True Fit

→ The prompt to build this yourself: https://skool.com/ai-incubator-pro/ → Work with me on something bespoke: https://itqaansolutions.co/apply I've tried to build a Jarvis assistant more times than I'd like to admit. Every version ended up the same way: technically cool, obviously unfinished. So I gave GPT-6 Astra a much harder brief — not a chatbot with a microphone, but a real local assistant that talks naturally, reaches into my actual data, works out which tools it needs, and answers through an interface I'd actually want to open. One prompt. I genuinely did not expect what came back. In this video I walk through how it works, how you'd build one yourself, and — the part I care about most — how you extend it to fit your own workflow. Stick around to the end. The last test is the one that convinced me. Chapters 0:00 — The prompt I almost didn't bother sending 0:50 — "Hey Jarvis, prep me for my next meeting" 1:16 — Why this one didn't collapse like the others 1:46 — Talking to it, and getting talked back to 3:12 — The brain that was already on my machine 4:41 — Watching it pick its own tools 6:13 — The moment it stopped feeling like a demo 7:19 — Giving it a brand new ability, live 8:52 — None of this is new. So what changed? 9:49 — The test I ran to make sure it wasn't lying

I run this episode solo. I explain local AI in plain terms: the model runs on hardware I control, and a cloud model runs somewhere else. I map the four pieces of the local AI landscape — the model, the warehouse, the software, and the workflow — and I define the words that beginners meet first: parameters, tokens, context window, quantization, and GGUF. I walk through the Google open model stack (Gemma 4, Google AI Edge, LiteRT-LM, AI Edge Gallery), compare the other open model families, and show three ways to run a model today. I close with a first workflow you can copy and three startup ideas that use local AI as the wedge. And a special thank you to Google for supporting the podcast. Get the full guide to running local AI: https://startup-ideas-pod.link/local-ai Timestamps 00:00 – Intro 01:35 – The Open Model the Landscape 03:09 – Vocab Decoder 06:48 – Google Gemma Clearly Explained 10:29 – Other Open Model Families 14:20 – Path 1: Run Gemma in LM Studio 18:17 – Path 2: Ollama 20:15 – Path 3: Google AI Edge 21:07 – Hardware Cheat Sheet 21:52 – First Workflow to Build 22:47 – Workflows Before Fine-Tuning 25:06 – Local vs Cloud vs Hybrid Eval 26:33 – Framework for Local AI Startup Ideas 27:22 – Startup Idea 1: Home Health QA Reviewer 29:24 – Startup Idea 2: Offline Field Report Copilot 32:10 – Startup Idea 3: Pre-Send Reviewer for Professional Services 34:47 – Build Your Local AI Lab 37:55 – Closing Thoughts Key Points • Ask whether the model is good enough for the job, and the business opportunities become clear. • Local AI has four pieces: the model, the warehouse (Hugging Face), the software (LM Studio or Ollama), and the workflow you build around them. • Gemma 4 E4B is my practical starting point; E2B fits phones and older machines. • Hybrid architecture wins: local does the private first pass, cloud does the heavy reasoning, and a human approves anything important. • Start with one repeated workflow — one folder, one model, one output — and run it 10 times. • I see a 24-month window to build local-AI-native software for verticals that still run early-2000s tools. Numbered Section Summaries 1. The Four Pieces of the Local AI Landscape I break the space into the model (the brain file, such as Gemma, Llama, or Mistral), the warehouse (Hugging Face), the software that runs the model (LM Studio or Ollama), and the workflow (the product around all of it). Underneath the tools sit Llama.cpp and MLX, and for shipping on-device apps in the Google ecosystem you reach Google AI Edge and LiteRT-LM. On Hugging Face I read a model card slowly and look for six things: purpose, size, license, hardware, supported inputs, and quantized files. 2. The Vocabulary That Matters I define parameters as the internal weights, where more parameters give more capacity and cost more memory: 2B and 4B for edge devices and fast workflows, 12B as a middle ground, and 26B or 31B for workstation territory. I define tokens, the context window, quantization (Q4 for easier running, Q8 for more quality), and GGUF as the common local file format. I suggest you start on a phone or a spare 2021 laptop and keep your money for later. 3. The Google Open Model Stack Gemma is Google's open model family, and Gemma 4 targets efficient local and on-device use across E2B, E4B, 12B, and 26B/31B. The specialized models deserve attention: EmbeddingGemma for search by meaning, FunctionGemma for tool use and structured function calling, PaliGemma for vision, ShieldGemma for safety, and Gemma Scope for interpretability. Around the models sit Google AI Edge, LiteRT-LM, AI Edge Gallery, and Gemini plus Google Cloud for frontier-level reasoning. 4. Three Ways to Run Gemma Today Path one is LM Studio: download the app, search for Gemma 4, pick E4B or E2B, grab the quantized GGUF, and paste real customer notes into a chat to feel the value. Then start the LM Studio local server so your scripts and prototypes call the model through localhost. Path two is Ollama with a local API on port 11434, and path three is Google AI Edge with LiteRT-LM for Android, iOS, web, desktop, and edge apps. I also give a RAM cheat sheet: 8 GB stays small, 16 GB runs useful experiments, 32 GB opens larger workflows, and a strong GPU makes the bigger models realistic. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/