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Shortcuts gets an AI upgrade, letting you describe the workflow you want in a prompt.

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

A new spatial "Reframe" feature will let users use AI to adjust perspectives.

If you work with Brazilian companies — as an accountant, credit analyst, or anyone processing PJ clients at scale — here's a practical automation approach using free public data. What you can verify automatically For any CNPJ, public data gives you: Situação cadastral : ATIVA, BAIXADA, INAPTA, SUSPENSA — critical for invoice validation Razão social : legal name for contract matching CNAE : is this company allowed to do what they claim? QSA : who are the actual partners/directors? Data abertura : how old is the company? The data 65M+ CNPJs from Receita Federal, indexed and searchable at Jurídico Online . Free. Also available as a Python package: pip install juridico-online from juridico_online import empresa_url , buscar_url # Get company page URL for a CNPJ url = empresa_url ( " 00.000.000/0001-91 " ) print ( url ) # https://juridicoonline.com.br/empresa/00000000000191 # Search by company or partner name search = buscar_url ( " Magazine Luiza " ) print ( search ) Checks worth automating 1. Situação ATIVA before accepting any invoice INAPTA or BAIXADA companies cannot legally issue NF-e. 2. CNAE vs service being billed A company with CNAE "comércio de alimentos" billing for software development is a red flag. 3. Company age vs contract value A 3-month-old company offering a R$500k contract deserves extra scrutiny. 4. Shared partners across suppliers If two suppliers share directors, that's a conflict of interest. Search partner names at juridicoonline.com.br to see all companies they control. Integration patterns ERP/AP : validate CNPJ status before releasing payment Onboarding : auto-fill razão social when client enters CNPJ Batch audit : cross-check your vendor list quarterly Monitoring : alert if a key supplier's CNPJ changes status The data is public, free, and updated regularly. No excuse to check manually at scale.

Along with new software for Macs and iPhones, Apple announced visionOS 27 during its keynote presentation at WWDC on Monday. Like its other platforms, visionOS 27 will feature Apple's updated Siri AI assistant and the new dedicated Siri app. While wearing the Vision Pro, you can use visual intelligence to ask Siri "about anything in […]

Another productive week of Google Summer of Code with OWASP BLT is in the books! Building on the visual blueprints from last week, this week was focused on locking down our structural foundations and diving deeper into the system architecture. Here is a simple breakdown of the progress made and what lies ahead. Milestones The primary goal for this phase was setting up the structural guardrails for how data travels through our app. Finalized Security Contract Structures: Successfully established the foundational security contract structures. This ensures that our application components have a uniform, strict schema to communicate safely and predictably. trying to figureout some missing point on the security_alerts with the help of my mentor. Merge request updates: Glad to share that the initial setup has been done across our repository through these milestones: 🔗 Merge Request #3 🔗 Merge Request #4 🔗 Merge Request #5 🔗 Merge Request #6 🧠 Current Focus: System Design & UI Polish With the basic structures merged, my day-to-day focus has shifted toward high-level engineering and refining the user experience. Architecture & System Designing: Spending time mapping out the data flows to ensure our local-first storage design works seamlessly with minimal, encrypted web updates. Ongoing UI Revamp: Continuing to polish the user interface layouts based on our initial feedback, ensuring the experience feels clean, intuitive, and highly minimal. ⚡ The Next Step: Building the Workers Now that the structural blueprints are active in the project, it is time to make them functional. ⚙️ Contract Workers: Moving forward, the next step is to start creating the edge contract workers. These workers will handle the actual validation and processing logic for the security contracts we just established. The structural groundwork is officially laid, and the architecture is shaping up beautifully. Excited to bring the core backend logic to life next week! 💻🛡️

"If you're grabbing a bite with friends and point your iPhone at the bill, then [you can] select what you ordered to split the tab with Apple Cash," said Apple VP of Software Sebastien Marineau-Mes.

Been doing a lot of competitive research for clients lately — checking hashtag volumes, tracking top posts in a niche, that kind of thing. Kept switching between Claude Code and browser tabs to cross-reference stuff manually. Got annoying fast. Found hikerapi-mcp, a Model Context Protocol server that exposes 100+ Instagram endpoints as tools directly inside Claude Code. Figured I'd try it. Setup was straightforward. The one thing I did differently was keeping the API key out of config files entirely — passed it as an environment variable instead. Smaller attack surface if I accidentally commit something. Also filtered down the tool groups with HIKERAPI_TAGS because 100+ tools showing up in context is chaos. I only need hashtag search and competitor profile data, so I scoped it to just those. "env": { "HIKERAPI_KEY": "${HIKERAPI_KEY}", "HIKERAPI_TAGS": "User Profile,Post Details,Search,Hashtags,Stories" } One thing that tripped me up for a solid 20 minutes: HikerAPI runs on a prepaid model (credits in rubles). If your balance is zero, you get HTTP 402, not 401. I kept thinking my key was invalid and regenerated it twice before I figured out I just needed to top up. Once that was sorted, it actually works well. Now I can ask things like "what are the top 10 posts for #socialmediamarketing this week" or pull a competitor's recent content directly in the same session where I'm building the campaign strategy. Cuts out a lot of context switching. Repo if you want to check it out: github.com/subzeroid/hikerapi-mcp Wrote up the full setup with config details here if useful: https://dev.to/simrp360/querying-instagram-from-claude-code-wiring-up-hikerapis-mcp-server-57jf Anyone else using MCP servers for social data research? Curious what other setups people are running.

Most people pick a model the wrong way around. They look at the leaderboard, see Opus on top, and reach for it by default. Sonnet if they want to save money. Haiku almost never, because the name says "small." That habit costs you. For a lot of what you actually build, Haiku is the right call, and you're paying three to five times more for capability the task never uses. This post is about how to choose, and why Haiku should be your default more often than it is. The short version: don't start from "what's the best model." Start from "what does this task need." Most tasks don't need much. Comparison Here is the current lineup, with the numbers that matter when you're choosing. Haiku 4.5 Sonnet 4.6 Opus 4.8 Model ID claude-haiku-4-5 claude-sonnet-4-6 claude-opus-4-8 Input price (per 1M tokens) $1 $3 $5 Output price (per 1M tokens) $5 $15 $25 Context window 200K 1M 1M Max output 64K 64K 128K Best at speed, volume balance hardest reasoning Two things jump out. First, price . Haiku input is a fifth of Opus and a third of Sonnet. Output is the same ratio. If you send a million tokens through Opus for $25 and the same work would have been fine on Haiku, you spent $20 for nothing. And that gap is per request, so it compounds. A feature that runs ten thousand times a day on Opus instead of Haiku is not a rounding error. It is the difference between a feature that ships and one that gets cut for cost. Second, the context window . This is where Haiku gives something up: 200K tokens instead of 1M. That is the real tradeoff, and it points straight at when to use it. We'll come back to that. The mental model Stop ranking models. Rank tasks . Ask three questions about the task in front of you: Does it need real reasoning, or is it bounded? A task is bounded when a competent junior could do it from a clear spec without much judgment: pull these fields out, sort this into one of five buckets, rewrite this in a different tone, answer this from the text I gave you. A task needs reason

AI design system output is approximate by default. Wrong border radii, raw hex values, inconsistent tokens across 60 components. The fix isn't better prompts. Here's the structural change that made it exact using Figma's REST API. The fourth time I manually corrected the same border radius mistake in an AI-generated component, I stopped and asked why this kept happening. Not "what prompt would fix this?" The deeper question: why does every AI tool I tried get the structure right and the values wrong? The button was correct. The variants were there. The layout matched the Figma spec. But borderRadius: 8 when it should be borderRadius: '8px' . A spacing gap of 8 when the spec said 6 . The color #3B82F6 sitting in the file where semantic.button.primary should be. None of it wrong in a way that breaks the build. All of it wrong in a way that breaks the design system. After hitting this wall enough times, I realized the problem wasn't the AI. It was the question I was asking it. Why AI keeps generating the wrong Figma design tokens When you give an AI tool a Figma screenshot and ask it to produce a component, it does something reasonable: it interprets what it sees. The structure, the layout, the hierarchy - it gets most of that right. What it cannot get right is the token mapping. The AI doesn't know your semantic token file. It doesn't know that #3B82F6 maps to semantic.button.primary in your codebase. It doesn't know that your MUI setup multiplies numeric border radii by 4, which means borderRadius: 8 renders at 32px instead of 8px . So it approximates. Here's what that looks like in practice: What AI produces What the spec requires Why it's wrong borderRadius: 8 borderRadius: '8px' MUI multiplies numeric values by 4 gap: 8 gap: 6 Spacing value not extracted from Figma color: '#3B82F6' semantic.button.primary Raw hex instead of semantic token fontSize: 14 variant="MD_Medium" Typography token not resolved Across one component, these deviations are small. Across 60 comp

I am a last-year data science major at university who initially joined because of AI's exciting potential across numerous industries. However, after learning about multiple companies backtracking on their AI use on their platforms and cutting back on their data center expansions, I can't help but think that something is very wrong behind closed doors. I came to understand that the demand for AI is slowly decreasing in some areas and increasing exponentially in others. To me, it seems every major industry "needs" AI to make life easier, yet is backtracking when it doesn't perform the way they want it to. My concerns revolve around how unpredictable AI's usage is. If I get involved in an industry that actively destroys land, water, and other resources, I would hope that the environmental costs will be outweighed by the benefits everyone sees from AI. However, with the economic trend of AI's value decreasing for companies that initially went all in on it, I can't help but feel like I'm actively destroying the planet. Does anyone have any suggestions or moral redemption for me? I want to jump ship before the big explosion, but I'll stay if there's great potential for growth with AI. submitted by /u/Emergency_Ad6929 [link] [留言]

80%. That is the share of code currently being merged into Anthropic's production systems that was written by Claude. Not code-reviewed. Not pair-programmed. Written. In February 2025, when Claude Code launched, that number was in the low single digits. Sixteen months later, the company decided that data point — and the trajectory behind it — was worth a public warning. On June 4, 2026, Anthropic published "When AI Builds Itself," a research paper co-authored by Marina Favaro, head of the Anthropic Institute, and Jack Clark, one of the company's co-founders. It was the first major publication from the Anthropic Institute since its founding in March 2026. The paper did two things simultaneously: disclosed internal productivity data that most AI companies keep private, and called for a global mechanism to slow or pause frontier AI development before the process becomes self-sustaining without meaningful human direction. The data came first. The policy recommendation followed from it. Here is what the numbers actually show and why every developer building on AI infrastructure today should read this carefully. The Productivity Curve Nobody Predicted Anthropic published a chart of engineering output per engineer, indexed to a baseline from 2021–2024. The curve is flat for four years. Then Claude Code shipped in February 2025. The multiplier progression from that point: 1.2x, 1.5x, 1.9x, 2.5x. By Q1 2026: 5.8x. By Q2 2026: 8x. The typical Anthropic engineer is now merging eight times as much code per day as they were in 2024. Not 8% more. Eight times more. That is not a productivity improvement — it is a different category of output from the same headcount. To understand what drives the number, you need to understand what Claude Code actually does inside Anthropic's engineering workflows. The tool was built for and by engineers working on frontier AI systems — which means the tasks it handles are not boilerplate CRUD endpoints. Claude is writing test harnesses for novel m

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

Apple is putting control back into the hands of parents with more granular screen time features.

Apple's OSes come with Liquid Glass tweaks and performance optimizations.