Domesticating AI

SoyPete Tech

Domesticating AI is a bi-weekly podcast about practical AI for developers. We cover self-hosted models, local AI, homelabs, hardware, agents, security, and reliability so software engineers can build

- Miriah Peterson: Software engineer, Go educator, and community builder focused on *production-first* AI. Runs SoyPete Tech (streams + writing + open-source).
- Matt Sharp: AI Engineer/Strategist, co-author of *LLMs in Production*, MLOps practitioner. Writes **The Data Pioneer**.
- Chris Brousseau: NLP practitioner, co-author of LLMs in Production, VP of AI at VEOX. You can find him as IMJONEZZ

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S1 · E16
Friday · 52 min
Your Homelab Is Where the Fun Starts
Your side project starts on the free tier. Then it gets interesting. This week, Tod from Catalyst Community joins us to talk about why the homelab is one of the best places to build open-source software, experiment with AI, and learn the infrastructure underneath the tools we use every day. We get into why Kubernetes does not need to turn you into a Kubernetes administrator, how Foundry is making it easier to stand up K3s on your own hardware, and why tools like Helm make adding databases, monitoring, storage, and other services much less painful once the foundation exists. Tod walks us through his own homelab: Proxmox, Debian, a three-node Kubernetes cluster, separate storage and GPU machines, and the open-source tooling he uses to build on top of it. We also get into Tailscale, KServe, Ray, CloudNativePG, Prometheus, Grafana, Loki, Whisper, and running GPUs both inside and outside Kubernetes. But the bigger conversation is about building. Matt talks about the GPU machine he originally bought to train a StarCraft bot and how that experimentation turned into skills he could use professionally. Miriah talks about replacing a Supabase free-tier database with Postgres on her homelab. Tod talks about replacing GitHub-hosted CI/CD infrastructure with his own open-source system. The common thread: doing the foundational work once gives you a platform where the next idea is much cheaper to try. And with modern hardware, open-source software, and LLM-assisted development, the barrier to building something useful at home has never been lower. So our question for you: What are you building for yourself, for fun, or for open source? Projects mentioned in the episode include Foundry, K3s, Kubernetes, Helm, Proxmox, Tailscale, KServe, Ray, CloudNativePG, Prometheus, Grafana, Loki, MLflow, Whisper, Render, Supabase, and Anthropic Claude. Keep your AI on a leash.
S1 · E15
August 14 · 41 min
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 Prometheus/Grafana for observability. We also get into build-vs-buy decisions, speech models like Whisper.cpp, Parakeet, and Granite, why state is where homelabs stop being cute, and why the best open-source projects are the ones that teach you what breaks next. Links mentioned: https://github.com/unslothai/unsloth https://ollama.com/ https://github.com/ggml-org/llama.cpp https://github.com/open-webui/open-webui https://k3s.io/ https://longhorn.io/ https://github.com/seaweedfs/seaweedfs https://prometheus.io/ https://grafana.com/ https://github.com/Arize-ai/phoenix https://github.com/langfuse/langfuse Support the show: https://patreon.com/DomesticatingAIPodcast Keep your AI on a leash.
S1 · E14
July 31 · 46 min
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 to turn, converting an abstract computational cost into physical exertion. CrankGPT began as an experiment: could Squeez Labs build a responsive, completely self-contained voice AI system without a cloud connection, wall outlet, or battery? Answering that question required much more than placing a small language model on a Raspberry Pi. Kat and Alex had to engineer the complete system, including speech recognition, voice activity detection, language-model inference, text-to-speech, boot time, memory usage, power delivery, and conversational latency. The result is a modular voice stack that uses specialized components rather than one enormous multimodal model. That architecture allowed the team to select each model according to the actual requirements of the device. The voice only needs to be understandable. The language model needs to respond quickly enough for conversation. The speech recognizer needs to run efficiently on a CPU. Anything beyond those requirements consumes resources without necessarily improving the experience. We discuss why local AI is not simply about buying an expensive GPU, why developers should begin with the machines they already own, and how constraints reveal the hardware characteristics that actually matter. In CrankGPT’s case, memory bandwidth and cold-start behavior created more significant limitations than several of the specifications the team initially expected to matter. The conversation also examines a larger problem in the AI industry: developers frequently select the largest available model before defining the problem they need to solve. Cloud services conceal the energy, infrastructure, privacy, and financial costs of those decisions. CrankGPT makes those costs visible—and makes a strong case for engineering systems around specific requirements instead of throwing more compute at every problem. We also cover Pinokio as an accessible entry point for experimenting with local AI, Whisper and whisper.cpp for speech recognition, modular voice-agent architecture, Raspberry Pi optimization, and why some supposedly ideal AI use cases are still better solved with a book, calculator, or conventional software. CrankGPT and build documentation:https://squeezlabs.github.io/handcrank/ CrankGPT:https://crankgpt.com/ Squeez Labs:https://squeezlabs.com/ Edge Voice Agent:https://github.com/ktomanek/edge_voice_agent Pinokio:https://pinokio.co/ whisper.cpp:https://github.com/ggml-org/whisper.cpp OpenAI Whisper:https://github.com/openai/whisper llama.cpp:https://github.com/ggml-org/llama.cpp Liquid AI:https://www.liquid.ai/ Gemma:https://deepmind.google/models/gemma/ DietPi:https://dietpi.com/ Raspberry Pi:https://www.raspberrypi.com/ Domesticating AI is a biweekly podcast about practical AI for developers. We help you brave the feral open-source AI landscape so you can tame it instead of getting dragged along by it. Keep your AI on a leash. Resources
S1 · E13
July 17 · 40 min
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 cover inference, small-model training, coding agents, local harnesses, OpenCode, Forge, LM Studio, Ollama, Docker, Podman AI Lab, vLLM, Lemonade, Unsloth, RunPod, Modal, and why hardware decisions should follow the workload instead of YouTube FOMO. We also get into the real reason local AI matters: data control, IP protection, regulated workflows, reproducibility, and understanding the engineering trade-offs instead of treating the model like magic. Comment question: What are you running local AI on right now, and what do you want to buy next? Links mentioned: LLMs in Production: https://www.manning.com/books/llms-in-production Andrew Ng Machine Learning Specialization: https://www.coursera.org/specializations/machine-learning-introduction fast.ai Practical Deep Learning for Coders: https://course.fast.ai/ NVIDIA DGX Spark: https://marketplace.nvidia.com/en-us/enterprise/personal-ai-supercomputers/dgx-spark/ NVIDIA Jetson Orin Nano: https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/nano-super-developer-kit/ Framework Desktop: https://frame.work/desktop Beelink GTR9 Pro: https://www.bee-link.com/products/beelink-gtr9-pro-amd-ryzen-ai-max-395 RunPod: https://www.runpod.io/ Modal: https://modal.com/ OpenRouter free models: https://openrouter.ai/collections/free-models OpenCode: https://opencode.ai/ OpenCode Zen: https://opencode.ai/docs/zen/ Forge: https://github.com/antoinezambelli/forge LM Studio: https://lmstudio.ai/ Ollama: https://ollama.com/ Docker Model Runner: https://www.docker.com/products/model-runner/ Podman AI Lab: https://podman-desktop.io/docs/ai-lab vLLM: https://docs.vllm.ai/ AMD Lemonade Server: https://lemonade-server.ai/ Unsloth: https://unsloth.ai/docs Follow Domesticating AI: YouTube: https://www.youtube.com/@DomesticatingAI Apple Podcasts: https://podcasts.apple.com/us/podcast/domesticating-ai/id1873338950 Spotify: https://open.spotify.com/show/2WsAR4fvcXzp3vVZGVlkE2 Amazon Music: https://music.amazon.com/podcasts/193658ea-5d7e-40c5-b78c-dbc1f6ada65a/domesticating-ai Patreon: https://patreon.com/DomesticatingAIPodcast Keep your AI on a leash.
S1 · E12
July 3 · 37 min
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 are actually looking for, and why the most important AI engineering skills have surprisingly little to do with prompting. We explore why system design has become essential, why RAG is far from dead, whether you need to self-host or fine-tune models, and why communication and engineering fundamentals are still the biggest predictors of success. If you're wondering what skills to invest in next—or trying to break into AI engineering—this episode is for you. What actually defines an AI engineer? Why system design matters more than ever Is RAG still relevant in 2026? The difference between AI users and AI engineers Why engineering fundamentals still matter Communication as a technical skill Why experimentation is part of the job Do you need to fine-tune or self-host models? How Gauntlet AI prepares engineers for AI careers Advice for learning AI without chasing every new framework Gauntlet AI — https://gauntletai.com LLMs in Production — https://www.manning.com/books/llms-in-production Claude Code — https://www.anthropic.com/claude-code Ray — https://www.ray.io/ Qwen — https://qwen.ai/blog?id=qwen3.5 UV Package Manager — https://docs.astral.sh/uv/ Domesticating AI is a bi-weekly podcast for software engineers building practical AI systems. We cover self-hosted AI, agents, infrastructure, context engineering, security, and the engineering practices that make AI reliable in production. Subscribe wherever you get your podcasts, and if you're enjoying the show, leave a rating and review—it helps more engineers discover the show. Keep your AI on a leash.
S1 · E11
June 19 · 36 min
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 about AI psychosis: the slow offloading of judgment, skepticism, and responsibility to systems that sound confident by design. We dig into AI slop, sycophantic models, no-slop.ai, Mitchell Hashimoto’s warning about companies operating under AI psychosis, and why “Claude wrote it” is not a defense when production breaks. This is not an anti-AI episode. We use AI constantly. The point is to stop treating AI like an oracle and start treating it like a tool that needs constraints, review, and ownership. Topics: The 1-of-55 meetup story What AI psychosis means for developers Who owns AI-generated code? Why confident output is not the same as correct output no-slop.ai and the rule: don’t send AI output you haven’t read Mitchell Hashimoto on AI psychosis in companies Why arguing with AI usually wastes time How sycophantic models pull users into the spiral Practical ways to keep AI honest Why smaller or self-hosted models can make AI feel less magical Links: no-slop.ai: https://no-slop.ai Mitchell Hashimoto post: https://x.com/mitchellh/status/2055380239711457578 Timnit Gebru post on AI psychosis: https://www.linkedin.com/posts/timnit-gebru-7b3b407_surviving-ai-psychosis-activity-7454588079467593729-X1eT HBR trendslop article: https://hbr.org/2026/03/researchers-asked-llms-for-strategic-advice-they-got-trendslop-in-return Patreon: https://patreon.com/DomesticatingAIPodcast YouTube: https://www.youtube.com/@DomesticatingAI Apple Podcasts: https://podcasts.apple.com/us/podcast/domesticating-ai/id1873338950 Spotify: https://open.spotify.com/show/2WsAR4fvcXzp3vVZGVlkE2 Keep your AI on a leash.
S1 · E10
June 6 · 43 min
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: retries state management orchestration context control durable execution This week we're joined by Hamza Tahir, CTO and co-founder of ZenML and creator of Kitaru, to discuss what happens when agents stop being simple chat interfaces and start behaving like long-running distributed systems. We explore: what an agent harness actually is durable execution and why it matters orchestration vs business logic state management for long-running agents retries, checkpoints, and human-in-the-loop workflows context management and token costs open vs closed agent frameworks why everyone seems to be rebuilding the same layer of infrastructure One of the biggest questions we kept coming back to: What is a meta harness? If you have an answer, let us know in the comments. Kitaru https://github.com/zenml-io/kitaru ZenML https://www.zenml.io Hamza Tahir https://www.linkedin.com/in/hamzatahir/ Pedro Agentware https://github.com/Soypete/pedro-agentware OpenAI Agents SDK https://platform.openai.com/docs/guides/agents Temporal https://temporal.io DBOS https://www.dbos.dev Apache Airflow https://airflow.apache.org Prefect https://www.prefect.io Domesticating AI is a bi-weekly podcast about practical AI for developers. We help you brace the feral open-source AI landscape — so you can tame it instead of getting dragged by it. Subscribe on YouTube, follow on Spotify or Apple Podcasts, and support the show on Patreon. Keep your AI on a leash. Links
S1 · E7
May 22 · 38 min
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 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 infrastructure that doesn’t behave like traditional software. We cover: Why GPU scaling is fundamentally different from CPU scaling Why tools like vLLM are becoming the default for high-performance inference How Ray and Kubernetes fit into real-world AI systems What parallelism (tensor, data, expert) actually means in practice When self-hosting AI makes sense When APIs are still the better choice Claude Opus 4.7 https://www.anthropic.com/news/claude-opus-4-7 Qwen 3.6 (Alibaba) https://qwen.ai/research Kimi K2.6 (community discussion) https://www.reddit.com/r/LocalLLaMA/s/kvRWb7uJgM vLLM → https://github.com/vllm-project/vllm Ray → https://github.com/ray-project/ray Kubernetes → https://kubernetes.io Kueue → https://kueue.sigs.k8s.io LiteLLM → https://github.com/BerriAI/litellm KServe → https://kserve.github.io Daniel Dowler Platform engineer at Red Hat focused on Kubernetes and AI infrastructure. Daniel works on how modern systems support real workloads, including GPU scheduling, distributed inference, and scaling AI in production environments. He recently spoke at Machine Learning Utah on AI infrastructure and clustering. You don’t scale AI with replicas. You scale it by managing scarce compute. Subscribe on Spotify or Apple, and follow us on YouTube. 👉 Keep your AI on a leash. 🧠 News🔗 Tools & Tech Mentioned👤 Guest🎯 Key Takeaway
S1 · E8
May 8 · 43 min
You’re Using AI Wrong: Build the System, Not Just the Prompt /w Lexi Pasi
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 of Lucidity Sciences, to talk about what it actually means to own the system around AI: why calling an API is still user behavior what changes when you build the harness how agent systems actually fail (loops, cost, drift) why switching models isn’t a reliability strategy how to add layers—constraints, validation, and control flow why engineering discipline matters more with AI, not less If you’ve built your first AI agent, workflow, or coding loop—this is the “now what?” episode. Alexandra Pasi is the CEO of Lucidity Sciences, where she works at the intersection of mathematics, machine learning, and real-world system design. She holds a PhD in Mathematics from Baylor University and specializes in building analytical and algorithmic systems that bring structure to complex, uncertain environments. 🔗 LinkedIn: https://www.linkedin.com/in/alexandrapasi/ 🔗 Lucidity Sciences: https://luciditysciences.com Google TurboQuant (LLM compression research) https://research.google/blog/turboquant-redefining-ai-efficiency-with-extreme-compression/ Anthropic Claude Mythos Preview (security-focused model) https://red.anthropic.com/2026/mythos-preview/ Project Glasswing (Anthropic security initiative) https://www.anthropic.com/glasswing Karpathy Autoresearch (self-improving training loop) https://github.com/karpathy/autoresearch Kitaru (durable agent execution framework) https://github.com/zenml-io/kitaru Subscribe on YouTube Follow on Spotify & Apple Podcasts Support the show on Patreon: 👉 https://patreon.com/DomesticatingAIPodcast Keep your AI on a leash. 🧾 Episode Summary👤 Guest: Alexandra “Lexi” Pasi, PhD🔗 Topics & Links Mentioned🔔 Follow & Support
S1 · E7
April 24 · 43 min
Hacking AI: Why Most AI Systems Are Insecure by Default
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 building AI — especially at a startup — you are the security team. A widely used AI dependency was compromised via a malicious .pth file: executes automatically when Python starts no import required targets credentials, SSH keys, and environment variables 👉 Just installing the package was enough. This highlights a critical reality: Your AI system is only as secure as your dependencies. Models cannot distinguish between instructions and data External content can override system behavior Still one of the most common AI vulnerabilities 🔗 https://learnprompting.org/docs/prompt_hacking/injection Hidden instructions embedded in images AI interprets images differently than humans Expands the attack surface significantly 🔗 https://arxiv.org/abs/2306.11698 AI systems can take real-world actions via tools Prompt injection → API calls, data leaks, unintended execution Agents amplify risk through autonomy and retries If you’re building AI systems today: separate instructions from data limit tool permissions treat outputs as untrusted validate everything before execution AI systems have an internet-sized attack surface Supply chain attacks bypass all AI safeguards Prompt injection is a fundamental problem AI doesn’t fail safely — it fails wherever your system is weakest LiteLLM incident: https://github.com/BerriAI/litellm/issues/24512 Attack breakdown: https://futuresearch.ai/blog/litellm-pypi-supply-chain-attack/ LLM attack techniques: https://llm-attacks.org/ OWASP LLM Top 10: https://owasp.org/www-project-top-10-for-large-language-model-applications/ Gandalf challenge: https://gandalf.lakera.ai/ We’ve launched a Patreon for Domesticating AI 🎉 Get: early access to episodes behind-the-scenes content bloopers and uncut moments 👉 https://patreon.com/DomesticatingAIPodcast 🎥 YouTube: https://youtu.be/HTTxE7Y1sko What’s the weirdest way an AI system has broken for you? Keep your AI on a leash.
S1 · E6
April 10 · 39 min
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 engineering Reasoning models vs coding models How to plan and prompt AI effectively When to let AI take the wheel (and when not to) Local vs cloud coding agents Token costs vs owning hardware Tyler Folkman — The AI Architect Anthropic https://www.anthropic.com OpenAI https://openai.com Ollama https://ollama.com MiniMax-M2.5 https://ollama.com/library/minimax-m2.5 GLM-5 https://ollama.com/library/glm-5 AmpCode Chronicle https://ampcode.com/chronicle Andrej Karpathy on Context Engineering https://x.com/karpathy “Human in the Loop is Tired” (add link if you have it) Domesticating AI is a bi-weekly podcast about practical AI for developers. We help you brace the feral open-source AI landscape — so you can tame it instead of getting dragged by it. contact@domesticatingai.com Spotify https://open.spotify.com/show/2WsAR4fvcXzp3vVZGVlkE2 Apple Podcasts https://podcasts.apple.com/us/podcast/domesticating-ai/id1873338950 Are you vibe coding — or engineering with AI? Let us know your setup. Keep your AI on a leash. 🧠 What We Cover🔗 Links & ResourcesGuestModels & ToolsArticles / Mentions🎧 About the Podcast📬 Contact🔥 Follow👇 Join the Discussion
S1 · E5
March 27 · 30 min
Securing Your Homelab: AI Infrastructure, Access Control & Why Docker Isn’t Isolation
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 questions behind self-hosting AI: Why a dedicated machine isn’t a sandbox Why Docker alone isn’t isolation How homelabs evolve from Plex servers to AI infrastructure The blast radius problem with local agents Why networking and access control matter more than model size We also discuss the surge in local AI hardware demand and the risks of running powerful agents on machines with unrestricted access. Whether you're running OpenClaw, Ollama, a NAS, Postgres, or a home automation stack, the same rule applies: Infrastructure without containment is just risk waiting to happen. High-memory Mac Minis are seeing long shipping delays as developers rush to build local AI systems. https://www.tomshardware.com/tech-industry/artificial-intelligence/openclaw-fueled-ordering-frenzy-creates-apple-mac-shortage-delivery-for-high-unified-memory-units-now-ranges-from-6-days-to-6-weeks Marketplace plugins and execution boundaries are becoming a growing security concern in agent systems. https://www.linkedin.com/posts/matthewsharp_i-use-to-do-nothing-but-post-about-clean-activity-7432832983339999232-iR04 Overview of risks around agent plugin ecosystems and execution boundaries. https://conscia.com/blog/the-openclaw-security-crisis/ Private mesh networking used to securely access homelabs. https://tailscale.com Local AI coding agent framework. https://openclaw.ai Local LLM runtime used for running models on personal machines. https://ollama.com Why people actually build homelabs Plex, NAS, and home automation as infrastructure entry points AI workloads vs dev workloads Why long-running services shouldn’t live on your laptop Networking architecture for homelabs RBAC-style access control between machines Secrets management mistakes developers make Containment and blast-radius thinking for AI agents Tailscale and private mesh networking Each host answers: If I had $0 What I would run What I would avoid If I had $1K What machine I’d buy How I’d isolate workloads If I had $5K How I’d segment infrastructure What monitoring I’d deploy What I would never expose to the internet Staff Data Engineer, content creator, and founder of SoyPete Tech. Miriah focuses on practical AI systems, Go infrastructure, and self-hosted AI engineering. She is also a Google Developer Expert in Go and organizer of Go West Conf. https://soypete.tech AI engineer and co-author of LLMs in Production. Matt focuses on applied AI systems, local model infrastructure, and developer-focused AI tooling. Software engineer and AI practitioner focused on practical applications of machine learning and developer infrastructure. Domesticating AI is supported by the SoyPete Tech community. If you enjoy the show: Subscribe on YouTube Follow on Spotify Join the Discord community Share the episode with another engineer building with AI More content and tutorials: https://soypetech.substack.com 📰 News DiscussedMac Mini Shortages from Local AI DemandOpenClaw Security DiscussionOpenClaw Security Concerns (Referenced)🧰 Tools & Technologies MentionedTailscaleOpenClawOllama🏗 Topics Covered⚡ Lightning Round🎙 HostsMiriah PetersonMatt SharpChris Brousseau🤝 Sponsors
S1 · E4
March 13 · 34 min
Agents Don’t Need More Compute — They Need Better Engineering
📅 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/report/869004/moltbot-clawdbot-local-ai-agent Fake Moltbot VS Code extension spreading malware https://thehackernews.com/2026/01/fake-moltbot-ai-coding-assistant-on-vs.html Exposed Moltbot admin panels and credential leaks https://www.bitdefender.com/en-us/blog/hotforsecurity/moltbot-security-alert-exposed-clawdbot-control-panels-risk-credential-leaks-and-account-takeovers Cloudflare Moltworker (self-hosted agent on Workers) https://blog.cloudflare.com/moltworker-self-hosted-ai-agent/ LangGraph – https://www.langchain.com/langgraph LangChain – https://www.langchain.com/ Langfuse – https://langfuse.com/ Pydantic AI – https://github.com/pydantic/pydantic-ai Instructor – https://github.com/jxnl/instructor Hugging Face SmolAgents – https://huggingface.co/blog/smolagents Have a topic suggestion or want to sponsor the show? 📩 contact@domesticatingai.com
S1 · E1
February 27 · 33 min
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 leash. Links mentioned: - GPU Glossary (Modal): https://modal.com/gpu-glossary - CUDA → ROCm headline: https://wccftech.com/the-claude-code-has-managed-to-port-nvidia-cuda-backend-to-rocm-in-just-30-minutes/ - Unsloth PR: https://github.com/unslothai/unsloth/pull/3856
S1 · E1
February 13 · 43 min
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 turnkey UIs, and how homelab economics + networking decisions shape what you should run at home. In this episode Training vs inference (and why “inference server” gets misused) Unified memory: what it changes for model loading + workflows Ollama + Open WebUI as the fastest on-ramp for local AI Fine-tuning workflows (QLoRA/Unsloth-style) and where Spark shines Homelab reality: Docker “recipes,” troubleshooting, and collaboration Safer remote access: Tailscale Cloud vs home economics (when cloud is cheaper… and when it explodes) NVIDIA / DGX Spark DGX Spark: https://www.nvidia.com/en-us/products/workstations/dgx-spark/ Build hub / recipes: https://build.nvidia.com/spark NIM on Spark playbook: https://build.nvidia.com/spark/nim-llm Local AI runners + UIs Ollama: https://ollama.com/ Open WebUI (GitHub): https://github.com/open-webui/open-webui Open WebUI docs: https://docs.openwebui.com/ llama.cpp: https://github.com/ggml-org/llama.cpp LM Studio: https://lmstudio.ai/ vLLM: https://github.com/vllm-project/vllm Jan: https://jan.ai/ Fine-tuning + workflows Unsloth: https://github.com/unslothai/unsloth Image generation tools (mentioned) ComfyUI: https://github.com/Comfy-Org/ComfyUI AUTOMATIC1111 SD WebUI: https://github.com/AUTOMATIC1111/stable-diffusion-webui Networking / Remote access Tailscale: https://tailscale.com/ Cloud GPU alternatives (mentioned) Runpod pricing: https://www.runpod.io/pricing Modal pricing: https://modal.com/pricing Miriah Peterson (Host): Miriah Peterson is a software engineer, Go educator, and community builder focused on production-first AI—treating LLM systems like real software with real users. She runs SoyPete Tech (streams + writing + open-source projects) and stays active in the Utah dev community through meetups and events, with a practical focus on shipping local and cloud AI systems. Connect: SoyPete Tech (YouTube): https://www.youtube.com/@SoyPete_Tech SoyPete Tech (Substack): https://soypetetech.substack.com/ LinkedIn: https://www.linkedin.com/in/miriah-peterson-35649b5b/ Matt Sharp (Host): Matt Sharp is an AI Engineer and Strategist for a tech consulting firm and co-author of LLMs in Production. He’s a recovering data scientist and MLOps expert with 10+ years of experience operationalizing ML systems in production. Matt also teaches a graduate-level MLOps-in-production course at Utah State University as an adjunct professor. You can find him on Substack (Data Pioneer), LinkedIn, and on his other podcast, the Learning Curve. Connect: Data Pioneer (Substack): https://thedatapioneer.substack.com/ Chris Brousseau (Host): Chris Brousseau is a linguist by training and an NLP practitioner by trade, with a career spanning linguistically informed NLP, modern LLM systems, and MLOps practices. He’s co-author of LLMs in Production and is currently VP of AI at VEOX. You can find him as IMJONEZZ (two Z’s) on YouTube, GitHub, and on LinkedIn. Connect: YouTube (IMJONEZZ): https://www.youtube.com/channel/UCPtkaw_x97yP4WevW7axk0g LinkedIn: https://www.linkedin.com/in/chris-brousseau/en 📘 LLMs in Production (Matt Sharp & Chris Brousseau): https://www.manning.com/books/llms-in-production Links & ResourcesHosts
S1 · E1
January 30 · 41 min
Your First AI at Home
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 memory/RAM crunch), then break down what an inference engine actually does, how popular runtimes compare (llama.cpp, vLLM, Ollama, TGI), and a sane starter workflow for getting from “downloaded a model” to “usable local AI.” Inference engines are the “runtime”: model loading, tokenization, KV cache/context handling, and the serving layer. Pick your engine based on your goal: tinkering (llama.cpp) vs serving throughput (vLLM/TGI) vs it-just-works packaging (Ollama). You don’t need a brand-new rig to start, but RAM/VRAM constraints will shape everything. Use leaderboards as a hint, then validate with your own small eval prompts that match your workload. If you’re exposing anything beyond your LAN: reverse proxy + TLS + don’t casually open ports. 0:00 Intro + host chaos + what the show is 1:08 News: tinygrad / “AI server appliance” thinking (tinybox vibes) 2:44 News: RAM prices + the memory crunch for builders 8:26 Main: building your first AI at home (why now) 8:49 What is an inference engine? 12:30 Engines compared: llama.cpp vs vLLM vs Ollama vs TGI 15:42 Do you need to buy a new computer? (CPU vs GPU realities) 25:32 Models for home: fit-to-hardware, quantization, context 34:37 Leaderboards vs evals: picking models you can trust 44:00 Community + meetups + where to follow 45:22 Outro — “Keep your AI on a leash” News / context Tom’s Hardware: TinyBox production + multi-GPU appliance concept (Tom's Hardware) Reuters: AI-driven memory shortage / supply-chain crunch (Reuters) IDC: 2026 device impacts from the memory shortage (IDC) Inference engines llama.cpp (GGML org) (GitHub) vLLM OpenAI-compatible server (docs.vllm.ai) Ollama docs (quickstart) (Ollama Documentation) Hugging Face Text Generation Inference (TGI) (GitHub) Miriah Peterson: Software engineer, Go educator, and community builder focused on production-first AI. Runs SoyPete Tech (streams + writing + open-source). Matt Sharp: AI Engineer/Strategist, co-author of LLMs in Production, MLOps practitioner. Writes The Data Pioneer. (thedatapioneer.substack.com) Chris Brousseau: NLP practitioner, co-author of LLMs in Production, VP of AI at VEOX. You can find him as IMJONEZZ. (veox.ai) SoyPete Tech (YouTube): (youtube.com) SoyPete Tech (Substack): (soypetetech.substack.com) Matt’s Substack (The Data Pioneer): (thedatapioneer.substack.com) Chris on YouTube (IMJONEZZ): (youtube.com) LLMs in Production (book): (Manning Publications)

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