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App Prototypes 2026-06-02

Tariff Radar

Tariff Radar
Prototype

TariffRadar

Know which of your planned purchases are about to get hit by tariffs — and exactly when to buy each one.

Date: 2026-06-02 Form factor: Web app (single-page; mobile-friendly) Status: Prototype

What it is

TariffRadar is a personal "buy-before-the-spike" radar for the 2026 tariff era. You list the things you're planning to buy — a laptop, a washer/dryer, a sofa, winter tires — and TariffRadar scores each one for tariff exposure: its country of origin, its current import-tariff rate, any scheduled rate change and its date, and how much of that tariff is still working its way onto the shelf price. It then hands back a plain verdict per item — Buy now, Watch, or No rush — plus a projected price after the spike and how much you'd save by acting before it lands.

The prototype runs a real household's 12-item wishlist through an in-browser pricing engine you can re-tune live: drag the budget, buy-now horizon, pass-through, and act-threshold sliders, filter by category, and watch every verdict and dollar figure recompute instantly.

Who it serves

Everyday shoppers with a few big-ticket purchases on deck during a period of rising, tariff-driven prices. In 2026, U.S. inflation is forecast to climb as businesses pass tariff costs through to consumers, and durable goods — electronics, appliances, furniture, tools, toys — are squarely in the blast radius. The specific pain: people know prices are rising and that some items are about to jump, but they have no way to tell which of their own planned purchases are exposed, by how much, or when — so they either panic-stockpile everything or get caught flat-footed when the price steps up. Concrete personas:

  • The big-purchase planner deciding whether to buy the laptop and OLED TV now or wait — and wanting the dollar difference, not vibes.
  • The home-project household mid-kitchen-remodel, where cabinets already jumped to a 50% tariff and the pass-through tail is still climbing.
  • The deal-timer who'll happily wait on low-impact items (US-made patio set, modest-ticket shoes) but wants a nudge the moment a meaningful spike is scheduled.

Why it could be profitable

Monetization is freemium consumer SaaS with affiliate and B2B layers:

  • Free: Track up to 5 items, see verdicts and projected increases.
  • Pro ($5/mo): Unlimited items, spike + price-drop alerts (email/push) as scheduled rate-change dates approach, retailer price comparison, and a "best month to buy" calendar.
  • Affiliate revenue: Every item has a "Shop [retailer] →" link; TariffRadar earns commission on purchases its urgency nudges drive (Amazon, Best Buy, Home Depot, Wayfair affiliate programs). This monetizes the free tier without a paywall.
  • B2B urgency widget: License the "this price is scheduled to rise — buy before [date]" engine to retailers as an honest, data-backed conversion widget on product pages, billed per impression or rev-share.

The timing is the thesis. Consumer-imported prices were already up 2.3% by mid-February 2026, Fed researchers reported a "full pass-through" of tariff costs to consumers by May 2026, and the average U.S. household faces a ~$1,500 tariff-driven cost increase in 2026. Roughly 40% of consumers said they'd stockpile ahead of hikes, and shoppers pulled forward **autos (12%), furniture (10%), and large electronics (9%)** specifically because of tariffs (GlobalData). The behavior is here and growing — what's missing is a tool that turns "prices are going up" into "this item, this much, by this date — buy it now." TariffRadar is that tool.

Form factor & scope

Single-page web app, sized for mobile and desktop. Scope-locked to the decision layer — TariffRadar does not transact, hold payment, or scrape live retailer prices; it is the radar and the verdict that sit in front of your shopping. The minimum viable scope demonstrated here:

  1. See your wishlist scored, each item tagged Buy now / Watch / No rush with the rule that fired.
  2. Re-tune the model — budget, buy-now horizon, tariff pass-through, and the dollar threshold that earns a "Buy now."
  3. Filter by category and re-sort by urgency, projected increase, soonest rate change, or price.
  4. Read the headline KPIs (items on radar, wishlist value, projected increase if you wait, savings if you act) and the tariff-exposed share of your budget.
  5. Export a plain-text buy plan grouped by verdict — copy or download.

How to run it

  1. Open index.html in any modern browser (Chrome, Firefox, Edge, Safari).
  2. Drag the Budget, Buy-now horizon, Pass-through, and Threshold sliders, or toggle the category chips — every verdict, KPI, and dollar figure recomputes instantly.
  3. Re-sort the radar (urgency / increase / date / price) from the dropdown.
  4. Click Copy buy plan or Download .txt to export your verdict list.

No build step, no API keys, no accounts. Sample data is embedded inside index.html as a <script type="application/json"> block so the page works directly from file:// with no local server. A standalone copy of the same data also lives at sample-data.json in this folder.

What's in this prototype

  • A live pricing engine (script.js) that, for each item, models two effects: a scheduled tariff step (rate change × price, on its effective date) and a lag tail (a slice of the current tariff still passing through over the ~7-month pass-through lag), both scaled by per-item and global pass-through.
  • A verdict rule that returns Buy now / Watch / No rush from the projected dollar increase, whether a scheduled change falls inside your horizon, and whether the item has tariff exposure at all (domestic / zero-tariff items never get false urgency).
  • A 12-item household wishlist spanning electronics, appliances, furniture, auto, tools, toys, and apparel — crafted to exercise every path: a July electronics step (laptop, OLED TV), an imminent appliance step (washer/dryer, <3 weeks out), post-hike lag tails (sofa at 30%, cabinets at 50%), far-out watches (toys in October), low-impact no-rush items (shoes), a flat item (no scheduled change), and a US-made zero-exposure item.
  • Editable controls — budget, buy-now horizon, pass-through %, and act-threshold sliders, plus category filter chips and a sort dropdown, all wired to instant re-evaluation.
  • KPIs + a budget-exposure bar showing how much of your planned budget rides on tariff-exposed goods.
  • A plain-text buy-plan export (copy or download) grouped by verdict — the artifact you'd actually shop from.

Roadmap

  • Live tariff data — wire the engine to a maintained tariff/HTS schedule and origin-country dataset so rates and effective dates update automatically.
  • Real retailer price feeds and price-history, so "projected increase" is validated against observed shelf prices, not modeled estimates.
  • Spike + price-drop alerts (email/push) that fire as a scheduled rate-change date approaches.
  • Barcode / URL paste to add an item, auto-classifying its category, origin, and likely tariff exposure.
  • Affiliate-linked "best month to buy" calendar and a one-tap "add to cart at [retailer]" handoff.
  • A B2B embeddable widget that shows the honest "buy before [date]" projection on a retailer's own product page.

Sources

Requirements

TariffRadar — Requirements

Goals

  • Turn the vague anxiety of "prices are going up because of tariffs" into a concrete, per-item answer: how much, by when, and whether to buy now.
  • Give each planned purchase a single clear verdict — Buy now / Watch / No rush — backed by a transparent dollar projection.
  • Let the user re-tune the model's assumptions (pass-through, horizon, threshold, budget) and see every verdict recompute live, so the tool teaches as much as it tells.
  • Run entirely client-side, with no accounts, keys, or build step, so the demo works the instant index.html opens.
  • Avoid false urgency: domestic / zero-tariff items must never be flagged Buy now.

Primary user

A budget-aware household shopper with several big-ticket purchases planned over the next few months during a period of rising, tariff-driven prices. They are comfortable on the web, motivated by concrete dollar figures, and want to time purchases — buy the exposed items before a scheduled rate step, and wait on the rest. Job-to-be-done: "Tell me which of the things I'm already planning to buy I should buy now, and how much waiting will cost me."

Functional requirements

  • FR1: Load a wishlist from an embedded JSON seed block, falling back to sample-data.json via fetch when served over HTTP.
  • FR2: For each item, compute a projected price increase from (a) a scheduled tariff step on its effective date and (b) a lag-tail share of the current tariff still passing through.
  • FR3: Scale every projection by the item's own pass-through factor and a global pass-through slider.
  • FR4: Assign each item a verdict — Buy now, Watch, or No rush — from its projected increase, the act-threshold, and whether a scheduled change falls inside the buy-now horizon.
  • FR5: Treat items with zero current and zero scheduled tariff as No rush regardless of price.
  • FR6: Show per-item details: icon, name, origin, retailer, current → scheduled tariff, note, current price, projected price + increase, and a countdown to the scheduled change.
  • FR7: Provide live controls — budget, buy-now horizon, pass-through %, and act-threshold sliders — that recompute the whole view on input.
  • FR8: Provide category filter chips (multi-select) and a sort dropdown (urgency / increase / date / price).
  • FR9: Render headline KPIs: items on radar, wishlist value, total projected increase if you wait, and savings by acting on Buy-now items.
  • FR10: Render a budget-exposure bar showing the tariff-exposed share of the user's planned budget, with an over-budget note.
  • FR11: Export a plain-text buy plan grouped by verdict, via both copy-to-clipboard and file download.
  • FR12: Provide a reset-to-defaults control that restores all controls and filters.
  • FR13: Show an "as of" date sourced from the data, and a persistent "estimates only / not financial advice" disclaimer.
  • FR14: Escape all user-facing string data before injecting into the DOM.

User stories

  • As a shopper, I want each planned purchase tagged Buy now / Watch / No rush, so that I know where to focus without reading every detail.
  • As a planner, I want to see the projected price after the spike and the dollar increase, so that I can judge whether acting early is worth it.
  • As a skeptic, I want to lower the pass-through assumption, so that I can see how the verdicts change under a more conservative model.
  • As a deal-timer, I want to widen or narrow the buy-now horizon, so that "act now" matches how far ahead I actually plan.
  • As a budgeter, I want to see how much of my budget is tariff-exposed, so that I can decide what to defer.
  • As a category shopper, I want to filter to just electronics or furniture, so that I can plan one project at a time.
  • As a list-maker, I want to export a buy plan, so that I can shop from it later or share it.
  • As a careful user, I want US-made items to never show false urgency, so that I trust the verdicts.

Non-functional requirements

  • Self-contained: runs from file:// with no server, no build, no external API, no keys. Only external asset is the Google Fonts stylesheet over HTTPS (optional — degrades to system fonts).
  • Performance: full re-render on every slider input must feel instant for a wishlist of this size (dozens of items).
  • Accessibility: semantic HTML, labelled controls, aria-live on the list, sufficient contrast on the dark theme, keyboard-operable controls.
  • Responsive: two-column desktop layout collapses to a single column and stacked card layout on small screens.
  • Privacy: no tracking, no network calls beyond fonts; all computation is local.
  • Honesty: all figures are clearly labelled estimates; no claim is presented as a guaranteed price.

Out of scope (for the prototype)

  • Live tariff/HTS rate data or origin-country lookups (rates are static sample figures).
  • Real retailer prices, price history, or in-stock checks.
  • User accounts, saved wishlists, or persistence across reloads.
  • Real alerts/notifications, payment, or cart handoff.
  • Affiliate-link attribution and the B2B embeddable widget.

Open questions

  • What is the most defensible default pass-through and lag-tail model once real price-history data is available to calibrate against?
  • Should the lag tail decay over time since the rate's effective date, rather than being a flat share?
  • How should items with multiple component origins (e.g. assembled-in-A, parts-from-B) be modeled?
  • What's the right cadence and channel for spike alerts so they're useful, not noisy?
  • For the B2B widget, what disclosure keeps a "buy before [date]" projection honest and compliant?

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