标签：#ev

As AI experimentation grows more expensive, Apple is waiving cloud API costs for developers with fewer than 2 million first-time App Store downloads.

This year's WWDC keynote was all about AI. But with all the attention on Apple Intelligence and Siri AI, the company breezed by - or neglected to mention - a bunch of cool, smaller features across its new updates. I've rounded up a bunch of them right here. The new operating systems are available in […]

Updates include a new souped-up Siri, lots of iOS enhancements, and some inkling on how an AI partnership with Google has come to power Apple’s products.

submitted by /u/feross [link] [留言]

Apple just announced watchOS 27, the next version of its Apple Watch operating system, introducing support for Siri AI, a redesigned "dynamic" app grid, and improvements to health and fitness tracking. The watchOS 27 update will be available "this fall," according to Apple, though support is notably limited - the new OS will only be […]

Alright, first, for context, we’re a small early-stage startup (3 engineers total) and we closed our first round of funding 3 months ago. Second, not a lawyer or a GDPR expert. Obviously, I know GDPR is something we’d need to comply with, but we were so focused on rolling out the product to our customers that signed LOIs that it was just never a main focus. Anyways, we were about 2 weeks away from rolling out the product with our first customer based in Germany and we were rigorously testing our platform to make sure there weren’t any major hiccups during our launch. For the most part things were solid, no major bugs (a few tickets in Linear for styling issues), and we were getting good responses. We had Sentry set up for error tracking and PostHog for analytics. For tracing, we're using Braintrust, and thankfully all the data there is being hosted in the EU, so we didn't have to worry about that. But I did notice an issue we had missed. We’ve been logging everything. I’m talking about all the inputs, outputs, and conversation history, which sounds fine and helped us with debugging and building out the product. But our logs also contained a ton of PII. Names users typed into prompts, email addresses that showed up in completions, the occasional address or phone number. It just never crossed our mind that this was user data storage (in retrospect, duh) andd we had zero controls in place. No retention policy, no documented deletion path, no way to respond to an access request. When I flagged it, our CTO was freaking pissed. He didn't want to delay the launch, but going live while logging all that PII was an instant no-go. Which I totally get, not just because of GDPR, but I also think that there was no real reason to hoard that much data in the first place. What we ended up doing was, I think, a clean fix by treating the eval platform itself as the control point. I.e. being intentional about what fields get captured, setting actual retention policies, and making sure

I use GitHub CoPilot in VS Code in my small webdev business, and today I just found out that I burned through my usage quota in two working days, using it the same way as I always have. I know they changed how the plan worked on June 1, but seriously? Previously I rarely hit the ceiling during an entire month of work - and now, in two days of pretty typical use, I hit the limit. I want to unsubscribe from this crap but am not too familiar with the alternatives. What do you recommend based on my use case? Or is it the same with all the CoPilot-like services now? submitted by /u/legable [link] [留言]

This is a submission for the June Solstice Game Jam. What I Built IMITATION is a browser...

Hello everyone! I'm looking for some advice from anyone who's managed a big portfolio of websites. We run about 50 dental practice sites and are finally thinking about consolidating them onto one platform. Nothing fancy, these are pretty simple sites where the main conversions are phone calls, contact forms, and appointment links to third-party booking systems. No e-commerce needed. What we're after: Easy multi-location management Simple content/copy updates Solid/SEO-friendly Clean, modern design Fast and secure I've been looking at WordPress & Webflow , but before I commit I'd love to hear from someone who's actually been in the trenches with 20–50+ sites. If you were starting over today, what platform would you choose and why? Also, if you've gone through a large website migration, what mistakes should I avoid? Appreciate any advice. Thank you SO much! submitted by /u/wallybonanza [link] [留言]

In JavaScript, truthy and falsy values are concepts related to boolean evaluation. Every value in JavaScript has an inherent boolean "truthiness" or "falsiness," which means they can be implicitly evaluated to true or false in boolean contexts, such as in conditional statements or logical operations. What Are Truthy Values? Truthy values are values that are evaluated to be true when used in a Boolean context. Simply put, any value that is not explicitly falsy is considered truthy. These are some truthy values Non-zero numbers: 42, -1, 3.14 Non-empty strings: "hello", "0", " " Objects and arrays: {}, [] Functions: function() {} Dates: new Date() Symbols: Symbol() BigInt values other than 0n: 10n if ( 42 ) console . log ( " This is truthy! " ); if ( " hello " ) console . log ( " Non-empty strings are truthy! " ); if ({}) console . log ( " Objects are truthy! " ); Output This is truthy ! Non - empty strings are truthy ! Objects are truthy ! What Are Falsy Values? Falsy values are values that evaluate to false when used in a Boolean. JavaScript has a fixed list of falsy values false 0 (and -0) 0n (BigInt zero) "" (empty string) null undefined NaN document.all (used for backward compatibility) if (0) console.log("This won't run because 0 is falsy."); if ("") console.log("This won't run because an empty string is falsy."); if (null) console.log("This won't run because null is falsy."); Truthy vs. Falsy Evaluation in JavaScript Whenever JavaScript evaluates an expression in a Boolean (e.g., in an if statement, a logical operator, or a loop condition), it implicitly converts the value into true or false based on whether it is truthy or falsy. With if Statement let s = " JavaScript " ; ​ if ( s ) { console . log ( " Truthy! " ); } else { console . log ( " Falsy! " ); } Output Truthy ! Logical Operators with Truthy and Falsy Logical operators like && (AND) and || (OR) work with truthy and falsy values && (AND): Returns the first falsy operand or the last operand if all are tr

Apple's keynote at this year's Worldwide Developers Conference was a big one. After months of delays, Apple reintroduced us to its AI-upgraded Siri, which will go beyond what the existing voice assistant can do by offering more personalized help. We also got a look at many other updates coming across the operating systems powering the […]

Fresh off the WWDC keynote presentation, The Verge has been invited to an "on-the-record technical deep dive into the bold new architecture enabling Apple Intelligence capabilities." Apple SVP of Software Engineering Craig Federighi and his team will be there, and so will we. The revamped Apple Intelligence is at the heart of nearly every update […]

Siri is now directly embedded into the camera app, and there are more artificial intelligence tools in the Photos app to alter your images.

Apple Intelligence is coming to cameras connected to Apple Home. At WWDC, Apple announced that with iOS27, the Home app will use Apple Intelligence to analyze footage and generate descriptions summarizing what the camera saw. You can also search footage with natural language to find clips from across connected cameras, such as when a package […]

JAVASCRIPT CONDITIONAL STATEMENTS JavaScript conditional statements are used to make decisions in a program based on given conditions. They control the flow of execution by running different code blocks depending on whether a condition is true or false. Conditions are evaluated using comparison and logical operators. They help in building dynamic and interactive applications by responding to different inputs. Types of Conditional Statements 1. if Statement The if statement checks a condition written inside parentheses. If the condition evaluates to true, the code inside {} is executed; otherwise, it is skipped. Executes code only when a specified condition is true. Useful for making simple decisions in a program. Syntax : if ( condition ) { // code runs if condition is true } let x = 20 ; ​ if ( x % 2 === 0 ) { console . log ( " Even " ); } ​ if ( x % 2 !== 0 ) { console . log ( " Odd " ); }; Output Even 2. if-else Statement The if-else statement executes one block of code if a condition is true and another block if it is false. It ensures that exactly one of the two code blocks runs. Used when there are two possible outcomes. The else block runs when the if condition is not satisfied. let age = 25 ; ​ if ( age >= 18 ) { console . log ( " Adult " ) } else { console . log ( " Not an Adult " ) }; Output Adult 3. else if Statement The else if statement is used to test multiple conditions in sequence. It executes the first block whose condition evaluates to true. Allows checking more than two conditions. Evaluated from top to bottom until a true condition is found. const x = 0 ; ​ if ( x > 0 ) { console . log ( " Positive. " ); } else if ( x < 0 ) { console . log ( " Negative. " ); } else { console . log ( " Zero. " ); }; Output Zero . 4. Using Switch Statement (JavaScript Switch Case) The switch statement evaluates an expression and executes the matching case block based on its value. It provides a clean and readable way to handle multiple conditions for a single varia

Hello, I'm Shrijith Venkatramana. I'm building git-lrc, an AI code reviewer that runs on every commit. Star Us to help devs discover the project. Do give it a try and share your feedback for improving the product. A few years ago, choosing a Go mocking framework was mostly a matter of personal preference. Today, things are different. Most Go developers have at least one AI coding assistant generating tests alongside them. Some teams even generate the majority of their unit tests automatically. Yet one area remains surprisingly messy: mocks. Ask an LLM to write a test for the same interface and you'll often get completely different results depending on whether your project uses GoMock, Mockery, MockIO, Minimock, Moq, or hand-written test doubles. The problem isn't that the models are bad. The problem is that mocking libraries represent very different philosophies: Strict vs flexible Generated vs runtime-created DSL-heavy vs idiomatic Go Feature-rich vs minimalist In this article we'll compare the most popular Go mocking libraries in 2026, examine their strengths and weaknesses, and discuss which one may be the best fit for your project. What Makes a Good Mocking Library? Before comparing tools, it's worth defining what matters. A good mocking library should ideally provide: Easy mock generation Clear test failures Minimal boilerplate Strong refactoring support Good IDE experience Readable tests Reliable call verification Different libraries optimize for different parts of this list. That's why there is no universally correct answer. 1. GoMock: The Enterprise Workhorse GoMock remains one of the most widely used mocking frameworks in the Go ecosystem. Originally created by Google and now actively maintained by Uber, it has become the standard choice for many large organizations. Its philosophy is straightforward: define expectations explicitly and verify them rigorously. Example func TestUserService ( t * testing . T ) { ctrl := gomock . NewController ( t ) repo := New

We wired our LLM eval suite into Datadog over about four months. Most of the panels we built got deleted. These are the five that stayed, and the metrics that feed them. TL;DR: We run an LLM-as-judge eval suite on every PR that touches a prompt, and we ship the results to Datadog as custom metrics. The dashboard started with fourteen panels. We kept five. The one that catches the most real regressions is per-criterion pass-rate split out by judge criterion, not the single rolled-up pass-rate number, because an aggregate of 91 percent hid the fact that one criterion had dropped from 0.95 to 0.62. Below are the metrics we emit, the Python that submits them, the monitor config we alert on, and the panels we tried and dropped. Some context on the setup so the rest makes sense. We are a Series-C dev-tool startup. We have a handful of prompts in production that do real work (classification, extraction, a summarization step in an agent loop). Each one has an eval set of tagged examples, somewhere between 80 and 400 per prompt. The judge is a separate model call that scores each output against a rubric. We run the suite in GitHub Actions. The eval job emits metrics to Datadog at the end of every run. Backend service health was already in Datadog, so putting eval data next to it meant one place to look during an incident instead of two. 1. Emit per-criterion pass-rate, not just the rolled-up number This is the one that earns its place. Our judge scores each output against multiple criteria. For the extraction prompt it is four: correct fields, no hallucinated fields, format valid, no refusal. Early on we only emitted one number, prompt_eval.pass_rate, the fraction of examples that passed every criterion. That number is fine for a smoke test and useless for debugging. The problem showed up on a prompt change that looked clean. Overall pass-rate went from 0.93 to 0.91. Two points. Nobody would block a PR on two points. But underneath, the "no hallucinated fields" criterion had

From a stand-alone app to a Google Gemini partnership, here’s everything you need to know from WWDC 2026 about Apple’s upcoming overhaul of Siri.

If you are just getting started with Azure CLI and Bash scripting, this post is for you. I will walk you through how I automated the creation of Azure resource groups for multiple environments using a single Bash script — something that was taking a cloud admin several manual steps every week. This is Project 2 in my TechRush Cloud Engineering bootcamp series. If you want to see where this journey started, you can read my previous post where I tackled deploying a web app across two Azure regions for the first time . That project involved real blockers — quota limits, CLI version mismatches, and a deep dive into Azure Resource Providers. This one went smoother, and I think that is because the previous project was the hard school. The Problem Imagine a cloud administrator who has to create five resource groups every single week, one for each active project: Project-A-RG Project-B-RG Project-C-RG Project-D-RG Project-E-RG Every week. By hand. Management's response was simple: automate it. But here is where the task gets more interesting. Instead of creating one flat resource group per project, the better approach is to create four resource groups per project — one for each environment: Dev Test UAT Production This matters because each environment needs its own access controls, cost tracking, and lifecycle rules. You do not want your Development environment sharing a resource group with Production. Keeping them separate is a real-world cloud best practice, not just a bootcamp exercise. What You Will Need Before running this script, make sure you have the following set up: Azure CLI installed on your local machine. You can follow the official installation guide . An active Azure account . A free account works fine for this. A terminal that runs Bash — Linux, macOS, or WSL on Windows. Understanding the Design The core idea behind this script is parameterization . Instead of hardcoding project names, the script accepts a project name as input and uses it as a prefix for ev

At WWDC 2026, Apple announced an overhaul to its Screen Time parental control features that aim to improve its safeguarding features to protect children who use iPhone, iPad, and Mac devices. Some of the new features coming with Apple's iOS 27, iPadOS 27, and macOS 27 updates include giving parents and guardians more control over […]