Ollama
First met in aicp-c3-basecamp-02 · Models & model access · Models & model access
OllamaFirst met in aicp-c3-basecamp-02 · Models & model access · Models & model access
An app store for local models — pull an open-source LLM and run it entirely on your own machine, free and offline.
Privacy, zero marginal cost, or uncensored models matter more than raw capability — and you have the RAM.
One command to pull and run a model (`ollama run <model>`), which is the whole install experience
Plugs straight into n8n as a chat-model node via a local URL on the standard port
Uncensored community models that no hosted API will serve you
Downloads and runs open-source models locally (Dolphin, Llama, Gemma, Qwen and the rest)
Serves them on a local endpoint that other tools can call
Completes the fully-local automation stack: n8n + Firecrawl + Ollama, no API bills
Doesn't run well on modest hardware — a 7B model was sluggish on an 18GB MacBook, and Intel laptops were steered to a rented VM
Doesn't remove all costs — you still need a paid coding agent to build against it
Doesn't match frontier models; the file-deletion horror stories in BC5 came from open models driving tooling
The bottom of the model shelf: cheap, private, always-on inference for high-volume or sensitive workflow steps.
Models & model access · Models & model access
No local-inference need yet, but the fully-local stack is the obvious fallback if API costs ever bite.
Plausible for bulk, low-stakes passes over the corpus — first-pass transcript condensing or tagging — where quality matters less than volume and Fable budget matters a lot.
current
openrouter: OpenRouter is the hosted answer to the same 'which model' question
n8n: Ollama chat-model node with a local-URL credential
runpod: rent a ~$10/mo VM when local hardware won't do
| Session | Coverage | Moment | What happened |
|---|---|---|---|
| Basecamp 3: Introduction to n8n — I | demonstrated | 2:15:55 | Installed live, pulled the Dolphin uncensored model in the terminal (`ollama run`), explained as app store for local models; n8n Ollama chat-model node credential = local URL, standard port, no API key |
| AI Sprint: The Hermes Agent — Day 1 (Always-On Agents, the Employee-on-Day-One System, and the Self-Improving Ghostwriter) | demonstrated | Hooked to Hermes via Codex; 16GB RAM sizing honesty; 'my system will die if I use local models' — teaching demo only | |
| Session 22: Automate Admin Tasks — Advanced RAG (Enterprise 'Internal Perplexity' Built Live) | explained | First-class production provider in the spec; not installed on his Mac that day, so smoke-testing deferred | |
| AI Sprint: OpenClaw — Session 2 (Token Anatomy, Claude Code Inside the Server, Skills, and Multi-Agent Workspaces) | explained | The Slido answer for local models: install into the server via Claude Code, then pull Qwen/Kimi/DeepSeek | |
| Basecamp 2: Vibe Coding | mentioned | 2:31:44 | Q&A: local open-source inference; RAM-hungry, trainer found 7B slow on an 18GB MacBook; building against it still needs a paid coding agent |
| Basecamp 4: Introduction to n8n — II | mentioned | 2:54:18 | Third leg of the fully-local stack (n8n + Firecrawl + Ollama) |
| Basecamp 5: MCP | mentioned | 1:16:21 | Cited as a source of file-deletion horror stories when open-source models drive Claude-style tooling |
| Session 3: Building Elegant Landing Pages | mentioned | 2:24:41 | His 7B-on-18GB experiment was 'terrible' — 24GB+ or stay on the $20 cloud plan |
| Session 4: How to Build Micro Prototypes | mentioned | 0:38:37 | The private-data lane: self-hosted models on a VPS when data can't touch cloud tools |
| Session 9: Introduction to Cursor & its principles | mentioned | 1:17:52 | Possible harness host only with serious local hardware — 'it will guzzle system power' |
| Session 12: Claude Code — Design & Building Agents (App-First: a Game, a GTM Board, and a Live Deploy) | mentioned | 1:52:54 | Learner question; possible but 'not that smart a model' for automations — not recommended |
| The Five-Level AI Generalist Roadmap (+ Operating System & Monetization) | mentioned | Local experimentation option (with LM Studio) | |
| Learning Topics: Sessions 1–5 — Foundations to AI Director | mentioned | The local-model privacy path with its hardware/maintenance trade-offs — 'local does not automatically mean compliant' | |
| AI Sprint: The Hermes Agent — Day 2 (Loop Engineering: the Scheduling Loop, the Delegation Loop, and the Skill Auditor) | mentioned | Local-model lane acknowledged: 'as long as your machine supports it' | |
| AI Sprint: The Open Source Playbook — Day 2 (Weights 101, OpenCode, and the Subsidy Math) | mentioned | Local-model route attachable to OpenCode for capable hardware |