标签：#commerce

Since 2026 every Shopify storefront answers an agent-commerce endpoint at POST /api/ucp/mcp , advertised at GET /.well-known/ucp . Merchants did not turn it on and it is not in their admin. It speaks the Universal Commerce Protocol over JSON-RPC, and the tool that matters is search_catalog : an AI shopping agent asks a store for its catalogue and gets structured product data back - integer prices in minor units with a currency, variants, SKUs, canonical URLs, and a Shopify taxonomy category per product. Fetch the tool list from any store and search_catalog declares catalog.filters.categories , an array of strings documented as "category filters combined with OR logic", next to catalog.filters.price.{min,max} . So an agent should be able to ask for running shoes and get running shoes. We were about to write a paragraph about what it costs a merchant to leave the category field blank. Then we tried it. Method 200 stores, drawn deterministically from a corpus of 10,099 known Shopify storefronts: sort the hostnames, take every Nth. Reproducible, so nobody has to take "we picked 200 stores" on trust. Run on 2 September 2026. Every store got the same five calls, 10 products requested each time: # Call Filter sent A working filter would 1 Control none return products 2 Impossible category gid://shopify/TaxonomyCategory/zz-99-99-99 return nothing 3 The store's own category a category the control's products carry return at least that product 4 Same, unwrapped the bare id without gid://... the other form an agent would try 5 Price control price.max = 1 return nothing - nothing costs a cent Calls 2 and 3 only mean something together. Call 2 alone cannot distinguish "the filter is ignored" from "the filter rejects everything". Those are opposite findings, and both happen. The query matters more than it looks. Generic words ("gift", "set", "new") surface a catalogue's odd corners rather than its catalogue, and produce numbers that are measured honestly and still wrong. Every que

Merchants are told two opposite things about AI crawlers: block them, because they take your content and give nothing back; and admit them, because that is how a store gets into an AI shopping answer. Both assume a decision is being made. We wanted to know how many stores have made one, and which way. Method Every store in a corpus of 10,099 known Shopify storefronts has its /robots.txt read as part of a scan. The file is parsed the way the major crawlers document parsing it: most specific User-agent group wins, * is the fallback, longest matching path rule wins, Allow beats Disallow on a tie. Each of twelve crawler names is asked one question: may it fetch /products/ ? A store counts as blocking a crawler when the answer is no. A second reading looks at the product page for a meta name="robots" tag carrying noai or noimageai . Readings were taken between 29 August and 2 September 2026. Every store that blocked at least one crawler, or carried the tag, is one row in the CSV at the end. The rest of the corpus blocked nothing and is the denominator. What this cannot see. A robots.txt is a request. A store can also block a crawler at the edge, with a bot-management rule or a firewall, and that block is invisible here because the scanner is not the crawler being blocked. Every count below is a floor. Result 133 of 10,099 stores block at least one crawler: 1.32%. Crawler What it feeds Fetches at answer time Stores blocking CCBot Common Crawl no 81 GPTBot OpenAI training & retrieval no 77 Bytespider TikTok / Doubao no 72 Amazonbot Alexa+ / Rufus no 60 Google-Extended AI Overviews & AI Mode grounding no 58 ClaudeBot Claude retrieval & citations no 53 Applebot-Extended Apple Intelligence no 48 meta-externalagent Meta AI no 47 ChatGPT-User Live fetches during a ChatGPT chat yes 11 PerplexityBot Perplexity search & shopping yes 10 OAI-SearchBot ChatGPT search & shopping results yes 6 Perplexity-User Live fetches when a Perplexity user asks yes 3 "Fetches at answer time" marks

Notes An AI shopping assistant for WooCommerce that stores no conversations: what it can answer, what it cannot, and a 10-minute setup Andrej Lauko, ARLing · 6 September 2026 Most chat widgets a WooCommerce store adds today keep a full transcript of what a shopper typed and how the widget answered, sitting in the vendor's database for as long as its retention policy allows. ARLing Asistent, a WordPress plugin for WooCommerce, is built the other way: it answers shopping questions from a store's own product feed and keeps no record of the conversation itself, only a daily count. This note covers why that difference matters, how a feed-grounded assistant actually works, what it answers well and where it stops, the setup, the pricing, and how it compares on price and data handling to Tidio's Lyro and Smartsupp's Mira. 01 Why most chat widgets store every conversation A chat widget that keeps transcripts makes the store a data controller for that content under GDPR: the store decides why the data is collected and how long it is kept, even though the vendor's server does the actual storing. That controller role brings duties many small stores never plan for: a lawful basis for keeping the transcript, a retention period that has to be justified if asked, a way to answer a shopper's access or deletion request within a month, and a data processing agreement with the vendor spelling out what it may do with the content. None of that is hard on its own, but it is another document to keep, another request to route, and another place a customer's name or order number can leak if the vendor has a breach. There is a smaller, everyday reason too: support load. A widget that saves every conversation eventually invites someone to go looking through it, whether to audit what the bot said or to answer "what did it tell this customer." A widget that keeps nothing beyond a daily count removes that job along with the risk. 02 How a feed-grounded assistant works ARLing Asistent connects to

If you are building a pricing, resale, or inventory tool, active listings answer the wrong question. The price a seller asks for an item is not necessarily the price a buyer paid. eBay's public Browse API returns active inventory. Marketplace Insights covers sold history, but access is restricted. That leaves many developers maintaining search-page parsers or using a sold-data provider. This example uses CompSniper because it returns completed listings and a price summary through one GET request. Disclosure: I am Marc, the owner of CompSniper. Make one sold-listings request Node.js 20 and newer already include fetch , URLSearchParams , and request timeouts, so this example does not need an HTTP package. const params = new URLSearchParams ({ keyword : " sony wh-1000xm5 " , count : " 10 " , ebaySite : " ebay.com " , itemCondition : " used " , }); const response = await fetch ( `https://api.compsniper.com/v1/scrape? ${ params } ` , { headers : { Authorization : `Bearer ${ process . env . COMPSNIPER_API_KEY } ` , }, signal : AbortSignal . timeout ( 75 _000 ), }, ); const data = await response . json (); if ( ! response . ok ) { throw new Error ( data . error ?? `HTTP ${ response . status } ` ); } console . log ( " Listings: " , data . totalItems ); console . log ( " Median: " , data . summary . median , data . summary . currency ); console . log ( " Range: " , data . summary . p25 , " to " , data . summary . p75 ); for ( const item of data . items . slice ( 0 , 5 )) { console . log ( item . title , item . soldPrice , item . endedAt ); } Keep the API key in a server, worker, or serverless function. Do not place it in browser JavaScript. Select the buyer's marketplace The marketplace matters. A UK reseller normally wants UK sold listings and prices in pounds, not US listings in dollars. const params = new URLSearchParams ({ keyword : " iphone 15 pro -case -charger " , ebaySite : " ebay.co.uk " , count : " 100 " , itemCondition : " used " , minPrice : " 250 " , maxPrice :

If approved, the combined company would become one of the largest food delivery platforms in the world.

Amazon is adding a scam-detection feature to Alexa for Shopping that can verify whether suspicious emails, texts, and other messages actually came from the retailer.

Amazon is adding a new Alexa-powered feature called “Update Me When” that can send personalized alerts about product launches, tours, books, shows, and other events that could trigger a purchase.

A raw sold-listings search is not automatically a usable comp set. Search for a phone and you may also get cases, chargers, broken screens, empty boxes, and nearby models. Search for a camera lens and you may get caps, adapters, or a different focal length. If those rows go directly into a median, the result can describe the search noise instead of the product. I wanted a larger measurement than a single convenient example, so I ran a fixed 100-product study through CompSniper, the sold-price API I own. The goal was not to prove that an automated classifier is always correct. The goal was narrower: Measure what the production relevance cleaner removed and how the product-level median changed on one predeclared sample. The protocol I selected the products before making the first request: 20 smartphones and tablets 20 gaming and computing products 20 cameras and lenses 20 audio and music products 20 collectibles and luxury products Every search used the same settings: Marketplace: ebay.com Sold window: 2026-06-02 through 2026-08-31 Page: 1 Requested rows: 240 Sort: ended recently Condition: any Relevance cleaning: enabled Each relevance-enabled response contained the raw sample count and raw median captured before classification, followed by the cleaned rows and deterministic price summary from the same fetched page. That meant one production request per product, not separate raw and cleaned fetches. All 100 requests succeeded with unique request IDs. The headline results Across the study: 19,220 priced raw rows were parsed 11,942 priced rows remained after cleaning 7,278 rows were classified out The weighted removal rate was 37.87% 34 of 100 product medians changed by at least 10% 15 of 100 changed by at least 25% 11 of 100 changed by at least 50% The direction was not always upward: 73 medians increased 21 medians decreased 6 medians stayed unchanged That is important. The cleaner is not instructed to raise prices. It tries to retain listings for the requested produ

Amazon is facing a new lawsuit from the FTC and 22 states for allegedly secretly charging businesses more for advertising.

For realtime release compatibility in a delivery tracking map, scale event delivery with a durable, ordered log per delivery and treat every browser connection as a disposable projection of that log. Presence can guide fan-out and capacity planning, but it must never decide whether a location update exists. Short answer: release compatibility comes from versioned envelopes, resume cursors, and an explicit resync path; scaling comes from partitioning by delivery ID and coalescing map updates at the edge, not from trusting a long-lived connection to carry every event exactly once. This decision targets an e-commerce tracking experience in which a shopper may open a map, lose connectivity in a tunnel, return on another network, and also join a delivery-specific support chat room. The deciding constraint is presence accuracy: an online indicator is useful only when its expiry rules are understood, while the delivery state must remain correct even when that indicator is late. A green dot isn't a commit log. How should realtime release compatibility scale event delivery in a delivery tracking map? Separate the system into three contracts: durable delivery state, transient room presence, and the connection used to move updates. The first contract owns truth. The second answers a narrower question: which sessions have renewed a lease recently enough to be considered reachable? The third may disappear at any point and should be replaceable without changing either of the other two. For each delivery, append an event with a monotonically increasing sequence within that delivery's partition. The client persists the last applied sequence and includes it when reconnecting. If retained events cover the gap, the server replays them in order; if they don't, the server returns a fresh snapshot plus its sequence. This is at-least-once delivery with idempotent application, which means duplicates are ordinary and gaps are detectable. It does not promise global order across unrelated del

A merchant running a small programme finds that percentage-based apps charge more as sales grow, while flat-fee tools often rely on third-party cookies that browsers increasingly block. I build ZeroCut. The app charges a fixed monthly fee — Free, $19, or $39 — with 0% commission on tracked sales, enforced server-side. Attribution follows three signals in order: a _zc_ref cart attribute from the theme embed, a real Shopify discount code per affiliate, then the landing_site query param. A 30-day window uses first-party cookies and localStorage only; no external scripts load on the storefront. When an order is cancelled or refunded, the commission reverses automatically. Partial refunds reverse proportionally. Anything already marked paid is never rewritten. An optional hold period delays approval. The app does not store customer PII. It also does not rewrite commissions once they are marked paid, even if a later refund occurs. For very small programmes, a percentage app's free tier can cost less than any flat fee including ours.

Flipkart's quick-commerce venture is delivering 1.1 million to 1.2 million orders a day, nearly triple its November volume.

Are pigs flying? Walmart has finally caved on its refusal to support Apple Pay and Google Pay.

Affiliate disclosure: I’m an independent Payhip Partner. The optional signup link at the end is my partner link; I may receive a commission from Payhip if a referred seller generates eligible revenue. I am not a Payhip employee or official representative. A creator selling one template or downloadable guide does not need to write a payment backend. The safer architecture is usually: Carrd or static page ↓ Payhip product page or direct checkout ↓ Hosted payment and product delivery Your public page explains the offer. The hosted commerce platform owns the payment flow. No card data, secret keys, or payment logic belongs in Carrd. This tutorial shows two link-based integrations and one optional embed route. Before you start You need: A published product in Payhip Its public product URL A button on your Carrd or static page A clear product description, support contact, and terms In Payhip, the product URL is available from the product’s Share / Embed controls. A typical product URL has this shape: https://payhip.com/b/PRODUCT_KEY Use your real product key in every example below. Option 1: Send visitors to the product page This is the safest default when the buyer still needs details before purchasing. In Carrd: Select the call-to-action button. Set its URL to your full Payhip product URL. Use a descriptive label such as View template details or See what’s included . Preview the page on desktop and mobile. On a conventional static site, the equivalent HTML is just an anchor: <a class= "product-button" href= "https://payhip.com/b/PRODUCT_KEY" > View product details </a> No JavaScript is required. Use this route when the Payhip product page contains important previews, license terms, compatibility notes, or variations that do not fit on your landing page. Option 2: Link directly to checkout If your landing page already gives the buyer everything needed to decide, a direct checkout removes an intermediate page. Payhip documents this URL format: https://payhip.com/buy?link=

Amazon is significantly expanding its Prime Air drone delivery service, with plans to reach nearly 500 U.S. cities by the end of 2026.

Apple is simplifying its EU App Store fees, replacing its per-install fee with a 5% commission for apps distributed outside the App Store and making it easier for developers to operate alternative app marketplaces.

WordPress.com Education lets teachers offer their students free domains, plugin support and professional website-building tools.

Uber is also making investing in Zipline a part of the tie-up.

Every Hydrogen project I've worked on started the same way — wire up a cart, build a PDP, add filters, then spend weeks on the "extras" that aren't really extras: wishlist, compare, quick view, proper i18n, RTL. After doing this enough times I decided to build it once and properly. ada ÉLAN is a Hydrogen storefront theme for fashion brands. It ships: — An editorial design system (Cormorant Garamond + Plus Jakarta Sans, documented design tokens) — Real i18n — English, French, Arabic with full RTL layout — Merchandising surfaces: lookbook, shop-the-look, compare, wishlist, quick view, reviews — A seeding CLI that provisions metaobjects and demo products so your store isn't empty on first run — Unit tests (Vitest), E2E across 5 browsers (Playwright), Storybook component docs Stack: Hydrogen 2025.7, React Router 7, Tailwind 4, TypeScript strict, Zustand, Framer Motion. The seeding CLI is probably the part I'm most proud of. You run one command and it creates all the metaobject definitions, uploads demo content, and provisions products. No more manual setup in the Shopify admin. MIT licensed. Feedback welcome. github.com/ozgursagiroglu/shopify-hydrogen-fashion-theme

I audit catalog migrations for a living. Every disaster I've seen was preventable — not by hiring better, but by agreeing in writing what "done" means before work starts. Copy this list. Send it to whoever is doing your migration. Ask them to commit to each line — and note the italics: every promise comes with a way you can check it yourself in about two minutes, no tools, no trust required. Every product made it across — none lost, none duplicated. Compare row counts in both files. Every variant made it across. Pick any product, count its rows in both files. No handle silently renamed ( -1 , -2 suffixes). Search the new file for -1 , -2 . SKUs unchanged and unique. Pick 5 SKUs from your export, find them in the new file. Prices and compare-at prices identical. Pick any product, compare both price fields. Inventory identical. Same spot-check. No missing titles, vendors, types, or prices. Sort each column, look for blanks at the top. Images attached to the right variant , not dumped at product level. Open a product with colors; each color shows its own image. Every image link loads. Click any 5 image URLs. Collections intact. Pick a collection, compare its product count. Custom fields (metafields) survived. Open a product that had them. Every old URL redirects. Try 5 URLs from your old sitemap. No garbled characters. Search the file for †. No description empty or cut short. Read 5 descriptions in both files. Description formatting survived (bullets, tables). Same 5 products. Option names still meaningful ("Size", not "Option1"). Open any product with options. Option values mean what they meant. Compare the value lists. Two more things worth writing down: What happens if a check fails — fix at no charge? partial refund? Agree now, not later. Anything already broken in your source data — list it upfront so nobody argues about whose fault it was. If your provider hesitates to commit to a list like this, that hesitation is information. (I keep a pre-filled version of t