标签：#EV

I don’t know if the adoption of 7.0 is just too low for this to become widespread or if I’m just a weirdo and most people don’t use their arrow keys to navigate the editor but this used to work perfectly and is now completely broken. Before 7.0 if you had a set of nested blocks, let’s say a group block with columns and then heading, paragraphs, images etc in the individual columns, you could use your arrow keys to move through the layers. For example if I was in the heading tag at the top of a column and clicked the up arrow it would make the column block the active selected block. Press it again and you’re on the columns container. One more time and you’re at your parent group. Now if I’m in that exact same scenario and click up from the heading block it will jump me to the lowest nested child block of the next highest root level block or if I’m already in the highest root level block it will take me to the page title. There is absolutely no way to use the keyboard to navigate between layout block layers anymore and it’s infuriating. This functionality is so engrained into my brain that it’s muscle memory at this point and I keep flying all over the page when I just want to adjust my column gaps or something. Forcing me to point and click around to the breadcrumbs or expanding the document overview sidebar is such a pain and takes so many steps. I have to imagine this is also absolutely horrible for accessibility, not being able to even get to certain blocks without a mouse. I just have no clue why they would change something that was so logical and just worked exactly as expected since the inception of the block editor. Was this just a mistake or did someone intentionally do something this stupid? I truly can’t see any value to how the keyboard navigation works now and see no point in why someone would choose for it to behave this way over the old way. Is there something I’m missing? Am I just a stubborn old developer who hates change? I feel like this is not unre

i'm having a hard time deciding which approach i should implement. i'm developing a Laravel api which is consumed by Vue & Nuxt and i didn't noticed that i actually implemented two approaches of the returned response: [1] return ArticleResource::collection($articles); this returns a JSON like this: { "id": 1, "title": "My Article" } [2] return response()->json([ 'data' => new ArticleResource($article), 'success' => true, 'message' => 'OK', ]); JSON: { "data": { // output of ArticleResource transformed $article }, "success": true, "message": "OK" } considering that the API and frontend are private repositories. does wrapping all of the response inside 'data' makes sense or should i just stick on [1] for less nesting? what do you guys think what do you usually do with your years of experience? submitted by /u/Totoro-Caelum [link] [留言]

[ Removed by Reddit on account of violating the content policy . ] submitted by /u/allenaa3 [link] [留言]

Yesterday, I read a blog by Shubham on how Generative UI is changing entire frontend space. The frontend has always been something you build ahead of time and ship. The agent just works inside it. For 30 years that was the deal. What's actually shifting: the interfaces shipping in 2026 are drawn partly by the agent itself, in real time, from what the user actually asked for. He breaks down the different approaches (A2UI, MCP Apps, AG-UI) via CopilotKit and where each one actually falls apart depending on how deep in the app you go.. along with the token tax - how typical tool description with its JSON schema runs around 400 tokens. 25 components are 10,000 tokens on every turn, you pay that tax per request. worth a read especially the declarative generative UI pattern where you hand the agent a catalog and let it assemble layouts you didn't pre-build. blog link: https://x.com/Saboo_Shubham_/status/2062220865643982875 repo: https://github.com/CopilotKit/copilotkit do you think it's just marketing hype or actually the future of UI? submitted by /u/allenaa3 [link] [留言]

Apple's App Store generated $1.4 trillion in sales, up from $1.3 trillion last year, with $149 billion in sales for digital goods.

Uber introduced a high-throughput financial ledger processing system designed to handle hot account write contention at scale. Using 250ms batching, Redis coordination, and optimistic atomic updates, the system supports 30+ updates per second per account while preserving consistency and auditability, reducing multi-hour processing pipelines to minutes in its distributed accounting infrastructure. By Leela Kumili

Agentic AI in software development: what's actually production-ready in 2025 There's a lot of noise about AI agents right now. This post is an attempt to be precise: what is an agent architecturally, what can it actually do in a dev workflow today, and where does it still break. **What makes something an "agent" vs. a standard LLM call **A standard LLM call is stateless. You send a prompt, you get a response. No memory of previous turns (unless you manage it yourself), no external actions, no loop. An agent is a system built around an LLM that adds: Persistent memory across steps in a task Tool use - structured access to external systems (file I/O, shell execution, HTTP calls, database queries) A planning + evaluation loop - the agent generates a plan, executes a step, checks whether it succeeded, and decides next action Without all three, you don't have an agent. You have a capable model with maybe some extra context. What's actually production-ready today High confidence (use in production): Unit test generation for existing, well-documented code Boilerplate scaffolding (new modules, new endpoints, CRUD patterns) Documentation generation tied to code diffs Code migration tasks (framework upgrades, Python 2→3, ORMs) PR description generation from diffs Bug triage: given an issue, find likely affected files * Works but needs oversight: * Multi-file refactoring Dependency updates with breaking changes Writing integration tests (more surface area for wrong assumptions) Not there yet: Novel architecture decisions Debugging in unfamiliar/undocumented codebases Tasks with genuinely ambiguous requirements Long autonomous chains (>10 steps) without human checkpoints The failure modes to build around Ambiguous task specification Agents optimize for completing the task as specified. If the spec is loose, they'll complete the wrong task confidently. Be more precise with agents than you'd be with a junior engineer - there's no informal Slack thread to resolve ambiguity. Error

I am a full time trader and part time developer based in Karachi, Pakistan. A year ago I sat down to research how to properly compare brokers on the Pakistan Stock Exchange. Three hours later I had 11 browser tabs open, two of which had broken links, one had data from 2019, and none of them had everything I needed in one place. So I built PSX Pulse. What PSX Pulse Is PSX Pulse is a free stock market education platform for Pakistani retail investors. Everything a beginner needs to start investing in Pakistan's stock market — in one place. What is live right now: 35 verified SECP-licensed brokers with full contact details Complete mutual funds directory across 15 AMCs DCA calculator with realistic return scenarios 30-day beginner learning path Islamic investing guide PSX sector guide covering 12 sectors IPO tracker 100-term searchable glossary Weekly market recap every Friday All free. No login required. Live at: https://psxpulse.xwen.com.pk/ The Stack React + Tailwind CSS for the frontend. Vercel for hosting — free tier handles everything comfortably. No backend for most features — localStorage and static data keeps it fast and simple. Newsletter handled via a serverless Vercel function writing to a private GitHub CSV. What I Learned Building This Solo 1. The information gap in emerging markets is enormous Pakistani investors are not underserved because nobody cares. They are underserved because nobody with the technical skills to build tools also has the market knowledge to know what those tools should do. Being both a trader and a developer turned out to be the actual unfair advantage. 2. Free tools beat content for SEO My DCA calculator and broker directory pages get more consistent Google clicks than any article I have written. Tools solve a specific search intent that AI overviews do not replace — people still need to interact with a calculator, not just read about one. 3. Building in public is uncomfortable but worth it Sharing what you are building before it i

As a developer who frequently switches between competitive FPS titles like CS2 and Valorant, re-tuning mouse sensitivity is always a hassle. I wanted a fast, ad-free tool to translate my aim perfectly across titles, so I built a clean Game Sens Converter . The Approach I built this using 100% Vanilla JS. It’s a simple utility, so there was absolutely no need for a backend or heavy frameworks. It loads instantly and calculates right in the browser. Here is a quick look at the core logic handling the sensitivity conversion multipliers: function convertSensitivity ( gameFrom , gameTo , currentSens ) { // Standardized multipliers relative to CS2 / Source engine const multipliers = { ' cs2 ' : 1 , ' valorant ' : 3.181818 , ' overwatch ' : 0.3 , ' apex ' : 1 }; if ( ! multipliers [ gameFrom ] || ! multipliers [ gameTo ]) return null ; // Convert to base (CS2), then to the target game const baseSens = currentSens * multipliers [ gameFrom ]; const convertedSens = baseSens / multipliers [ gameTo ]; return convertedSens . toFixed ( 3 ); } Try it out You can use the live tool for free here: Game Sens Converter Let me know what your main game is or if you'd add any other FPS titles to the list in the comments!

Create a storage account and configure high availability. Create a storage account for the internal private company documents. In the portal, search for and select Storage accounts . Select + Create . Select the Resource group created in the previous lab. Set the Storage account name to private . Add an identifier to the name to ensure the name is unique. Select Review , and then Create the storage account. Wait for the storage account to deploy, and then select Go to resource . This storage requires high availability if there’s a regional outage. Read access in the secondary region is not required. Configure the appropriate level of redundancy . Explanation A storage account is like a digital locker in the cloud. Resource group is a folder that organizes related services. High availability means your files stay safe even if one region (data center area) has problems Configure Redundancy In the storage account, in the Data management section, select the Redundancy blade . Ensure Geo-redundant storage (GRS) is selected. **Refresh **the page. Review the primary and secondary location information. Save your changes. Explanation : Redundancy means keeping copies of your files in multiple places. GRS ensures your files are copied to another region for safety. Create a storage container, upload a file, and restrict access to the file. Create a private storage container for the corporate data. In the storage account, in the Data storage section, select the Containers blade. Select + Container . Ensure the Name of the container is private . Ensure the Public access level is Private (no anonymous access). As you have time, review the Advanced settings, but take the defaults. It means: don’t change anything in the Advanced settings unless the lab specifically tells you to. Azure already chooses safe, recommended defaults for you. Select Create . Explanation : A container is like a folder inside your storage account. Setting Public access level to Private means nobody can see

📌 This article was originally published on Sherdil E-Learning . I'm republishing it here so the dev.to community can benefit too. The Kubernetes vs Docker question is one of the most common sources of confusion for developers entering DevOps. People hear both names constantly, see them used together in job listings, and assume they must be competitors. They are not. Docker and Kubernetes do different jobs, and most modern infrastructure uses both. This guide explains what each tool actually does, how they fit together in a real deployment, the practical difference between Docker Compose and Kubernetes, and which one you should learn first. Docker: the container creator Docker is a tool for building, running, and managing containers . A container is a lightweight, portable package that contains an application together with its dependencies, runtime, system libraries, environment variables, and configuration files. The same container runs the same way on a laptop, a CI runner, a production server, or a cloud platform. In a typical Docker workflow you: Write a Dockerfile that describes how to build the image Run docker build to produce the image Run docker run to launch a container from it For multiple containers (a web app plus a database, for example), you use Docker Compose to define the whole set in a docker-compose.yml file and start them with one command. Docker is excellent for individual containers and small multi-container applications. The limitation is scale. What happens when you need a hundred containers across a dozen servers? When one container crashes at 3 a.m.? When you need to roll out a new version without downtime? Docker alone does not solve those problems. For the official reference, see docs.docker.com . Kubernetes: the orchestration layer above Docker Kubernetes (often shortened to K8s ) is an open-source platform that runs containers across many machines as a single coordinated system . It was originally built at Google, based on their internal

This is not an Oura Ring 5 review. That's coming later, once I've had enough time to really test the new durability and battery life claims, plus the new software updates that start rolling out today. In the meantime, I did want to provide an answer to a burning question that I've seen asked in […]

To provide SRE as a service, a team built a center of excellence, introducing Federated SREs and roles like production manager and technical tribe lead. They created a culture of data-driven conversations where SLOs and SLAs were democratised. Surviving growing cognitive load meant continuously simplifying architecture and embedding sovereignty and resilience into platform design decisions. By Ben Linders

From app-controlled security to electronic shifting and radar alerts, Segway’s Myon may have more tech than you need. That’s not always a bad thing.

Here is the situation of the website. If someone in Canada enters www.example.com they are redirected to www.example.com/ca and ALL the mention of "USA" is hidden and replaced with "Canada"! For example in Canada, instead of people seeing "Home Improvement in the USA and Canada", people in Canada just see "Home improvement in Canada", and vice versa; someone in USA and everywhere other than Canada on the globe does NOT see Canada on the website pages . My question is: Shouldn't a website have unified info and list BOTH USA and Canada, because with current situation someone accessing the homepage in Canada would NOT know that the company can also do Home improvement in the USA and vice versa . Even for AIs, I asked Chatgpt where is the company located and did NOT see Canada. P.S. The only mention of both countries is in the contact page. submitted by /u/RadiantQuests [link] [留言]

submitted by /u/enador [link] [留言]

Marketing wants an A/B landing page by Friday. Product wants to gracefully deprecate a legacy API without breaking old mobile clients. Growth wants ten thousand short links, and Ops does not want ten thousand Nginx edits. At some point, a single return 301 in your CDN stops being a configuration problem and becomes a routing product . Someone has to answer, on every HTTP request: Given this host, path, query, and method—where does this visitor go, and with which status code? I built that answer as a pipeline at LinkShift . Not a pile of special cases, but a fixed sequence of steps that runs the exact same way for real visitors and for the tools you use to test rules before rollout. Here is how I designed a deterministic redirect engine, the pipeline that powers it, and the edge cases that kept me up at night so they don't have to keep you up. Why "Usually Works" Is Not Enough A redirect engine fails quietly. The browser follows a broken 302 and nobody files a ticket. The damage shows up days later in analytics: wrong campaign, wrong locale, or an infinite loop that only appears when two rules on the same host point at each other. What I wanted early on was boring, bulletproof reliability: Same inputs → same decision. Two engineers simulating the same request against the same rule set should get the same target URL. Same resolution logic everywhere. Matching and destination resolution must not diverge between the "Test Rule" button in the dashboard and an actual click on a custom domain. Guards before cleverness. Rate limits and access checks run before anyone evaluates a ternary conditional in a destination string. Expressiveness is easy. Ordering is what saves you in production. The Pipeline, Told as a Story Picture a request hitting a hostname. Before the engine asks "which rule wins?", the request walks through a strict corridor of gates. Only then does it enter the rule loop. Gate 1: The Host Has to Exist If the hostname does not resolve to a domain or LinkShift

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Let me tell you exactly what happened the first time I tried to set up Lambda manually. Four hours. IAM trust policies I didn't fully understand, ARNs copy-pasted into the wrong fields, an API Gateway that was technically configured but somehow not routing anything correctly, and a deploy that failed with an error message pointing me nowhere useful. I hadn't written a single line of actual business logic yet. That's when someone on my team mentioned the Serverless Framework. My first reaction was honestly skepticism — another abstraction layer sounded like another thing to learn and eventually fight with. I was wrong about that. This isn't a "look how clean this tool is" post. It's more like: here's what I actually did to get a Postgres-backed CRUD API running on Lambda, step by step, including the parts that tripped me up. What the Framework Is Actually Doing Under the Hood Worth knowing before you start: the Serverless Framework isn't magic. It's generating CloudFormation templates and submitting them to AWS on your behalf. Your Lambda functions, API Gateway routes, CloudWatch log groups — all of it gets provisioned from a single config file. It works with other providers too, but the AWS integration is where it really earns its keep. The console clicking and manual ARN-wiring that burns time at the start of every serverless project? Gone. Same deploy workflow whether you're building a REST API, an event processor, or a cron job. Once you've done it once, the second project takes a fraction of the time. What You're Building Four live endpoints backed by PostgreSQL. A Users table. Create, read, update, delete — nothing exotic, but a real enough foundation that you can extend it into something actual once this guide is done. You'll need an AWS account, the AWS CLI installed, and the Serverless Framework installed before starting. That's it. Step 1: Sort Out Your AWS Credentials Run this to create both config files in one go: bash cat << EOF > ~/.aws/credentials [def

Internal note to the team, we need to improve test coverage and keep shipping, which means we should treat AI as a helper in the workflow, not as a replacement for testing discipline. AI-assisted development changes the shape of our risk. It can produce more code faster, but it also increases the chance that small logic mistakes, brittle selectors, and shallow test cases slip through review. The answer is not to add more manual checking everywhere. The answer is to be more deliberate about what we review, what we automate, and where we let AI help. What changes when AI writes part of the code The first thing that changes is review. When a developer uses AI to draft a feature, a test, or a refactor, the reviewer is no longer only checking intent and style. The reviewer also needs to check whether the generated code matches the product rule, whether it introduced a hidden dependency, and whether it quietly weakened coverage. That does not mean every AI-assisted change deserves extra ceremony. It means our review checklist should shift from "does this look correct" to "what did the model assume, and did we verify those assumptions?" That is especially important for test code, because generated tests often look plausible even when they do not prove much. Coverage should move from volume to signal AI tends to produce more test cases, but more cases are not the same as better coverage. If a generated test suite repeats the same happy path under slightly different names, the team gets a false sense of safety. Coverage should answer a more practical question, where are we most likely to break the user experience, and where will a test actually catch it? For chat and other AI features, prompt-by-prompt manual checks are a trap. They do not scale, and they encourage a habit of eyeballing output instead of verifying behavior. A better pattern is to build assertions around expected properties, create eval sets for representative prompts, and add regression coverage for failure