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AT&T suffers setback but will keep asking court and FCC to preempt state rules.

The end of the Ford F-150 Lightning was also the start of a new era for the automaker. After failing to capture the market with the electric version of its popular F-Series truck, Ford is going back to the drawing board with its Universal Electric Vehicle Platform. The first offering will be a $30,000 electric […]

The one-year-old startup aims to reinvent financial architecture for autonomous AI transactions.

Had submitted a paper to ACL ARR May 2026 cycle. Unfortunately, none of the reviewers acknowledged the rebuttal during the author-reviewer discussion I am curious to know from people who had volunteered to review papers this cycle- are you still able to update the ratings, or even your review based on the rebuttal? Also is there any meta-reviewer discussion going on? submitted by /u/Forsaken-Order-7376 [link] [留言]

Netflix has teamed up with a company called SignUp Media to offer ASL interpretations on a bunch of kids shows and movies.

Every AI writing tool does the same thing: you type a prompt, it spits out text, and you copy-paste it into Word. Then you spend the next 30 minutes manually applying headings, fixing list styles, rebuilding tables, and cleaning up formatting that broke on paste. Co-pilot lives inside Word, sure, but it's a writing assistant, not a formatting one. It drafts text, rewrites paragraphs. What it doesn't do is look at your messy 40-page report and restructure it with your actual Word styles, proper heading hierarchy, and correctly formatted tables. That's the gap we set out to close with Stylifyword. What Stylifyword Actually Does: Stylifyword is a Microsoft Word add-in paired with a companion desktop app. You give it a document, a messy draft, a copy pasted ChatGPT output, a stitched together report from five different authors and voila, it writes & edits your text when you ask it to (drafting, rewriting, summarizing the usual AI stuff). Also formats the entire document with your headings, lists, tables, and all. Outputs every change as a tracked redline in Word's Review tab. Nothing touches your document until you accept it. That third point is the key differentiator. It's non-destructive. The AI proposes, you dispose. Why Local-AI support Matters: Here's the thing that got me building this: most professionals who need AI the most can't use cloud AI tools. Corporate attorneys can't paste M&A deal terms into ChatGPT. Healthcare teams can't upload patient data to a cloud endpoint. Defense contractors, compliance officers, financial analysts, they're all locked out of the AI productivity wave because of legitimate data sovereignty concerns. Stylifyword's default mode runs entirely on your machine: 100% on-device inference via a companion desktop app. No Ollama, no command line, no model downloads to configure. Works offline, airplane mode, air-gapped networks, whatever. Zero data leaves your device, ever. Install the app, open Word, and it works. Three Ways to Run It: Not

This week's Java roundup for July 13th, 2026, features news highlighting: a reintroduction of Value Objects (Preview); the GA release of WildFly 41; the July 2026 edition of Open Liberty 26.0.0.7; point releases of TornadoVM, Apache TomEE, Java Operator SDK and LangChain4j; a maintenance release of Micronaut; a new extension, Quarkus Shim; and a new Oracle AI Agent Studio for Fusion Applications. By Michael Redlich

Overview Open WebUI exposed a real Ota lifecycle boundary. This was not mainly a parsing or contract-shape repo. The contract was already strong enough to model: source-checkout verification packaged native runtime through uv run open-webui serve frontend development runtime default Docker Compose runtime What the repo exposed was operational truth after proof: a successful native proof still left a host workload alive the first cleanup fix then widened too far and treated a Compose-owned runtime as the same class of host workload That made Open WebUI a valuable pressure repo. It forced Ota to get more precise about cleanup ownership instead of treating all successful runtime proof as one generic teardown problem. The current pressure contract pins released Ota v1.6.24 . Its latest green matrix run proves the release surface at the exact contract and workflow revision linked below. What Open WebUI exposed in Ota This repo exposed four meaningful weaknesses. 1. proof success was weaker than it looked The first issue was not that runtime proof failed. It was that runtime proof succeeded and still left the native workload alive afterward. In this repo, the packaged native workflow launches: serve:native : launch : kind : command exe : uv args : - run - open-webui - serve - --host - 0.0.0.0 - --port - " 8080" Ota proved that workflow, but the launched process tree was still alive after proof completed. In GitHub Actions, that surfaced through setup-uv post-job cleanup, which blocked while the uv cache was still in use. That was an Ota gap. If proof succeeds but leaves behind repo-owned runtime state that later breaks CI cleanup, the proof surface is still incomplete. 2. native service cleanup widened past its real ownership boundary The first core fix made Ota clean selected native service workloads after successful proof. That was directionally correct, but Open WebUI immediately exposed the next boundary. The Docker workflow uses a native task shape to launch Compose:

A simple tutorial series on go in Hinglish. Jitna mujhe aata hai utna, aur zyada tar mere notes se! Note: Me non-native speaker hu to spelling mistakes to honge, is liye Hinglish spellings aur grammar ke liye main AI use karunga, par dev ke bot ko pata nahi chalega kyuki ye English nahi hai 😉 Parichay / Intro Shuruat hum isse karenge ki Go kya hai aur uske fayde kya hain. Pehle hum Wikipedia ki definition dekhte hain: To, Go ek aasan language hai. Go compiled language hai, matlab ek bar compiler ko humne apna code diya to wo ek executable file dega jo sidha hum chala sakte hain bina kisi aur dependency ke, jaise ki Python ka interpreter. Dusri high-level languages jaise Python ya JavaScript se Go tez chalta hai. Garbage collected hai, yani C ki tarah hame memory ko manually manage karna nahi padega. Go ka istemal kahan hota hai? APIs aur Web Servers banane me Network Programming aur Distributed Systems me Cloud-Native Applications aur Microservices banane me DevOps aur Infrastructure Automation me Command Line (CLI) Tools banane me Agar upar ki baatein abhi samajh na aayein, to koi baat nahi. Filhal itna samajh lijiye ki Go ka istemal bahut jagah hota hai to job ke liye kaam aayega. To ye thi Go ki kahani, CSM ki zubani... (jo chalti rahegi!) Aur haan, har post ke aakhir me meri ek shayari hogi, jo aapko guru dakshina ke roop me jhelni padegi! 😄 ख़यालों को बातों में उलझाए रखना । इस पल के हक़ीक़त को सुलझाए रखना । शिद्दत से चाहा जो मिल कर रहेगा । चेहरे पे मुस्कान थोड़ी बनाए रखना ।। -csm

Microsoft PowerToys includes a Hosts File Editor. It is free, signed, and already on many Windows machines. It is a good editor. It is not always a good hosts workflow . What PowerToys Hosts does well Opens the real Windows hosts file with the right elevation story Simpler than hunting C:\Windows\System32\drivers\etc\hosts in Notepad Free if you already use PowerToys Fine for a handful of static lines When people search for an alternative You switch environments all day Local shop in the morning, client staging after lunch, cutover IP at night. An editor with one big file turns into commented chaos. You want named profiles you can toggle, not archaeology in comments. You also use a Mac or Linux box PowerToys is Windows-only. Your hosts process should not fork by OS if the team shares domain names. You forget ipconfig /flushdns Same bug as every other hosts tool without auto flush: file correct, browser wrong. Alternatives on Windows SwitchHosts Free, open source, profiles, also runs on Mac/Linux. Best PowerToys alternative when you need environment switching and maybe multi-OS later. Locahl Paid one-time. Windows, macOS, Linux. Automatic DNS flush and backups. Best when PowerToys feels too manual and you want the apply step to include flush + safety. Notepad as Administrator Still works. Still easy to save the wrong copy or skip flush. Only fine for rare edits. Hostly / CLI hosts switchers Interesting if you want hosts open Dev from scripts. Check that the project is maintained before you depend on it in CI. Feature snapshot (Windows view) Tool Profiles Auto flush Multi-OS Cost PowerToys Hosts Limited No No Free SwitchHosts Yes No Yes Free Locahl Yes Yes Yes One-time Notepad Admin No No Manual Free Practical upgrade path Keep PowerToys for now if you only have 5 stable lines When you start commenting / staging blocks every week, move to SwitchHosts or Locahl Always backup before the first import After every apply: ipconfig /flushdns ping myapp .test If PowerToys is

Every distributor we spoke to has the same quiet bottleneck, and none of them call it a problem. They call it Tuesday. A request for quote lands in a shared inbox. Sometimes it is a tidy bulleted list. More often it is three lines of text from someone's phone, or a PDF that was scanned at an angle. Someone on the sales desk reads it, works out which catalog part each line actually refers to, checks pricing, and types up a quote. A busy desk does this thirty or forty times a day. It is slow, it is boring, and it is exactly the kind of work where a tired person on a Friday afternoon quotes the wrong bolt and nobody notices until the shipment arrives. We spent three weeks building Distill.ai to do that job. This is what we learned, including the parts that went badly. What we actually built You paste an email or upload a PDF. From there a seven stage pipeline runs: parse -> extract -> classify -> match -> price -> policy -> score Parse cleans the document into text. Extract pulls out the individual line items, quantities, and specs. Classify works out what kind of request this is. Match maps each line to a real catalog SKU. Price applies the pricing rules. Policy runs the business checks. Score attaches a confidence value to every match. The interesting part is not the happy path. It is what happens when the model is unsure. Any line that scores below a 0.70 match threshold does not get quoted. It gets flagged with a reason and routed to a human review queue. A person confirms or corrects it, and the quote goes out clean. That one decision is the difference between a demo and something a sales desk would actually put its name on. An agent that is confidently wrong 5% of the time is worse than useless in procurement, because someone has to check all 100% of the output anyway. An agent that says "I got 47 of these 50 lines, here are the 3 I could not resolve" saves real hours. Why Qwen, and how we wired it up We used two models from Alibaba Cloud Model Studio: Qwen-Plus

submitted by /u/cipherself [link] [留言]

You have been here. The test suite is green. The deployment pipeline reports all checks passed. Then a customer opens a ticket with a screenshot that shows something your test never caught. The test passed in staging. It fails in production. And you cannot reproduce it locally. This is not a flaky test problem. It is a fidelity problem. Your test environment and your production environment are not the same thing. The gap between them is where real bugs live. Let me walk through one concrete example, the fix, and what it teaches about writing tests that survive the handoff to a real user. The Problem: Environment Drift A fintech team I worked with had a checkout flow. The test clicked "Pay Now", waited for a success message, and asserted the text "Payment successful" appeared on screen. It passed every time in staging. Customers reported that after paying, they saw a blank white page for several seconds before the success message appeared. Some of them closed the tab during that blank period, thinking the payment failed. The transaction went through. The customer never saw the confirmation. Support tickets piled up. The test never caught this because the staging environment served the success page in under 200 milliseconds. The blank period did not exist there. Production had a slower downstream service that introduced a three-second delay between the payment confirmation and the page render. The test was correct in what it checked. It was wrong in what it assumed about timing and state. The Fix: Test the Experience, Not Just the Outcome The fix was not to add a longer wait. The fix was to test what the user actually experiences during that gap. Here is a minimal Playwright test in TypeScript that catches this class of problem: import { test , expect } from ' @playwright/test ' ; test ( ' checkout shows loading state before success ' , async ({ page }) => { await page . goto ( ' /checkout ' ); await page . fill ( ' #card-number ' , ' 4111111111111111 ' ); await page