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I told myself I wouldn't buy an OhSnap accessory for my phone. Every one I'd tested was nice, but felt overpriced and slightly flawed. After I weighed in on the $40 Snap Grip 5 last year, I wound up sticking with my $25 Syncwire instead. But OhSnap's $50 Snap Grip Stand finally got me. To […]

Don't wait to have your car checked out if its traction battery is having problems.

One of the big tradeoffs with Xteink's pocket-friendly e-readers is a lack of easy access to ebooks. On a Kindle or Kobo you can download tens of thousands of titles through each device's respective online bookstores, but with Xteink's devices you have to source DRM-free ebooks on your own. That's about to change, as the […]

It wasn't long ago that shooting with a super high-resolution camera meant making serious sacrifices for the sake of all those megapixels. Enter the A7R VI. Sony's latest flagship high-res camera is a 66.8-megapixel spec monster. In addition to its modest bump in resolution from the last-gen A7R V, it uses a new sensor and […]

Utility-scale solar leads by a mile, followed by batteries. Fossil fuels, not so much.

Noware’s Puff necklace whimsically reimagines flash drives, turning something functional into personalized jewelry.

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

New fonts, better customization, and a whole new layout all make it worth dusting off your Instapaper account.

We’ve been testing bands since the first Apple Watch launched in 2015. From silicone sports straps to leather bands, I’ve rounded up the best for every occasion.

I built a small manufacturing company end-to-end inside an SAP S/4HANA sandbox — one plant, one product, one month — specifically to watch what the month-end close does to a margin that looks healthy at billing time. Every number below comes from an actual document in that system. At billing, the month looked good Revenue 20,000 COGS at standard 12,800 Margin 7,200 = 36% Three days later, after the close, the same month landed at 1,200 = 6% . Nothing was posted incorrectly. Three gates took the 30 points, in this order. Gate 1 — Cost center revaluation (KSS1 / KSII) The planned price for the labour activity type was derived the usual way: planned cost divided by planned activity quantity. Production orders consumed hours at that planned rate all month. Then the actuals arrived. Depreciation posted 9,000 against a plan of 3,000 . Activity quantity did not move. So the actual activity rate came out at roughly three times the planned rate, and every hour any order had already consumed became retroactively more expensive. This is the part that surprises people: the damage was decided weeks earlier, in a transaction nobody files under "costing decisions" — planning the activity price. Gate 2 — Order variance (KKS1 / CO88) With the revalued rate applied (CON2), the production orders no longer settled clean. The difference split across variance categories and settled to variance accounts — not into inventory. That distinction matters. If it went to inventory, it would sit on the balance sheet until the goods were sold. It doesn't. It is parked, waiting for the next step. Gate 3 — Actual costing (CKMLCP) This is the step people forget, and it is where the margin actually dies. The actual costing run rolls the variance into the material's periodic unit price, and then moves the portion belonging to what was already sold into COGS. Before this run, the P&L still looked fine. After it, the 6,000 that had been sitting in variance found its way onto the income statement. What I

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

When Twitch announced that streamers could opt out, thousands of users questioned why their content was being used to train AI models in the first place.

Real transaction data is never clean — and the worst part is that it looks clean. This is a short story from a real dataset (UCI Online Retail: 541,909 e-commerce transactions) about the quietest way to destroy data: silent type coercion. All numbers below come verbatim from an executed notebook. The head looks perfect Peek at the first rows of the file and InvoiceNo parses as clean integers — 100% parse rate, full confidence. Any type-inference step, mine included, would call it int64 and move on. Measure the whole file instead of the head, and the number drops to ~98%. The other 2%: invoice numbers starting with "C" — which in this dataset marks a cancellation . Coerce the column to numeric and every one of them becomes NaN : Invoice numbers destroyed by numeric coercion: 9,291 DextraLoaderWarning: load: ambiguous decision(s): column 'InvoiceNo': ambiguous - float64 at parse_rate=0.98 An entire class of business events — silently gone. No exception, no crash. That's what makes coercion the quietest bug in data work: the pipeline succeeds . Why those 9,291 rows matter They are not noise. They are the returns side of the business : cancelled orders worth 8.4% of everything sold. Lose them and every revenue number downstream is quietly wrong. One example of what they catch: the dataset's apparent #1 bestseller, "PAPER CRAFT, LITTLE BIRDIE" (168,470 GBP), is a phantom — a single 80,995-unit order entered at 09:15 and fully cancelled at 09:27 the same morning. Only the preserved cancellation rows expose it. The genuine bestseller is a cake stand. The fix: identifiers are labels, not quantities No library can know that "InvoiceNo" is an ID — that's domain knowledge. What a tool can do is disclose its guess and hand you a replayable plan you can correct: naive , plan = dx . load ( CSV_PATH , return_params = True ) # warns: ambiguous at 0.98 plan [ " columns " ][ " InvoiceNo " ][ " dtype " ] = " object " # invoices are labels plan [ " columns " ][ " StockCode " ][ " dtype

Building Pure Dart Web Apps Without Compromise When developers evaluate Dart for the web, they typically face a stark tradeoff: Flutter Web : Exceptional for canvas-driven applications, design systems, and cross-platform desktop/mobile parity—but heavy for content-first landing pages, docs, and fast-loading SEO sites. Jaspr Web : A lightweight, component-driven framework that compiles pure Dart to HTML and CSS with instant first paint and full search engine indexing. When we built the official documentation and showcase site for BlocSignal , we knew Jaspr was the perfect foundation. But like many engineers diving into a new UI paradigm, our initial implementation took a shortcut: we used raw StatefulComponent lifecycles and manual .subscribe() callbacks to wire up our state machines. It worked—but it wasn't idiomatic. In this behind-the-scenes case study, we walk through the process of dogfooding bloc_signals_jaspr across blocsignal.dev , replacing manual subscription glue with declarative consumer components, achieving 100,000 operations/sec in compiled JavaScript , and exploring the sheer developer ergonomics of Dart 3.13 primary constructors . The "Manual Subscription Trap": Why Raw .subscribe() Fails at Scale In classic Flutter or Jaspr development, when you create a state machine without framework-level consumer widgets, you might be tempted to subscribe inside initState() : // ❌ THE ANTI-PATTERN: Manual subscription glue in StatefulComponent class LiveVisualizerState extends State < LiveVisualizer > { late final LiveCounterBloc _bloc ; @override void initState () { super . initState (); _bloc = LiveCounterBloc (); // ⚠️ Flaw 1: Every state change triggers a full component setState _bloc . state . subscribe (( _ ) { if ( mounted ) setState (() {}); }); } @override void dispose () { // ⚠️ Flaw 2: Manual dispose tracking _bloc . close (); super . dispose (); } } While this appears harmless in a simple counter demo, it introduces three severe architectural flaws:

It happened during a quiet Friday sermon at the local masjid. The room was dense with silence, the kind that feels heavy and intentional. Suddenly, a jarring ringtone shattered the atmosphere—someone’s phone, vibrating against the hardwood floor. It wasn't my phone, but the collective wince of the entire room was visceral. A hundred people stopped mid-thought, turning their heads toward the source of the noise. I sat there, my own phone tucked in my pocket, realizing that I had almost been that person just a week prior. It was a moment of pure, avoidable human friction. We live in an age where our devices are supposed to be smart, yet they consistently fail at the most basic context-awareness. I found myself manually toggling my sound profile before every meeting, lecture, or appointment. It is a recurring cognitive tax. If I remembered, great. If I forgot, I risked social embarrassment. Even worse, once the meeting ended, I would inevitably leave my phone on silent for the rest of the day, missing important calls from family or clients. Existing solutions often felt like overkill—they required account creation, constant background sync to a cloud server, or permissions that felt invasive for a task as simple as changing a volume setting. I wanted something that lived entirely on the device, functioning as a silent, invisible utility that didn't need to 'phone home' to function. When I started building Muffle, I decided early on that the entire architecture would be zero-cloud. This wasn't just a philosophical choice; it was a technical constraint I imposed to ensure the app remained performant and trustworthy. By forcing myself to avoid backend dependencies, I had to rely heavily on Android’s AlarmManager and ForegroundService patterns. The biggest challenge was the 'Prayer Time' trigger. Most developers would reach for a Firebase Cloud Function to calculate these times based on the user's location. Instead, I integrated the Adhan library locally. I had to handle c

The company will also be able to expand its fleet across more of the San Francisco Bay Area and Los Angeles.

At a press conference outside Madison Square Garden, politicians, musicians, and privacy advocates argued for tighter restrictions on how public venues deploy biometric surveillance.

That makes it the cheapest EV in Kia's lineup.

Aurora Innovation and Kodiak AI, two companies developing self-driving trucks, have received permits from the California Department of Motor Vehicles.

Airline-backed venture aims to develop a hybrid-electric commercial aircraft.