Domesticating AI
Domesticating AI is a bi-weekly podcast focused on practical AI for developers. It covers self-hosted models, local AI, homelabs, hardware, agents, security, and reliability. The show is hosted by software engineer Miriah Peterson, AI engineer Matt Sharp, and NLP practitioner Chris Brousseau, who bring production-first perspectives to building with AI.
Episodes
Open Source Projects Every AI Homelabber Should Know
Everyone has a list of open-source AI projects they want to try. But installing repos is not the same as building a stack.Ryan Russon joins Miriah, Matt, and Chris to talk through the projects that actually teach you how AI homelabs work: Unsloth for training, Ollama and llama.cpp for local inference, Open WebUI for a usable front door, K3s for hosting, Longhorn and SeaweedFS for storage, and Prom
CrankGPT: The Hand-Powered AI That Exposes the Cost of Inference
What happens when the cost of running AI is no longer hidden behind a cloud bill—but something you can physically feel?In this episode of Domesticating AI, Kat Tomanek and Alex Kauffman of Squeez Labs introduce CrankGPT, a fully offline voice assistant that runs on a Raspberry Pi and is powered by a hand crank. As the language model performs inference and generates speech, the crank becomes harder
AI Homelab Hardware: What You Actually Need to Run Local LLMs
You don’t need a DGX Spark to start running local AI.In this episode, Miriah, Matt, and Chris talk about realistic AI homelab hardware: old gaming PCs, refurbished desktops, Raspberry Pis, MacBooks, RTX cards, Mac Studio, DGX Spark, AMD Strix Halo machines, and cloud GPU rentals.The argument is simple: start with the hardware you already have, learn the constraints, then decide what to buy.We cove
The Skills Every AI Engineer Needs in 2026
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 companies
Trust AI? Stop Shipping Output You Didn’t Read
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 abo
Stop Building AI Agents: Build Harnesses Instead | Hamza Tahir (ZenML / Kitaru)
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:retriesstate managementorchestrationcontext controldurable executionThis week we're joined by Hamza Tahir, CTO and co-founder of ZenML and creat
Self-Hosting AI: Scaling Is the Real Problem
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 21stMost developers interact with AI through APIs — fast, simple, and pay-per-token. But behind the scenes, those systems rely on GPU scheduling, batching, and infrastr
You’re Using AI Wrong: Build the System, Not Just the Prompt /w Lexi Pasi
Recorded: April 14, 2026Most 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 of Lucid
Hacking AI: Why Most AI Systems Are Insecure by Default
Hosts: Miriah Peterson, Matt Sharp, Chris BrousseauRecorded: April 2026Status: ReleasedMost 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 dependencyprompt injection and why it still worksimage-based (multimodal) exploitstool and agent abuseIf you’re building
Coding with AI: Vibe Coding vs Real Engineering (with Tyler Folkman)
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 engineeringReasoning models vs coding modelsHow to plan and prompt AI effectivelyWhen to let AI take the
Securing Your Homelab: AI Infrastructure, Access Control & Why Docker Isn’t Isolation
Recording Date: February 27, 2026Hosts: Miriah Peterson, Matt Sharp, Chris BrousseauRunning 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 questions behi
Agents Don’t Need More Compute — They Need Better Engineering
📅 Recorded: February 6, 2026In 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/repor
Hardware-First Home AI: Chips, Memory, Backends, and What to Buy
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 lea
From “Inference Box” to Dev Rig: What NVIDIA DGX Spark Actually Is | Ep 2
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 outgrow tur
Your First AI at Home
Domesticating AI — S01E01: Your First AI at HomeHosts: Miriah Peterson, Matt Sharp, Chris BrousseauThis 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 mem
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