All demosHot Dog / Not Hot Dog

Hot Dog / Not Hot Dog

Hot dog. Not hot dog. You know the drill.

Examples are free. Bring a key for your own inputs. Get a free key

Performance & cost

This session

Ready to run
Delivery time · median
Live model compute
Live input tokens
0
Est. cost · USD
$0.00

Free examples. Switch on to measure live inference with your key and quota.

Measurement details · 0 successful requests
Successful requests
0

Run an example for free to see its measurements.

Model compute is the average per measured request. Delivery time is the median browser time for successful requests, including queue, network, and retries. Cached delivery speed is not inference speed. Model compute can exceed browser time when one request processes many inputs.

Tokens come from response headers. Inference costs use $0.04 per million input tokens, before plan credits. The estimate includes live and recorded tokens at the same rate; cached examples are not charged again. Missing usage is unavailable or a partial total (≥). Demo resets keep these totals; reload to start a new session.

Try it

Classify the grid, then try your own photo.

Build with the SDKTypeScript · Python · dm1

One request from this demo. Copy it into your app.

Uses your MS_API_KEY and quota. Keep the key on your server.

Install · set your key
npm install @cloudraker/milliseconds
export MS_API_KEY="your-api-key"
example.ts
import { readFile } from "node:fs/promises";
import { DecisionMachine } from "@cloudraker/milliseconds";

// Run on your server; reads MS_API_KEY from the environment.
// The image carries the input; the leading text is optional context.
const dm = new DecisionMachine();

const { result, usage } = await dm.classify(
  await readFile("image.jpg"),
  {
    "hot dog": "a cooked frankfurter or wiener served in a sliced bun, with or without toppings",
    "not hot dog": "anything else, including burgers, sausages without a bun, corn dogs, an empty bun, other food and animals"
  },
  { detail: "low" }
).withUsage();

console.log(result);
console.log({ inputTokens: usage.inputTokens, modelMs: usage.inferenceMs });

Under the hood How it works & limitations

API calls/classify

The smallest useful image job

Silicon Valley's SeeFood app answered one question about a photo. That question is a real API call: one classify over one image, two described labels, a probability for each. Nothing is parsed, described or summarised first.

Pick the tier the job needs

Detail selects the longest edge the model reads: 512 px at low, 768 px at medium, 1024 px at high. Telling a hot dog from a banana needs none of the extra pixels, so this demo stays at low and bills 1,000 image tokens per photo. Reading small print off a receipt is the job that needs a higher tier.

A verdict is still a reading

A probability near 0.5 means the model is undecided, not that the answer is half true. Two of the stock subjects, a corn dog and a bratwurst in a roll, divide people as well. Route the undecided cases to a person instead of forcing a label.

This demo sends one photo of at most 5 MB as JPEG, PNG or WebP. It does not accept image URLs or batches of images. Photos are processed in memory, never written to disk and never logged.