Vending Machine Lab

At a glance

Vending Machine Lab is worth trying when you want to examine what a supposedly hands-off business still depends on. Choose a business type, set its assumptions and rehearse fourteen fictional days while the owner is unavailable. Its useful feature is the comparison: change one setting and see which failures remain. It makes a dependency discussion concrete, without establishing whether a real business would succeed.

Good fit

People working through fulfillment, support and recovery responsibilities before automating them. In the built-in snack-machine trial, adding a physical service plan raised completions from 25 to 94 out of 112 customer attempts. Owner requests also rose from seven to fourteen. The comparison separates delivering more orders from operating independently, rather than treating one improved metric as success. The decision log and waiting-order list let you inspect what still blocks the plan.

Consider something else when

You need a demand forecast, a measure of an AI model’s ability or a service that runs a real business. Customer arrivals and failures are supplied by the scenario. The instant controller follows rules, and its modeled AI and human help use preset outcomes. Actual assistant decisions can be brought in separately, but no assistant-supplied order decisions were tried. Changing a price or adding staff does not create buyers.

Start here

Choose Workplace snack machines and run the first fourteen days. Inspect owner requests and unfinished promises before the money figure. Then choose Test: Arrange human restocking and repairs and compare it with Starting design. The tested comparison kept the same arrivals and failures and changed the physical service plan. The first run was invoked through the browser-agent tool; the repair used the visible button. Both saved runs remained after one reload.

Cost and requirements

The tested hosted browser flow required no account, payment or AI API key. Displayed dollars are fictional inputs and outputs, not charges. Drafts and runs are saved in this browser; the explanation recommends exporting a workspace before changing devices or clearing storage. Using an external assistant for the optional decision-packet workflow depends on that assistant’s access and cost. No package, app or model was installed.

Limits to know

The vending model has one stock pool and no separate product inventory, spoilage or delivery lead times. Its sales-minus-costs result deducts inventory purchases, including unsold stock, and excludes setup and owner time; it is not accounting profit. Only one ordinary snack-machine scenario and its physical-service repair were tried. The selected source confirms local simulation and browser-tool registration, not a validated business model or an audit of every data path. Export/import, the real CSV demonstration and external-agent decisions were not tested.

Still unclear

• Workspace export and restoration on another device were not exercised.
• Other business types and imported assistant decisions were not tried.