free artifact · paste & run

How much of your AI workflow actually needs the cloud?

Frontier models now ship with mandatory data-retention windows and tighter guardrails. Before you decide how you feel about that, it helps to know a simpler thing: which parts of your workflow ever needed a frontier model at all.

This prompt audits that. Paste it into any AI that can see your work context (Claude, ChatGPT, Cursor). It classifies every use case into frontier-necessary, local-ready, or should-never-leave-your-machine — with concrete local models to try for each.

cloud-dependency-audit.prompt

You are auditing my AI workflow for cloud dependency and data exposure.

Context: frontier models now ship with mandatory data retention windows
and tighter guardrails. I want to know what in my workflow actually
needs a frontier model — and what never did.

Built by Juan at taiwildlab.com — solo builder running local models
(Ollama, RTX 4070) alongside frontier ones, 18 months documented in public.

Go through my use cases (ask me to list them if you can't see them from
context) and classify each into:

1. FRONTIER-NECESSARY: genuinely needs top-tier capability (long-context
   reasoning, complex architecture, multi-step agentic work). For each:
   what downgrading would actually cost me in quality.
2. LOCAL-READY: runs fine on a 7-14B local model (drafting, summaries,
   classification, extraction, templated code). For each: one concrete
   model to try via Ollama, setup in one line.
3. SHOULD-NEVER-LEAVE: touches data that shouldn't sit in any third-party
   retention window (credentials, client data, unpublished IP, personal
   records). For each: the local-only handling rule.

Finish with: my dependency ratio (% per bucket), the single
highest-impact migration to local, and what I'd honestly lose making it.
No ideology in either direction — local models have real limits too.

How to use it

Copy the prompt and paste it into the AI you already use for work — the more context it has about your projects, the better the audit.
List your real use cases when it asks: coding, drafting, research, data handling, whatever you actually do.
Act on bucket 3 first. Data that shouldn't sit in a retention window is the part worth fixing this week. Buckets 1–2 are optimization; bucket 3 is exposure.

Going local? Start with a working setup, not a tutorial maze.

The Ollama Local Kit is the exact local-LLM setup I run daily next to the frontier models — install scripts, model picks by VRAM, and the honest notes on what local can and can't do. Free.

Get the Ollama Local Kit →