Domesticating AI

Everyone seems to be hiring AI engineers—but what does that actually mean? Using Claude Code, Cursor, or ChatGPT doesn't automatically make someone an AI engineer. So where's the line between software engineering with AI and engineering AI systems? This week we're joined by Byron McKay , Director of Learning at Gauntlet AI, to discuss how they train engineers for AI roles, what compani...

At a recent meetup, a room of about 55 people was asked: do you know every line of code you shipped to production? One person raised their hand: Chris. That moment became the center of this episode. Not because AI-assisted coding is bad, but because it exposes the real risk: developers are starting to trust AI-generated output without fully owning it. In this episode, Miriah, Chris, and Matt talk...

Everyone is building AI agents. OpenAI SDKs, Claude Code, Deep Agent systems, custom workflows, and orchestration frameworks all promise more autonomous AI. But as these systems become more capable, they start running into familiar engineering problems: retries state management orchestration context control durable execution This week we're joined by Hamza Tahir, CTO and co-founder of ZenML an...

AI is easy to use — but hard to scale. In this episode of Domesticating AI , we’re joined by Daniel Dowler (Red Hat) to break down what actually happens when you move from calling APIs to running AI systems yourself. Recorded on April 21st Most developers interact with AI through APIs — fast, simple, and pay-per-token. But behind the scenes, those systems rely on GPU scheduling, batching, and infr...

Recorded: April 14, 2026 Most people using AI today are still users . They open ChatGPT, call an API, and get an answer. And honestly… it works. But that’s not the same as building with AI. In this episode of Domesticating AI , we break down the difference between AI users and AI practitioners —and why that shift matters if you want reliable systems. We’re joined by Alexandra “Lexi” Pasi, PhD, CEO...

Hosts: Miriah Peterson, Matt Sharp, Chris Brousseau Recorded: April 2026 Status: Released Most AI systems today are designed to be helpful — not secure. In this episode, we break down how AI systems actually get exploited in production: a real supply chain attack on a widely used AI dependency prompt injection and why it still works image-based (multimodal) exploits tool and agent abuse If you’re...

AI can write code — but that doesn’t mean you should trust it. In this episode of Domesticating AI , we’re joined by Tyler Folkman (author of The AI Architect ) to break down how engineers are actually using AI to build software — and why most people are still just vibe coding. Vibe coding vs real engineering Reasoning models vs coding models How to plan and prompt AI effectively When to let AI ta...

Recording Date: February 27, 2026 Hosts: Miriah Peterson, Matt Sharp, Chris Brousseau Running AI locally is easier than ever. Running it securely is another story. In this episode of Domesticating AI , we break down the moment every homelab builder hits: The second you move from one machine to two machines… access becomes your first real engineering problem. We explore the real architecture questi...

📅 Recorded: February 6, 2026 In this episode of Domesticating AI , we discuss why scaling AI systems with more compute often hides weak engineering decisions — especially in agent workflows. We explore constrained hardware, context management, tool calling, logit manipulation, and why small models can make you a better AI engineer. Moltbot / Clawdbot overview (The Verge) https://www.theverge.com/...

Episode 3 is a hardware-first guide to running AI at home. We break down what CPUs vs GPUs vs NPUs vs TPUs actually do in the inference pipeline, why memory capacity isn’t the same as performance (model loading, KV cache, and MoE), why backends/runtimes are real constraints (CUDA vs ROCm vs Metal/MLX vs CPU), and how to scale from one box to multi-GPU and multi-machine setups. Keep your AI on a le...

Everyone keeps calling NVIDIA DGX Spark an “inference box”… but in practice it behaves more like a dev rig . In Ep 2 of Domesticating AI , we break down what Spark is actually good for (AI development + fine-tuning) vs what it isn’t (a magical drop-in inference server). We also dig into why unified memory changes the local-AI experience, the “gateway stack” ( Ollama + Open WebUI ), when you outgro...

Domesticating AI — S01E01: Your First AI at Home Hosts: Miriah Peterson, Matt Sharp, Chris Brousseau This episode is your practical on-ramp to running AI at home : why inference engines matter, what to install first, and how to make “local AI” feel stable instead of fragile. The hosts start with a hardware + market reality check (tinygrad’s tinybox-style “AI server appliance” idea and the ongoing...