moving parts of the data world, so if you're new to the space, you'll have an opportunity to learn from the experts. And if you've been around for a while, you'll find out what's happening in many other parts of the data world.
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