Puter.js Decision Room: test sign-in, AI usage, history and export

Define a personal tool before assuming a shared backend

Thanks to Puter for sponsoring this video.

The demonstration builds a multi-model Decision Room with Codex. Puter.js supplies authentication, AI, storage, database and hosting through its library; the user signs into their own Puter account and uses their allowance.

1. Describe the decision input, models and report you want to compare.
2. Keep personal case history separate from any requirement for shared team records.
3. Ask for the Puter sign-in and persistence route shown in the recording, rather than inventing separate provider-key setup.

Test the complete decision flow

The recording checks sign-in, model selection and a multi-model response. Those are separate checks from whether the result is useful.

1. Sign in with a test account and submit a small case.
2. Select the intended models and inspect their responses against the input.
3. Review usage in the Puter desktop so the user-pays behaviour is visible.
4. Save the case and verify it remains after reopening the app.

Publish and locate the exported file

The later steps cover persistent history, cloud-file export and publishing from the Puter desktop.

1. Export a report and locate it in the intended cloud-file location.
2. Publish a test version and repeat sign-in, response, history and export checks on the live URL.
3. Test a second account if the app will serve more than one person.
4. Check the actual shared-data requirements and cost model before treating this personal-tool pattern as a complete multi-user service.

Adapt this brief

Build a small Decision Room for [decision]. Use the demonstrated Puter account flow. Let the signed-in user select the intended models, save case history and export a report. Show where the user’s allowance is used. Test persistence and a second account before claiming shared use works.

The takeaway

The demonstrated convenience comes with a specific account and usage model. Your own users’ allowances and shared-data needs still need evaluation.