Podcast
Domesticating AI
Hosted by SoyPete Tech · EN
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
10episodes
Episodes
Newest firstAll episodes
CrankGPT: The Hand-Powered AI That Exposes the Cost of Inference
1w ago00:46:43Tap to summarizeWhat 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_agentPinokio:https://pinokio.co/whisper.cpp:https://github.com/ggml-org/whisper.cppOpenAI Whisper:https://github.com/openai/whisperllama.cpp:https://github.com/ggml-org/llama.cppLiquid 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
Transcribe →The Skills Every AI Engineer Needs in 2026
Jul 300:37:23Tap to summarizeEveryone 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 everIs RAG still relevant in 2026?The difference between AI users and AI engineersWhy engineering fundamentals still matterCommunication as a technical skillWhy experimentation is part of the jobDo you need to fine-tune or self-host models?How Gauntlet AI prepares engineers for AI careersAdvice for learning AI without chasing every new frameworkGauntlet AI — https://gauntletai.comLLMs in Production — https://www.manning.com/books/llms-in-productionClaude Code — https://www.anthropic.com/claude-codeRay — https://www.ray.io/Qwen — https://qwen.ai/blog?id=qwen3.5UV 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.
Transcribe →Trust AI? Stop Shipping Output You Didn’t Read
Jun 1900:36:20Tap to summarizeAt 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 storyWhat AI psychosis means for developersWho owns AI-generated code?Why confident output is not the same as correct outputno-slop.ai and the rule: don’t send AI output you haven’t readMitchell Hashimoto on AI psychosis in companiesWhy arguing with AI usually wastes timeHow sycophantic models pull users into the spiralPractical ways to keep AI honestWhy smaller or self-hosted models can make AI feel less magicalLinks:no-slop.ai: https://no-slop.aiMitchell Hashimoto post: https://x.com/mitchellh/status/2055380239711457578Timnit Gebru post on AI psychosis: https://www.linkedin.com/posts/timnit-gebru-7b3b407_surviving-ai-psychosis-activity-7454588079467593729-X1eTHBR trendslop article: https://hbr.org/2026/03/researchers-asked-llms-for-strategic-advice-they-got-trendslop-in-returnPatreon: https://patreon.com/DomesticatingAIPodcastYouTube: https://www.youtube.com/@DomesticatingAIApple Podcasts: https://podcasts.apple.com/us/podcast/domesticating-ai/id1873338950Spotify: https://open.spotify.com/show/2WsAR4fvcXzp3vVZGVlkE2Keep your AI on a leash.
Transcribe →Stop Building AI Agents: Build Harnesses Instead | Hamza Tahir (ZenML / Kitaru)
Jun 600:43:38Tap to summarizeEveryone 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 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 isdurable execution and why it mattersorchestration vs business logicstate management for long-running agentsretries, checkpoints, and human-in-the-loop workflowscontext management and token costsopen vs closed agent frameworkswhy everyone seems to be rebuilding the same layer of infrastructureOne 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.Kitaruhttps://github.com/zenml-io/kitaruZenMLhttps://www.zenml.ioHamza Tahirhttps://www.linkedin.com/in/hamzatahir/Pedro Agentwarehttps://github.com/Soypete/pedro-agentwareOpenAI Agents SDKhttps://platform.openai.com/docs/guides/agentsTemporalhttps://temporal.ioDBOShttps://www.dbos.devApache Airflowhttps://airflow.apache.orgPrefecthttps://www.prefect.ioDomesticating 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
Transcribe →Self-Hosting AI: Scaling Is the Real Problem
May 2200:38:09Tap to summarizeAI 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 infrastructure that doesn’t behave like traditional software.We cover:Why GPU scaling is fundamentally different from CPU scalingWhy tools like vLLM are becoming the default for high-performance inferenceHow Ray and Kubernetes fit into real-world AI systemsWhat parallelism (tensor, data, expert) actually means in practiceWhen self-hosting AI makes senseWhen APIs are still the better choiceClaude Opus 4.7https://www.anthropic.com/news/claude-opus-4-7Qwen 3.6 (Alibaba)https://qwen.ai/researchKimi K2.6 (community discussion)https://www.reddit.com/r/LocalLLaMA/s/kvRWb7uJgMvLLM → https://github.com/vllm-project/vllmRay → https://github.com/ray-project/rayKubernetes → https://kubernetes.ioKueue → https://kueue.sigs.k8s.ioLiteLLM → https://github.com/BerriAI/litellmKServe → https://kserve.github.ioDaniel DowlerPlatform 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
Transcribe →You’re Using AI Wrong: Build the System, Not Just the Prompt /w Lexi Pasi
May 800:43:25Tap to summarizeRecorded: 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 Lucidity Sciences, to talk about what it actually means to own the system around AI:why calling an API is still user behaviorwhat changes when you build the harnesshow agent systems actually fail (loops, cost, drift)why switching models isn’t a reliability strategyhow to add layers—constraints, validation, and control flowwhy engineering discipline matters more with AI, not lessIf 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.comGoogle 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/glasswingKarpathy Autoresearch (self-improving training loop)https://github.com/karpathy/autoresearchKitaru (durable agent execution framework)https://github.com/zenml-io/kitaruSubscribe on YouTubeFollow on Spotify & Apple PodcastsSupport the show on Patreon:👉 https://patreon.com/DomesticatingAIPodcastKeep your AI on a leash.🧾 Episode Summary👤 Guest: Alexandra “Lexi” Pasi, PhD🔗 Topics & Links Mentioned🔔 Follow & Support
Transcribe →Hacking AI: Why Most AI Systems Are Insecure by Default
Apr 2400:43:09Tap to summarizeHosts: 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 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 startsno import requiredtargets 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 dataExternal content can override system behaviorStill one of the most common AI vulnerabilities🔗 https://learnprompting.org/docs/prompt_hacking/injectionHidden instructions embedded in imagesAI interprets images differently than humansExpands the attack surface significantly🔗 https://arxiv.org/abs/2306.11698AI systems can take real-world actions via toolsPrompt injection → API calls, data leaks, unintended executionAgents amplify risk through autonomy and retriesIf you’re building AI systems today:separate instructions from datalimit tool permissionstreat outputs as untrustedvalidate everything before executionAI systems have an internet-sized attack surfaceSupply chain attacks bypass all AI safeguardsPrompt injection is a fundamental problemAI doesn’t fail safely — it fails wherever your system is weakestLiteLLM incident: https://github.com/BerriAI/litellm/issues/24512Attack 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 episodesbehind-the-scenes contentbloopers and uncut moments👉 https://patreon.com/DomesticatingAIPodcast🎥 YouTube: https://youtu.be/HTTxE7Y1skoWhat’s the weirdest way an AI system has broken for you?Keep your AI on a leash.
Transcribe →Coding with AI: Vibe Coding vs Real Engineering (with Tyler Folkman)
Apr 1000:39:55Tap to summarizeAI 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 wheel (and when not to)Local vs cloud coding agentsToken costs vs owning hardwareTyler Folkman — The AI ArchitectAnthropichttps://www.anthropic.comOpenAIhttps://openai.comOllamahttps://ollama.comMiniMax-M2.5https://ollama.com/library/minimax-m2.5GLM-5https://ollama.com/library/glm-5AmpCode Chroniclehttps://ampcode.com/chronicleAndrej Karpathy on Context Engineeringhttps://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.comSpotifyhttps://open.spotify.com/show/2WsAR4fvcXzp3vVZGVlkE2Apple Podcastshttps://podcasts.apple.com/us/podcast/domesticating-ai/id1873338950Are 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
Transcribe →Securing Your Homelab: AI Infrastructure, Access Control & Why Docker Isn’t Isolation
Mar 2700:30:18Tap to summarizeRecording 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 behind self-hosting AI:Why a dedicated machine isn’t a sandboxWhy Docker alone isn’t isolationHow homelabs evolve from Plex servers to AI infrastructureThe blast radius problem with local agentsWhy networking and access control matter more than model sizeWe 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-weeksMarketplace 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-iR04Overview 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.comLocal AI coding agent framework.https://openclaw.aiLocal LLM runtime used for running models on personal machines.https://ollama.comWhy people actually build homelabsPlex, NAS, and home automation as infrastructure entry pointsAI workloads vs dev workloadsWhy long-running services shouldn’t live on your laptopNetworking architecture for homelabsRBAC-style access control between machinesSecrets management mistakes developers makeContainment and blast-radius thinking for AI agentsTailscale and private mesh networkingEach host answers:If I had $0What I would runWhat I would avoidIf I had $1KWhat machine I’d buyHow I’d isolate workloadsIf I had $5KHow I’d segment infrastructureWhat monitoring I’d deployWhat I would never expose to the internetStaff 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.techAI 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 YouTubeFollow on SpotifyJoin the Discord communityShare the episode with another engineer building with AIMore 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
Transcribe →Agents Don’t Need More Compute — They Need Better Engineering
Mar 1300:34:45Tap to summarize📅 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/report/869004/moltbot-clawdbot-local-ai-agentFake Moltbot VS Code extension spreading malwarehttps://thehackernews.com/2026/01/fake-moltbot-ai-coding-assistant-on-vs.htmlExposed Moltbot admin panels and credential leakshttps://www.bitdefender.com/en-us/blog/hotforsecurity/moltbot-security-alert-exposed-clawdbot-control-panels-risk-credential-leaks-and-account-takeoversCloudflare Moltworker (self-hosted agent on Workers)https://blog.cloudflare.com/moltworker-self-hosted-ai-agent/LangGraph – https://www.langchain.com/langgraphLangChain – https://www.langchain.com/Langfuse – https://langfuse.com/Pydantic AI – https://github.com/pydantic/pydantic-aiInstructor – https://github.com/jxnl/instructorHugging Face SmolAgents – https://huggingface.co/blog/smolagentsHave a topic suggestion or want to sponsor the show?📩 contact@domesticatingai.com
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