Local install
AI Optimizer is installed and opened locally instead of being treated like a remote SaaS middle layer.
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Watch demo
AI Optimizer Install and Cache-Hit Proof Demo
Watch AI Optimizer install locally, connect to an OpenAI-compatible workflow, and prove that an exact repeated request can be served from cache instead of billed upstream again.
Watch the full demo
Install it, route traffic through localhost, and verify the hit.
This video shows the full install flow, localhost routing, and visible cache-hit proof inside AI Optimizer.
Demo flow: install AI Optimizer, route a local OpenAI-compatible workflow through localhost, run the request again, and confirm the exact cache hit in the popup and local app stats.
Quick answer
This video shows AI Optimizer being installed, configured for a local OpenAI-compatible workflow, and then proving that an exact repeated request can be served from cache instead of hitting the upstream API at full cost again.
Current proof staging
The honest staged proof pair right now is the browser popup plus the in-app dashboard stats view. A true terminal curl /stats still is optional stricter support, not something this page should imply is already captured.
That keeps the watch page aligned with the actual screenshots already prepared instead of overstating the proof bundle.
What you’ll see in this demo
A quick proof-focused walkthrough of the core local caching story.
Local proxy routing
The workflow is pointed at localhost so AI traffic passes through AI Optimizer before reaching the upstream provider.
Repeated request test
The same request is run again to test whether repeat behavior can be served locally from cache.
Visible proof
The cache hit is shown in the browser popup and local app stats so the result is inspectable instead of just claimed.
Walkthrough
If you want the short version without watching the full video first, this is the basic flow shown on screen.
1
Install AI Optimizer locally
The demo begins with the local app installation and startup.
2
Point the workflow at localhost
The request path is changed so the workflow uses AI Optimizer locally instead of calling the provider directly.
3
Configure provider + cache behavior
The OpenAI-compatible setup and local caching layer are shown inside the app.
4
Run the request once
The first request goes through the normal upstream path.
5
Run the same request again
The repeated request is run a second time to test whether it can be served locally from cache.
6
Verify the hit in the popup and local stats
The staged proof today is visible in the popup and local app stats. If we later want the stricter matched pair, add a true terminal curl /stats still as companion proof.
Why this matters: a lot of AI cost claims stay abstract. This demo matters because it shows the practical case clearly — install the app, point traffic through localhost, repeat the request, and verify the hit.
Related reading
The watch page should support the main explanatory pages, not replace them.
Reduce repeated AI API waste locally.
If your scripts, agents, or repeated local workflows keep sending the same request patterns over time, AI Optimizer gives you a local-first way to verify repeat savings instead of guessing.