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Sudeep Das shares how DoorDash shifts from legacy one-shot predictions to an agentic recommendation platform. He discusses leveraging language-native consumer memory, RQ-VAE semantic IDs for catalog representation, and grounded search to dramatically boost relevance and conversion metrics. By Sudeep Das
If you're running Magento 2 with MSI (Multi-Source Inventory) enabled — and since Magento 2.4 it's the default — you have a silent performance killer lurking in your database. The inventory_reservation table grows without bound, and every single cart operation hits it. This post walks through why this table becomes a bottleneck, how to measure the impact, and concrete steps to fix it. How Inventory Reservations Work When a customer adds a product to their cart, Magento doesn't immediately decrement stock. Instead, it creates a reservation — a record in inventory_reservation that says "this quantity is tentatively reserved for this order." The actual stock deduction happens later, when the order is placed and the shipment is processed. The flow looks like this: Add to cart → placeReservation writes a negative reservation record Place order → reservation is linked to the order Ship order → inventory_source_item is decremented, reservation should be compensated Compensation reservation → a positive record that cancels out the original negative one In theory, reservations are transient. They exist to bridge the gap between cart and shipment. In practice, they accumulate forever. The Problem: Unbounded Growth Here's what happens in production: Orders that are canceled leave orphaned negative reservations Orders that fail during checkout leave reservations that are never compensated Partial shipments create partial compensation records Quote conversions that error out mid-process leave dangling reservations Re-indexing, re-stocking, and admin edits can create duplicate records After 6–12 months of moderate traffic, the inventory_reservation table routinely hits several million rows . I've seen tables with 10M+ rows on stores doing 200 orders/day. SELECT COUNT ( * ) FROM inventory_reservation ; -- 4,872,341 rows on a store running 8 months SELECT COUNT ( * ) FROM inventory_reservation WHERE created_at < DATE_SUB ( NOW (), INTERVAL 30 DAY ); -- 4,710,882 — 96.7% of rows are
We just shipped EverShop 2.2.1 — the largest release since 2.0. It folds in the React 19 work that had been sitting in an unpublished 2.1.3 branch and stacks four months of development on top of it: a visual page builder, a blog module, entity custom fields, a multi-language storefront with a translated admin, a rebuilt shipping and fulfillment stack, built-in cloud storage, product recommendations, and a serious security and performance pass. If you're upgrading an existing store, one number to keep in mind: 31 database migrations across 10 modules run automatically on first start. Several of them transform data and drop legacy tables, so back up your database first and read the breaking-changes section below. This release also patches several security vulnerabilities, so upgrading promptly is the right move. Here's a tour of what's new, and what you'll need to change if you maintain themes or extensions. Visual Page Builder The headline feature is a drag-and-drop editor for the storefront, living at /admin/page-builder . You edit any storefront route — plus CMS pages and landing pages — by composing widgets into your theme's areas, with layout-aware drag/drop. The workflow is draft-based: changes accumulate in a per-admin, per-theme draft changeset with per-widget auto-save. When you're ready you can publish immediately, or schedule a rollout for later — and those rollout plans stay editable and cancelable right up until they run. There's inline editing on the canvas (text and images edited in place, with an image picker that understands cloud storage), a layers panel, a "Globals" view for site-wide areas, and per-widget styling controls. Link fields resolve products, categories, CMS pages, and blog posts through a single unified link resolver. Because it's touching public-facing content, the whole editor pipeline went through a dedicated security-hardening pass and ships with an end-to-end test suite. Blog module EverShop now has a first-class blog core module: p
Ruby Rose Bloom sells one-of-a-kind vintage — a self-hosted storefront, no Shopify, no marketplace underneath it. Search Console's "Merchant opportunities" report told me 3 active products weren't showing up on the Shopping tab, and I went looking for the setting to fix. There wasn't one. What I actually found, three days of digging later, is that "get into Merchant Center" is not one thing — it's several different surfaces, each fed by a different mechanism, and the one everyone talks about (the Shopping tab) turned out to be the least interesting of them. This post is the question I actually had, answered with screenshots taken today: I have a storefront. What does getting into Merchant Center buy me, and where do my products actually end up? It also has an ending I didn't plan. After three days of feed fields and structured data I opened one Search Console report I'd been ignoring and found that Google had indexed 5 of my 436 pages — and, chasing that, that essentially none of my product photos were in the image index either. Those two sections are the most useful thing here, and they're the part I'd read first if I were you. What Merchant Center actually is Before the surfaces: Merchant Center is not an ads product by default. There are two lanes. Free listings are unpaid — you register a feed, Google reviews the items, approved items become eligible to appear in Shopping-related placements at no cost per click. This is the lane a small shop should care about first, because it costs nothing beyond the engineering time to feed it correctly. Shopping ads are the paid lane on top — you attach a budget and the same feed becomes the input to a campaign. Ruby Rose Bloom is running free listings only; there is no ad spend anywhere in this post. Free listings in Merchant Center: approved items, no ad spend, click potential still "available soon" on a three-day-old account. Free listings is the whole story for this shop. Worth saying plainly since most "how to get on Goo
Marketplace sellers often discover image problems too late. A product photo may look fine in an editor, but after uploading it to a marketplace it can become: cropped in search thumbnails too small for zoom previews the wrong aspect ratio for a sales channel risky for Amazon-style main image requirements awkward when reused across Etsy, Amazon, TikTok Shop, Shopify, eBay, or Walmart I wanted a simple preflight step before publishing product images, so I built ListingPic : 👉 https://listingpic.com/ What it does ListingPic is a browser-based marketplace image checker and resizer. You upload a product photo, choose the marketplaces you care about, and get a readiness report covering things like: image dimensions aspect ratio file type file size thumbnail crop risk safe-area positioning marketplace-specific warnings The goal is not to replace manual review. It is to catch obvious image risks before sellers waste time uploading, previewing, deleting, resizing, and re-uploading. Why local-first? A lot of product photos are sensitive: unreleased SKUs private product photography branded assets client images images sellers do not want copied or stored elsewhere So ListingPic processes images locally in the browser. Your images are not uploaded to our server for analysis. That also makes the tool fast for quick checks: drop in an image, review the warnings, adjust before publishing. Current checkers The MVP includes marketplace-focused checks for: general marketplace readiness Etsy image checks Amazon product image checks TikTok Shop image checks There are also entry points for Shopify, eBay, and Walmart workflows. Example use case Imagine you have one product photo and want to reuse it across multiple channels. ListingPic can help answer: Is the image large enough? Will the product be cut off in thumbnails? Is the image close enough to square for a channel that prefers square previews? Is the product too close to the edge? Do I need a separate crop for Etsy or TikTok Shop? D
Originally published at avalayer.com/writing , Field Notes 003. Last Sunday a piece of software posted to an IETF mailing list. Not through someone's account as a tool. Under its own signature: "Composed and sent by Elara, this project's AI maintainer, acting under its receipted on-chain mandate," followed by a receipt string you could write down. A reviewer on the list did what reviewers do. He declined to take the claims on faith and asked for manifests and reproducible vectors. The software shipped a corrected test-vector pair with a patch inside a day. A third participant then reproduced the whole thing independently, and along the way discovered that the mailing list itself had corrupted the patch in transit, normalized exactly those transport changes, and got the declared hash back. His summary was a model of saying only what you know: the reproduction establishes the artifact, he wrote, not adoption, and not the broader truth of the events the artifact records. So that happened. An agent that says its name, names its principal, and does the work, on the public record of a standards body. Meanwhile, in front of the same working group, there is a proposal to let automated clients prove they are trustworthy without ever saying who they are. It is called Anonymous Bot Authentication, and the mechanism is elegant. A bot registers with an entity called an Anchor, which checks that it complies with some published policy. The Anchor issues a credential. The bot presents that credential to a website, and the site learns exactly one thing: this client was vetted by that Anchor. Not which client. Not whether it has been here before. Not whether the request an hour ago came from the same machine. The cryptography is designed so the site cannot tell, and so the Anchor cannot follow the bot around either. The reflex, if you sell verification for a living, is to treat the masked agent as a threat and the named one as a relief. I want to argue that both reflexes are wrong in
Magento 2's full page cache is one of its strongest performance features — when it works. But every week, we see stores where a simple product save triggers a 30-second Varnish flush and subsequent cache stampede. The culprit is almost never Varnish itself. It's cache tags. This post covers how Magento 2 cache tags work, why broad tags destroy performance, and exactly how to audit and fix them. How Cache Tags Work in Magento 2 Every cached page, block, and data fragment in Magento is tagged with identifiers. When a product changes, Magento invalidates all cache entries tagged with that product's ID. The tag system is hierarchical: cat_p_123 — specific product cat_p — all products cat_c_5 — specific category cat_c — all categories cms_b_about_us — a CMS block cms_p — all CMS pages These tags are stored alongside cached content and used during invalidation. When you call $cache->clean(["cat_p_123"]) , every cache entry tagged with cat_p_123 is removed. This is elegant until someone tags a global block with cat_p , and saving any product flushes half your store. The Invalidation Storm Problem Here's what happens during a storm: Admin saves a simple product update (price change) Magento generates the invalidation list: cat_p_456 , cat_c (because the product is in categories), cat_p (from a badly written block) cat_p is too broad — it matches the product list page, layered navigation, homepage widgets, and every product detail page Varnish receives 50,000 BAN requests Store goes from sub-100ms response times to 2-5 seconds for the next 10 minutes while the cache rebuilds We've seen this on a store with 80,000 SKUs. A single product save dropped cache hit rate from 94% to 12%. Diagnosing Bad Cache Tags Check Your Current Tags Add this to any block template to inspect what tags are being applied: $block -> getCacheKeyInfo (); // Or for the full page: $block -> getIdentities (); For a full audit, intercept cache writes in development: // In di.xml: < type name = "Magento\Fr
eBay touted an increase in eBay Live's gross merchandise volume as it plans to expand it to more international markets in the coming weeks and months.
The startup's platform predicts what product a shopper wants next, learn their general taste, and fine-tune continuously based on what they do in real time.
Shopify says AI isn’t cannibalizing search traffic the way it has for publishers. Instead, AI-driven traffic and orders to Shopify stores tripled year over year in Q2.
Over the last year I shipped 9 production 3D configurators for polish manufacturers — pools, garage doors, saunas, pergolas, greenhouses, packaging, decorative lamps, terrace roofs, and light-boxes. Each one runs live on its own subdomain of my studio at grodev.pl . Some of the lessons were obvious in hindsight. Some cost me a weekend of debugging. Sharing the non-obvious ones here. 1. Draco compression is not optional for CAD-heavy models Manufacturers send you STEP or SolidWorks files exported to glTF . Raw output is 40–120 MB per variant. On 4G mobile that's a 20-second load with an empty white canvas. Draco compression brings that to 2–5 MB with no visible quality loss on product shots: import { GLTFLoader } from ' three/examples/jsm/loaders/GLTFLoader.js ' import { DRACOLoader } from ' three/examples/jsm/loaders/DRACOLoader.js ' const dracoLoader = new DRACOLoader () dracoLoader . setDecoderPath ( ' /draco/ ' ) // self-hosted, don't use CDN const loader = new GLTFLoader () loader . setDRACOLoader ( dracoLoader ) loader . load ( ' /models/pool-3.5m.glb ' , ( gltf ) => { scene . add ( gltf . scene ) }) Self-host the decoder — Google's CDN version added ~600 ms to first paint in my measurements. Copy node_modules/three/examples/jsm/libs/draco/ to your public/ folder. Tooling: gltf-pipeline -i model.glb -o model.draco.glb --draco.compressionLevel 10 2. Instancing beats individual meshes past ~200 objects A pergola with 40 louvres × 3 tilt positions × user color picker = 120 meshes updating on every frame. Naive approach tanks FPS to 12 on mid-range phones. InstancedMesh batches identical geometry into one draw call: const geo = new THREE . BoxGeometry ( 1 , 0.05 , 3 ) const mat = new THREE . MeshStandardMaterial () const louvres = new THREE . InstancedMesh ( geo , mat , 40 ) const dummy = new THREE . Object3D () for ( let i = 0 ; i < 40 ; i ++ ) { dummy . position . set ( 0 , 0 , i * 0.15 ) dummy . rotation . x = userTilt // update per frame is fine dummy . updateM
Ina and David Steiner inspired the ire of high-level eBay executives after occasionally criticizing the company in their newsletter. In 2019, a plot was concocted to intimidate the couple into halting their negative coverage.
After reporting better-than-expected Q2 results, PayPal said it remains focused on its AI-driven turnaround, but would consider a deal that creates more value for shareholders.
Building Profitable Niche Lifestyle Stores: A Developer's Guide to E-Commerce Essentials Launching a niche lifestyle e-commerce store is more than just setting up a storefront—it's a technical challenge that requires thoughtful architecture, performance optimization, and strategic content planning. Whether you're building a store for outdoor gear, fashion accessories, or humor products like humor24.se , here's what developers need to know. Choose the Right Tech Stack Most profitable niche stores run on WordPress + WooCommerce + a performance-focused theme (like Blocksy). This combination offers: Flexibility : Extend functionality without rewrites SEO-friendly : Built-in structured data support Cost-effective : No complex deployment infrastructure needed Content integration : Blog + products on the same platform Use a headless CMS approach only if you have compelling reasons (high-traffic requirements, complex frontend needs). For 99% of niche stores, the overhead isn't worth it. Core Web Vitals = Revenue Google's Core Updates consistently penalize slow stores. Prioritize: LCP (Largest Contentful Paint) < 2.5s : Optimize images (WebP/AVIF format), lazy load below-fold content, defer non-critical CSS INP (Interaction to Next Paint) < 200ms : Minimize main thread blocking, defer JavaScript CLS (Cumulative Layout Shift) < 0.1 : Use fixed image dimensions, avoid late-loading ads/widgets # Generate WebP variants of product images convert image.jpg -quality 80 image.webp # Check your Core Web Vitals # Use PageSpeed Insights API or Lighthouse CI in your deployment pipeline Each 0.1s improvement in LCP can yield 3-5% conversion uplift. Performance is a feature. Structured Data Wins Traffic Rich snippets dramatically improve click-through rates. Implement: Product schema : name , price , availability , image , brand , AggregateRating BreadcrumbList : Navigation structure FAQ schema : If you have FAQs (47% higher CTR for FAQ snippets) { "@context" : "https://schema.org/" , "@t
A few weeks ago I wrote about describing your site once so any AI can use it. Since then the thing I was hand-waving at ("agents will check out for users") stopped being hypothetical. OpenAI shipped ACP and Google shipped AP2, and they solve the same problem in almost opposite ways. I implemented the merchant side of both. Here are the field notes, because the differences aren't obvious until you're in them. The 30-second version ACP (Agentic Commerce Protocol, OpenAI + Stripe) is session-based. The agent drives a live checkout session on your server, like a headless cart. It powers ChatGPT's Instant Checkout. AP2 (Agent Payments Protocol, Google) is mandate-based. Your store signs a "here is the cart and the price" object; the buyer's agent signs a "I authorize this" object. It leans on verifiable credentials and now the FIDO Alliance. Same goal. Completely different shape. ACP: a checkout session you don't own the UI for ACP is five REST endpoints and a state machine. The agent creates a session, updates it (address, shipping, coupon), and completes it: POST /checkout_sessions create from line items POST /checkout_sessions/:id update (address, shipping, discounts) POST /checkout_sessions/:id/complete pay What you return is a CheckoutSession: line items, live shipping options, and totals broken out (subtotal, discount, fulfillment, tax, total). Money is integer minor units (330 = $3.30). Payment completes when the agent hands you a Shared Payment Token and you charge it through Stripe. The card never touches the agent. The mental model: it's your existing checkout, minus the browser. AP2: sign the cart, don't run the session AP2 has no session. The agent sends you an Intent Mandate ("a red basketball shoe, under $120"). You price it and return a Cart Mandate you have cryptographically signed: { "contents" : { /* a W 3 C PaymentRequest: items , total , currency */ }, "merchant_authorization" : "<RS256 JWT>" // iss , sub , aud , exp , jti , cart_hash } That JWT is a
Originally published at https://eveningdesk.cloud/blog/woocommerce-customer-support-workflow.html . WooCommerce support often starts in a shared mailbox and ends in several tabs. A customer asks where an order is. An agent searches the inbox, opens the store admin, finds the order, checks fulfilment, then returns to write a reply. That sequence is manageable at low volume. It becomes fragile when several people share the work or when the same question arrives all day. The goal is not to add a complicated service desk. It is to make the support workflow reliable: one owner, the right order context, and a reply a human has reviewed. Start With One Support Inbox Use a dedicated address for customer questions, such as support@yourstore.com , rather than mixing them with supplier, marketing, and personal email. Every incoming request should have a clear status: New, when nobody has handled it. Assigned, when one person owns the reply. Waiting, when the customer or carrier needs to respond. Resolved, when no further action is needed. The important part is ownership. If two people read the same message and neither knows who is replying, the customer waits. If both reply, the customer receives conflicting information. Keep Order Context Beside the Conversation Support agents should not guess about an order. A useful workflow links the conversation to the order reference, then checks the information that matters for the question: Order number and purchase date. Payment and fulfilment status. Items, quantities, and shipping method. Tracking or carrier information, when it exists. Previous support messages about the same order. For a connected WooCommerce store, eveningDesk can sync an order record into the support workspace. The agent still reviews the information and decides what to promise. The software provides context. It does not invent delivery dates or approve refunds on its own. Use a Repeatable Reply Process Most store questions fall into a small set of categories: d
When I started working on a WooCommerce slider plugin, I noticed that many existing solutions were packed with features that a lot of websites simply don’t need. More features often mean more CSS, more JavaScript, and a bigger impact on performance. So I asked myself a simple question: What would a WooCommerce slider look like if performance came first? My goals Instead of creating another all-in-one slider plugin, I focused on a few principles: Lightweight codebase Fast loading times Responsive by default Easy integration with Gutenberg Elementor support Shortcode support Clean and maintainable architecture Performance matters Every additional request and every unnecessary asset affects page speed. Some optimizations I implemented include: Assets are loaded only when required. Local libraries instead of unnecessary external requests. Server-side rendering where appropriate. Clean HTML output. Developer experience I also wanted the plugin to be simple for users. Instead of a complicated interface, the goal was: Install Create a slider Insert it into a page Done Lessons learned Building a public WordPress plugin taught me a lot: Documentation is almost as important as the code. User feedback quickly reveals edge cases you never considered. Keeping the codebase simple often leads to better long-term maintainability. What’s next? I’m continuing to improve the plugin by adding new features while keeping performance as the top priority. I’d also love to hear how other developers approach WordPress plugin development and performance optimization. Thanks for reading! ⸻ If you’re interested, you can check out my project here: WordPress.org: https://wordpress.org/plugins/amitry-product-category-slider/ GitHub: https://github.com/amitry-de/amitry-product-category-slider Live Demo: https://slider.amitry.de/
Uber has agreed to acquire Delivery Hero in a $14.8 billion all-stock deal that would nearly double the company’s global footprint and create one of the world’s largest food-delivery platforms outside China.
DoorDash is opening a limited beta of dd-cli, a command-line tool that lets developers and AI agents search stores, build carts, and place orders from the terminal, marking another step toward software designed for AI agents instead of just humans.
Apple has published the policies governing its upcoming Maps advertising business, revealing a strategy that differs from Google’s. The new rules prohibit home services businesses like plumbers, electricians, locksmiths, and roofers from advertising on Apple Maps, along with several other sensitive categories, suggesting Apple is taking a more curated approach to these ads.