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Pat Gelsinger wants to pave the way to ever more powerful artificial intelligence using tiny beams of light.

Everyone is arguing about AI ROI right now. Boards want a number. Consultants are selling frameworks. And the headlines look brutal: an MIT report claimed 95% of enterprise GenAI pilots showed no measurable return, and a Forbes piece this January says 56% of CEOs see zero ROI from AI. So AI is a bust, right? Here is the thing. Ask any of those same companies to turn off email for a week. Just try it. Nobody ever ran a six month ROI study on email. Nobody had to. It became the way work happens, and the return stopped being a line item because it was everywhere. I think AI is following the same path, and the data backs it up. The gap between the pilot and the person That same MIT report has a stat that got way less coverage than the 95% number: 90% of employees regularly use personal AI tools for work, while only 40% of companies have an official LLM subscription. Workers adopted it at more than twice the rate of their own employers. The report even found that 70% of workers prefer AI over a colleague for quick stuff like drafting emails and basic analysis. So the official pilot fails its KPI review while the people inside the building are quietly using AI multiple times a day. That is not a failed technology. That is a measurement problem. Worth noting: the 95% figure itself has been heavily criticized. It came from 52 interviews and a narrow definition of success. I would not build a strategy on that number in either direction. Why the ROI is invisible The value seems to be landing in the same place email's value landed: the unglamorous middle of the workday. Drafting the reply. Summarizing the thread. Turning messy notes into something you can send. None of that shows up as a new revenue line. It shows up as an hour you got back and immediately spent on something else. You cannot easily measure that, but you can feel it. Take the tools away from a developer who has been using them for a year and watch what happens. People cannot imagine working without it anymore.

Analysis of bestseller charts shows an 80 percent dip from 2025's sales rate.

It must be earnings day, because Tesla is making a Robotaxi announcement. The company said in a post on X that it was launching its Robotaxi service in Orlando and Tampa, attaching a pair of maps that show each city's service area. No word on fleet size or whether interested customers would be able to […]

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

Nvidia’s Vera Rubin platform combines CPUs and GPUs into a single system, reflecting the company’s growing ambition to power every layer of AI infrastructure.

Stellantis will use technology from Intel's Mobileye to power its Level 2 hands-free driving features for its vehicle brands, the companies announced Tuesday. Starting in 2027, Stellantis will begin using Mobileye's EyeQ system-on-a-chip and a data crowdsourcing program called the Road Experience Management, or REM, which uses real-time data from Mobileye-equipped vehicles to build out […]

The FDA could decide whether to loosen rules around peptide production this week. Some telehealth players are already seeing dollar signs.

Deezer said more than 90,000 AI-generated tracks were uploaded daily on the platform in June.

More than one-eighth of apps analyzed contained foreign code.

GitHub热门项目 | Control what your AI can see. LeanCTX (Lean Context) is the context intelligence layer for AI agents — one local Rust binary that decides what they read, remembers what they learn, guards what they touch, and proves what they save. 60–90% fewer tokens as the receipt. 76 MCP tools, 30+ agents, local-first. | Stars: 3,326 | 14 stars today | 语言: Rust

Building a Decompiler Pipeline in Rust: Why Fission Separates NIR and HIR Decompiler output often looks simple from the outside. A binary goes in. Pseudocode comes out. But between those two points, a decompiler must recover several different kinds of information: instruction semantics register and memory effects control flow stack variables calling conventions data types expressions loops and conditionals readable source-like structure Trying to represent all of this in one intermediate representation quickly becomes difficult. While building Fission , a reverse-engineering and binary decompilation workspace written primarily in Rust, I decided to separate the decompiler pipeline into two main intermediate representations: NIR , a lower-level representation intended to preserve machine semantics HIR , a higher-level representation intended to express recovered, human-readable program structure This article explains why that separation exists, what each representation owns, and why it makes decompiler development easier to reason about. Correctness and readability want different things A decompiler has at least two responsibilities. First, it must preserve the behavior of the original machine code. Second, it must produce output that a human can understand. Those goals overlap, but they are not identical. Consider a simplified fragment of machine-level behavior: tmp0 = RAX tmp1 = tmp0 + 1 RAX = tmp1 flags = update_flags(tmp0, 1, tmp1) A human reader may prefer to see: rax ++ ; The concise form is easier to read, but it omits details that may still matter elsewhere in the pipeline. The flags update could affect a later conditional branch. The operation width may matter. The source and destination could alias. The operation may have originated from an instruction with additional side effects. If the decompiler converts everything into source-like syntax too early, it becomes easy to discard evidence. If it keeps everything at machine level until the final rendering st

GitLab has introduced a new approach to Green DevOps, demonstrating how software engineering teams can measure the carbon emissions generated by their CI/CD pipelines. By Craig Risi

Last month, more than a hundred Stanford students left their own graduation to protest Google’s military contracts and deals with ICE. Two organizers, Amanda Campos and Eva Jones, tell us why.

Every day, the internet quietly convinces us that we're running out of time. A 19-year-old launches...

Gritt is coming out of stealth with $34 million and plan to automate the hardest tasks on construction sites.

Event-Driven Architecture (EDA) is one of the most popular approaches for building scalable distributed systems. Instead of tightly coupling services through synchronous APIs, services communicate by publishing and consuming events. It sounds perfect. Until your consumers start making API calls for every event they receive. At that point, you've reintroduced the very coupling Event-Driven Architecture was supposed to eliminate. This is where Event-Carried State Transfer (ECST) comes in. The Hidden Problem in Event-Driven Architecture Imagine an e-commerce platform. A customer places an order. The Order Service publishes an event: { "event" : "OrderCreated" , "orderId" : "ORD-10234" } Several services subscribe to this event: Inventory Service Notification Service Analytics Service Shipping Service Billing Service Everything looks asynchronous. But here's what actually happens. Order Created Event │ ▼ Inventory Service │ GET /orders/ORD-10234 │ Order Service Notification Service does the same. Analytics Service does the same. Shipping Service does the same. Suddenly, one event generates dozens of synchronous API calls. Congratulations, You've Recreated a Monolith Your architecture now looks like this: Order Service ▲ ┌───────────┼───────────┐ │ │ │ Inventory Shipping Notification │ │ │ └───────────┼───────────┘ API Requests Although events are being used, every consumer still depends on the Order Service. If the Order Service is unavailable: Notifications fail Inventory updates fail Analytics stop processing Shipping cannot continue The event broker isn't the bottleneck anymore. The originating service is. Event-Carried State Transfer Solves This Instead of publishing only an identifier, publish the data consumers actually need. Instead of this: { "event" : "OrderCreated" , "orderId" : "ORD-10234" } Publish: { "event" : "OrderCreated" , "orderId" : "ORD-10234" , "customerId" : "USR-1001" , "customerName" : "John Doe" , "totalAmount" : 249.99 , "currency" : "USD" , "i

GitLab has released version 19.2 of its DevSecOps platform, adding agentic automation aimed at the security and review work that has piled up as AI coding tools generate more code than developers can check by hand. The release, announced on 16 July 2026, brings four features out of beta or into public beta: Dependency Scanning Auto-Remediation, Security Review Flow, GitLab Duo CLI and Custom Flows By Matt Saunders

Welcome to this week's Top 7, where the DEV editorial team handpicks their favorite posts from the...

The tutorial-to-production gap Every RAG tutorial follows the same arc. Load some...