标签：#commerce

We've been a Magento agency in Chicago since 2008. When Hyvä Themes hit the ecosystem, we were skeptical—another theme promise. Then we measured Core Web Vitals on client stores and the case became obvious: Hyvä is the most practical path to a fast Magento storefront without a full replatform. This is the migration framework we use at Towering Media for US and Canadian merchants moving off Luma (or aged custom frontends) onto Hyvä. Why Hyvä now (not next year) Google's CWV thresholds affect ad quality and organic visibility. Luma checkout and catalog pages often ship 1.5–2+ MB of JavaScript before you add analytics, chat, and personalization. Hyvä replaces Knockout/RequireJS on the storefront with Alpine.js and Tailwind. Typical results on our projects: 50–70% less frontend JS on category and product pages LCP improvements of 1–3 seconds on mobile field data (highly variable by hosting and images) Lower maintenance — fewer JS conflicts between theme and extensions Delaying migration means paying for performance twice: once in emergency fixes, again in the eventual theme project. Phase 1: Discovery (1–2 weeks) Extension audit List every module that touches the frontend: bin/magento module:status | grep -v "Module is disabled" Flag anything with view/frontend , RequireJS , or Knockout in: Layered navigation and search Checkout and cart Page Builder widgets Blog and CMS enhancements Hyvä maintains a compatibility module ecosystem; unsupported extensions need replacements or custom Hyvä templates. Towering Media includes extension compatibility mapping in every Hyvä migration engagement. CWV baseline Capture before metrics from: Google PageSpeed Insights (origin-level) Chrome UX Report for key templates: home, category, product, cart Real-user monitoring if the client has it (GA4, SpeedCurve, etc.) Store screenshots. Stakeholders forget how slow the old site felt. Business constraints Document: Peak seasons (do not launch in November without war room

Magento's default Luma checkout loads a heavy Knockout.js stack, dozens of RequireJS modules, and payment iframes that fight for the main thread. For merchants where checkout is the conversion bottleneck, shaving seconds off load and interaction time pays back faster than another homepage hero image. We rebuilt checkout in React— React Checkout Pro —for Magento 2 and Hyvä stores that needed Shopify-like speed without leaving Adobe Commerce. Here is what we measured, what surprised us, and what we would do differently. The problem: checkout is where Core Web Vitals go to die Homepage optimizations are table stakes. Checkout is different: More JavaScript. Payment methods, validators, shipping step observers, and third-party scripts stack on one route. More layout shift. Address suggestions, shipping method lists, and tax updates re-render large DOM regions. More input delay. Autocomplete plugins, reCAPTCHA, and BNPL widgets compete on keydown handlers. On a representative Luma checkout (mid-size US retailer, ~80 SKUs in catalog, 4 payment methods), lab tests before migration showed: Metric Luma checkout (before) React checkout (after) LCP (lab, 4G) 4.8s 2.1s INP (field interaction) 320ms 95ms CLS (full flow) 0.18 0.04 JS transferred (checkout route) ~1.9 MB ~420 KB Time to interactive (est.) 6.2s 2.8s Field data from CrUX lagged lab wins by 4–6 weeks but trended the same direction once cache and CDN rules settled. Your numbers will differ. The pattern we see repeatedly: the biggest win is shipping less JavaScript to checkout , not micro-optimizing the JavaScript you keep. Architecture: React island, Magento brain We did not headless the entire storefront. Magento still owns: Quote totals and tax calculation Shipping rate requests Payment tokenization and order placement APIs Customer session and cart persistence React owns the UI layer: step navigation, form state, validation UX, and optimistic updates while Magento APIs catch up. High-level flow: Browser → React Chec

When you build Shopify apps or integrations, pagination becomes important very quickly. A small test store may have a few products and orders. A real merchant store can have thousands of products, variants, orders, customers, inventory items, metafields, and fulfillment records. You cannot fetch all of that data in one Shopify GraphQL request. You need pagination. More importantly, you need pagination that performs well. Poor Shopify GraphQL pagination can create slow syncs, API throttling, timeout errors, duplicate processing, and incomplete exports. This post explains how Shopify GraphQL pagination works and how to handle large Shopify datasets in a practical way. What Shopify GraphQL Pagination Solves Pagination lets your app retrieve data in smaller chunks. Instead of asking Shopify for 50,000 products at once, your app asks for 100 or 250 products per request. Shopify returns the data and gives your app information about the next page. This protects your app from huge responses and protects Shopify from heavy requests. It also gives your integration more control over retries, progress tracking, and background processing. Shopify Uses Cursor-Based Pagination Shopify GraphQL uses cursor-based pagination. That means you do not request data using page numbers. You request the next page using a cursor from the previous response. A basic product pagination query looks like this: query GetProducts ( $cursor : String ) { products ( first : 100 , after : $cursor ) { nodes { id title handle updatedAt } pageInfo { hasNextPage endCursor } } } The first time you run this query, pass cursor as null. Shopify returns the first 100 products and gives you an endCursor . Use that endCursor as the after value in the next request. Keep doing this until hasNextPage is false. Why Cursors Work Better Than Page Numbers Offset pagination usually works like this: page=1 page=2 page=3 or: offset=5000&limit=100 This approach becomes inefficient when datasets grow. The system may need to sk

Wing is expanding into seven more U.S. cities through its partnership with Walmart.

Backed by Alexis Ohanian’s 776 and Kindred Ventures, Zest uses transaction data and AI to generate restaurant recommendations based on users’ real dining habits and the places they frequent.

Pinterest is adding support for Amazon Storefronts, allowing creators to earn affiliate commissions more easily while showcasing their product recommendations in one place.

When a Shopify product CSV imports but product photos fail, the problem is often not the CSV syntax. It is usually that Shopify cannot fetch one or more image URLs during import. Here is the preflight I use before retrying a large product upload: Check that every Image Src or Variant Image value starts with http or https. Local paths like C:\images\shirt.jpg will not work. Open a few image URLs in a private browser window. If the image requires a login, expires, redirects to a file-sharing preview page, or blocks hotlinking, Shopify may not be able to download it. Keep image rows grouped with the correct product handle. Sorting a CSV by image column or price can separate continuation image rows from their product. Watch for URLs that do not end in a normal image extension. They can work, but they are worth checking manually before a full import. Test one small batch first, then verify the product admin after Shopify finishes downloading the images. For a larger file, I also like to extract the image columns into a review worksheet before touching product data. I built a small browser-side checker for that workflow here: https://shopify-csv.aivismonitor.com/shopify-csv-image-url-reachability-checker The important part is to fix image reachability before changing product titles, variants, or prices. Otherwise you can spend time debugging the wrong part of the import.

Zepto's advertising revenue jumped 151%, outpacing the company's 104% growth in operating revenue.

A new feature in the Amazon Shopping app allows users to generate designs with Alexa, then print them on products like T-shirts, hoodies, and tumblers.

You can tap the star-shaped, NFC-enabled wand at terminals to make contactless payments. It's the first of several tap-to-pay hardware doodads coming from Cash App.

The Bengaluru startup has crossed 1 million orders and reached a $50 million annualized GMV run rate within a year of launch.

Amazon will use visual search and AI to show AI generated product images that match your search queries. The retailer says it will help guide users to products.

Caching in e-commerce is never just about speed. A fast storefront is useful only if it still shows the right price, the correct stock level, and the right experience for the current customer. That is why caching in a Next.js storefront can be deceptively hard. Some data should be shared broadly and kept warm for SEO and performance. Some data should be refreshed often. Some should never be shared between users at all. Next.js 16 gives teams a much clearer toolbox for solving this problem with Cache Components, use cache , tag-based invalidation, and explicit cache lifetime controls. Used properly, these features let you keep pages fast without drifting into stale commerce data. In this guide, I will walk through a practical way to think about caching in a modern storefront and show how to combine use cache , cacheLife , and revalidateTag for real e-commerce use cases. Why Caching Is Harder in E-Commerce Than in a Typical Content Site On a standard marketing site, most content changes infrequently. If a page is cached for a few minutes or even a few hours, the business impact is usually negligible. Commerce systems work differently. The same product page may contain: stable product descriptions and category copy semi-dynamic data such as price, availability, shipping estimates, or promotion labels private data such as cart state, recently viewed items, or customer-specific pricing Treating all of that data the same way leads to one of two bad outcomes: You cache too aggressively and serve stale prices or availability. You disable caching everywhere and lose the performance benefits that help SEO and conversion. The better approach is to split your data by volatility and audience. The Three Cache Boundaries That Matter Most For most commerce projects, the cleanest mental model is to divide data into three groups. 1. Stable catalog content This is the part of the page that usually changes only when content editors or merchandisers update the catalog. Examples: product

Google launched Universal Cart at I/O 2026 last week. An intelligent cart that follows users across Search, Gemini, YouTube, and Gmail. ALM Corp published the list of named early checkout merchants on May 20: Nike, Sephora, Target, Ulta Beauty, Walmart, Wayfair, and Shopify brands. I read that list twice looking for a Magento store. None. That's the article. Below: the five-protocol stack you'd otherwise have to read five different specs to understand, the one decision your existing payment processor has already made for you, and a thirty-day Magento-specific playbook to ship before agent-routed traffic starts flowing past your store. If your store runs on Magento or Adobe Commerce, agent-routed traffic is going to flow past you - first in the US, then Canada and Australia "in the coming months," then the UK. The agent layer isn't going to wait for Adobe Commerce to ship native UCP support. The merchants in the first cohort had thirty days of head-start. Most of that window is already gone. Here's what to ship before the rest of it closes. The five-protocol stack, compressed Four protocols define how an AI agent buys something on behalf of a user. A fifth ties payments together. UCP - the discovery layer. Your store publishes a manifest at /.well-known/ucp declaring its capabilities, transports, and payment handlers. MCP - the transport layer. Agents dispatch your commerce tool calls over MCP messages. ACP - OpenAI and Stripe's checkout protocol. Stripe-led coalition. AP2 - Google's payment-authorization protocol. Sixty-plus partners signed at launch: Adyen, American Express, Mastercard, PayPal, Coinbase, Revolut, Worldpay, and more. MPP - Stripe's machine-payments protocol. Same family as ACP. Benji Fisher's synthesis post on dev.to is the sharpest framing I've read: UCP discovers, MCP transports, ACP and AP2 authorize. Read it if you haven't. The UCP spec itself is densifying fast. A loyalty extension landed on May 19 ( #340 ). A schema-validated documentation har

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The Mall lets shoppers build a personalized feed of brands, track sales and drops, and discover products across thousands of retailers.