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Time zones are one of those topics that look simple until you ship something and a user in another country sees the wrong time. Here are the traps I keep seeing, and how to reason about them in 2026. UTC is not a time zone, and GMT is not UTC UTC (Coordinated Universal Time) is a time standard, not a region. GMT is a time zone that happens to share the same offset as UTC most of the year. For storage and math, always think in UTC. Treat GMT as just another named zone. Rule 1: store timestamps in UTC Store every instant as UTC (or an epoch value). Convert to a local zone only at the edges, when you display to a user. If you store local times, you will eventually lose the offset and never recover the true instant. Rule 2: an offset is not a zone +09:00 tells you the offset right now. It does not tell you the zone, because zones change offset across the year due to daylight saving time. Store the IANA zone name (like America/New_York ), not just the offset. The offset is derived from the zone plus the date. Rule 3: DST is where it hurts The same wall-clock time can happen twice (fall back) or never (spring forward). Scheduling "9am every day" is a zone-aware operation, not an offset-aware one. Libraries like the built-in Intl.DateTimeFormat and Temporal (now widely available) handle this correctly if you give them a zone name. new Intl . DateTimeFormat ( ' en-US ' , { timeZone : ' Asia/Tokyo ' , dateStyle : ' short ' , timeStyle : ' short ' , }). format ( new Date ()); Rule 4: scheduling across teams is an overlap problem For a distributed team, the useful question is not "what time is it there" but "when do our working hours overlap". That is a set-intersection over each person's 9-to-5 expressed in UTC. A tool for the human side When I just need to eyeball overlaps and pick a meeting time without writing code, I use the free tool I built: ZonePlan , a time zone meeting planner and live world clock. If you want the practical playbook for picking meeting times, I wrote

One of the biggest realizations I've had over the last year wasn't about software. It was about focus. When I first started building KiwiEngine, I wanted it to power everything. Business software. CRMs. Inventory systems. Scheduling platforms. Accounting tools. SaaS products. If someone could build it, I wanted KiwiEngine to support it. Technically, I still do. But something changed. I realized there is a difference between building software that can solve every problem and trying to solve every problem yourself. Those aren't the same thing. The Architecture Never Changed KiwiEngine is still designed to power business applications. Nothing about the architecture changed. The modules. The APIs. The philosophy. The engine remains general-purpose. What changed was my focus. Build What You Understand I started asking myself a simple question. Who do I actually understand? Not as a developer. As a creator. The answer wasn't accountants. It wasn't HR departments. It wasn't inventory managers. The answer was musicians. Artists. Game developers. Creators. Builders. Those are the people whose problems I experience every day. Those are the workflows I naturally understand. Open Source Changes The Equation One of the beautiful things about open source is that I don't have to build every application. I can build the engine. I can document it. I can share the philosophy. Someone else can build the CRM. Someone else can build the scheduling platform. Someone else can build the accounting software. Meanwhile, I can focus on building the creative tools I genuinely want to use. The Best Proving Ground Today, KiwiEngine's proving ground is becoming: Artist websites EPKs Music production tools Digital storefronts Creative workflows Game development Media platforms Not because they're the only things KiwiEngine can build. Because they're the things I care deeply enough to refine every day. And I think that creates better software than chasing every possible market ever could.

Hey DEV community! 👋 I've been building web apps for a while, and I noticed there was no good place to discover and rate web-based OS projects — those cool browser-based operating systems you can run without installing anything. So I built Web OS Community 🎉 What is it? A platform where you can: 🔍 Browse web-based OS projects (Windows XP, Ubuntu, macOS clones, and more) ⭐ Rate your favorites with a global rating system 🏷️ Filter by tags (webos, demo, linux, macos, windows...) 📤 Submit your own web OS project via GitHub PR Why I built this The existing resources were scattered — some projects on GitHub, some on random personal pages. I wanted a central hub where developers could showcase their work and users could find these cool experiments easily. Tech stack Frontend: Vanilla JS / HTML / CSS (no frameworks, keeping it lightweight) Backend: Supabase (PostgreSQL + RLS policies) Auth: Custom RPC-based authentication Hosting: GitHub Pages + Cloudflare Workers Some features Global rating system (one vote per user per project) Admin panel for managing submissions Tag-based filtering Dark mode UI 🌙 Check it out! 🔗 web-os-community.tfhy5321.workers.dev If you have a web OS project, feel free to submit it! PRs are welcome 🙌 What web-based OS have you seen that blew your mind? Drop it in the comments!

I've been using Home Assistant for a few years and Trading212 for longer than that. It was inevitable these two things would end up connected. The Trading212 API is surprisingly good — portfolio value, individual positions, pies, dividends, all there. So I wrote a custom integration to pull it all into HA as sensors, then a Lovelace card to make it actually look decent on a dashboard rather than a wall of entity rows. The card does zero-config auto-discovery which was the bit I spent the most time on. You drop it on a dashboard and it finds your sensors automatically — no copying entity IDs, no manual config unless you want it. Five card types: portfolio overview with a sparkline, scrollable positions list, pies with goal progress, and a combined one if you want everything in one card. The sparkline was fiddly. HA's recorder only writes state changes, not regular samples, so if your portfolio value is flat between polls the chart has gaps. Had to smooth over those client-side. The part I use most though is the automations. Every weekday at 8am Alexa tells me where I stand: action : - action : notify.alexa_media_kitchen data : message : > Portfolio is worth {{ states('sensor.trading212_total_value') | float | round(0) | int }} pounds. Today you are {% if states('sensor.trading212_pnl_today') | float >= 0 %}up{% else %}down{% endif %} {{ states('sensor.trading212_pnl_today') | float | abs | round(2) }} pounds. data : type : tts And Friday at 6pm I get the weekly version with P&L for the week and which position moved the most. I like that it just tells me — if the market's had a bad week I'd probably avoid opening the app, but Alexa doesn't give me the option to ignore it. Both the integration and the card are on GitHub. The card is in HACS as a custom repo while it waits for default catalogue approval: https://github.com/Smart-Home-Assistant-UK/lovelace-trading212-card I wrote up the full setup with all the automation YAML here if you want to copy the whole thing: ful

In my journey as a backend developer, I had already spent time working with APIs, databases, authentication flows, and background processing. I understood the basic idea that not everything should occur within a request-response cycle, especially when dealing with expensive operations such as sending emails, processing files, or generating reports. Offloading work to the background felt like a solved problem to me. That confidence was exactly what led me into confusion. When I first encountered message brokers and task queues, they looked like different names for the same idea. Both involved queues, both involved workers, and both involved asynchronous processing. In my head, the distinction didn’t seem important, so I treated them interchangeably and assumed that choosing one over the other was just a matter of preference or framework availability. The real issue was that I had not yet understood the difference in intent between communication and execution. What I thought was a simple design choice actually turned into an architectural mistake that affected how I structured an entire system. How I Misunderstood the Problem At the time, I was building systems where the backend had to handle multiple heavy operations. A user could upload files, request reports, or trigger processes that should not block the main API response. Naturally, I reached for a queue-based solution because it is the standard answer for background work. However, instead of asking what role the system needed to play, I focused on what tool could make things asynchronous. That small shift in thinking created the confusion. I assumed that anything that gets delayed or processed later should automatically go into a queue, without distinguishing whether I was dealing with a job that must be executed or an event that other services should react to. This is where I started building the wrong abstraction. Where Task Queues Actually Fit A task queue exists primarily to assign work that must be complete

The problem: I kept missing big moves on Polymarket because I had no way to see what the biggest traders were betting on in real time. So I built WhaleTrack — a free, no-signup tool that shows you exactly what top Polymarket whales are buying and selling. What it does Live whale activity feed — see the last 40 trades from top wallets, updated on refresh Whale leaderboard — P&L, win rate, trade count for the biggest accounts No login, no ads, no fluff — just the data How it works The whole thing is vanilla HTML/CSS/JS deployed on Vercel with two serverless functions: /api/whales.js — hits the Polymarket leaderboard API, fetches position stats for each whale, calculates win rates from closed positions /api/activity.js — pulls recent trades for each whale wallet in parallel, filters out internal combo transactions (no title / zero price), and returns the 40 most recent trades The serverless layer solves CORS — Polymarket's data API doesn't allow browser requests, so everything goes server-side. Tech stack Frontend: Vanilla HTML/CSS/JS (zero dependencies) Backend: Vercel serverless functions Data: Polymarket public data API Deploy: Vercel (free tier) Biggest lesson Filtering bad data is half the work. The raw API returns combo trades and internal transactions that show up as "Unknown Market @ 0¢" — useless noise. Had to figure out which fields to check (title, price > 0) to strip them. Also: win rate calculation is tricky when most whales have unrealized profits. Showing "—" instead of 0% is more honest. Try it WhaleTrack → Also launched on Product Hunt today if you want to show some love: Product Hunt Built this in a weekend. Happy to answer questions about the Polymarket API or Vercel serverless setup.

For a long time, I hesitated to share my work. I kept telling myself: "If I post a simple hero section, a basic Bootstrap grid, or a landing page clone, people will judge me. They’ll think I’m not a 'real' developer yet." But today, I saw a video of a developer who built a complete Netflix clone using only HTML & CSS in just 4 hours https://x.com/Aditwariii/status/1681403710457643009?s=20 . It made me stop and think. It’s easy to get so obsessed with complex frameworks, cloud architectures, and database optimizations that we begin to look down on the fundamentals. But here is the psychology of software engineering that we often ignore: Every master was once a beginner: The engineers managing complex distributed systems today started exactly where we are—struggling to center a div and fighting with CSS media queries. Shipping beats hiding: Building a clean, responsive interface in 4 hours shows speed, focus, and attention to detail. Those are core professional hygiene habits. Code is for humans, not just machines: Before we write APIs or database queries, we must master how a human being actually interacts with our interface. I’m letting go of the fear of being judged for "simple" things. From now on, I am building in public. Whether it’s a massive full-stack application or just a beautifully aligned hero section, it is proof of active practice and continuous momentum. Massive respect to [ https://x.com/Aditwariii?s=20 Check out Aditya Tiwari on X. POLYMATH 🧑‍💻 sde @IEX_INDIA_ ] for the inspiration and the reminder to keep shipping! 👇 What is a "small" project or layout you built recently that taught you a major lesson? Let's connect in the comments.

How we handle complex EPUB structures for AI translation without breaking navigation and metadata At LectuLibre , we built an AI‑powered book translation service. Users upload an EPUB, and our pipeline translates the text using LLMs like Claude and DeepSeek. That sounds straightforward until you have to parse and rebuild a valid EPUB without mangling the table of contents, internal links, or styles. I’m sharing the real‑world challenge we faced, how we chose our tooling, and the ugly corners we discovered when dealing with real‑world EPUB files. The Problem: EPUB is a Messy Zip File An EPUB is essentially a ZIP archive containing XHTML, CSS, images, and an OPF manifest. It’s a well‑defined standard (EPUB 3.2), but in practice publishers produce files that bend the rules: missing container.xml , inline styles that break after translation, and structural quirks that make parsing fragile. Our translation process needed to: Accept any EPUB the user throws at us. Extract all text content while preserving the exact structure. Send each paragraph to an LLM for translation. Re‑insert the translated text into the original XHTML files. Repackage everything into a new, valid EPUB. Step 4 is the tricky part: the translated text can be longer or shorter, it may contain characters that need escaping, and the surrounding markup must remain intact. Our Approach: Use ebooklib with a Dose of Defensive Coding We evaluated several Python libraries: epub (pypub) – too simple, no editing support. lxml + manual zip – too much boilerplate. ebooklib – full read/write with a clean API. We went with ebooklib . It provides an object‑oriented model of the EPUB structure, allows us to iterate over documents, and can write a new EPUB from the modified objects. The downside: its documentation is sparse and it can choke on malformed files. We had to layer on a lot of validation. Step 1: Loading and Validating the EPUB import ebooklib from ebooklib import epub def load_epub ( epub_path : str ) -> ep

Java 26 was officially released in March 2026, and after spending the past three months exploring its new features, experimenting with preview APIs, and using it in personal projects, I think it's a good time to share my impressions. Unlike launch-day articles that simply list every new feature, this is a practical look at what actually stood out to me after having some time to work with Java 26. Some improvements are immediately useful, while others feel like building blocks for the future of the language. Java continues its predictable six-month release cycle, and Java 26 is another example of gradual, thoughtful evolution rather than dramatic change. In this article, I'll cover the features I found most interesting, what I like, what I probably won't use right away, and whether I think Java 26 is worth upgrading to. Why Upgrade to Java 26? Every Java release makes the platform: Faster More secure Easier to write Better for cloud applications Even if you don't immediately use every new feature, upgrading allows you to benefit from JVM optimizations and improved tooling. 1. Better Performance Java 26 continues improving the JVM with optimizations for: Faster startup Better garbage collection Reduced memory usage Improved JIT compilation Most applications will benefit automatically without changing a single line of code. 2. Improved Pattern Matching Pattern matching keeps becoming more powerful. Instead of writing: if ( obj instanceof String ) { String text = ( String ) obj ; System . out . println ( text . length ()); } You can simply write: if ( obj instanceof String text ) { System . out . println ( text . length ()); } Cleaner code with less casting. 3. Record Improvements Records remain one of Java's best additions for immutable data. public record User ( Long id , String name , String email ) {} Instead of writing dozens of lines containing: constructor getters equals() hashCode() toString() Java generates them automatically. 4. Better String Templates (Previe

Most guides to OTP testing in Playwright include a function that looks something like this: function extractOtp ( emailBody : string ): string { const patterns = [ / \b(\d{6})\b / , /code [ : \s] + (\d{4,8}) /i , /verification [ : \s] + (\d{4,8}) /i , /OTP [ : \s] + (\d{4,8}) /i , ]; for ( const pattern of patterns ) { const match = emailBody . match ( pattern ); if ( match ) return match [ 1 ]; } throw new Error ( ' OTP not found in email body ' ); } This function is fragile. It breaks when the email template changes. It returns false positives when the email body contains order IDs or timestamps. It requires you to maintain regex patterns for every email provider your app might use. There is a better way. The Problem with Regex OTP Extraction When your app sends a verification email, the OTP is buried somewhere in the HTML body. To extract it you need to: Fetch the raw email body Parse HTML or plain text Apply regex patterns that match your specific email format Handle edge cases — 4-digit vs 6-digit codes, codes in tables, codes in buttons Every time your email provider changes their template, your regex breaks. Every time you add a new auth provider, you write new patterns. It is maintenance overhead that compounds forever. The right place to extract the OTP is at the infrastructure layer — before the email even reaches your test suite. How ZeroDrop Extracts OTPs at the Edge ZeroDrop catches emails at Cloudflare's edge before storing them. When an email arrives, the worker runs OTP detection on the body and stores the result as a structured field alongside the raw email. By the time your test calls waitForLatest() , the OTP is already extracted and sitting in email.otp . No regex. No HTML parsing. No maintenance. const email = await mail . waitForLatest ( inbox ); email . otp // "847291" — already extracted Setup npm install zerodrop-client No API key. No signup. No environment variables. Basic OTP Test import { test , expect } from ' @playwright/test ' ; import

TMX: The open standard AI agent memory has been waiting for The problem no one talks about: your agent's memories are prisoners. If you build an AI agent today using Mem0, your memories are locked in Mem0. Switch to Zep? You lose everything. Move to a new framework? Start from zero. This is exactly the problem email had in 1970. Every system had its own format. You couldn't send an email from one system to another. Then SMTP was invented. And email became universal. Today I'm publishing TMX v0.1 — the SMTP of AI agent memory. What is TMX? TMX (Truvem Memory eXchange) is an open, model-agnostic JSON format for storing, exporting, and importing AI agent memories across any platform, framework, or provider. It looks like this: { "tmx_version" : "0.1" , "exported_at" : "2026-06-26T20:00:00Z" , "source" : "truvem" , "agent_id" : "my-agent" , "memories" : [ { "id" : "550e8400-e29b-41d4-a716-446655440000" , "content" : "User prefers dark mode and concise responses" , "created_at" : "2026-06-01T08:30:00Z" , "updated_at" : "2026-06-01T08:30:00Z" , "expires_at" : null , "tags" : [ "preference" , "ui" ], "source_model" : "gpt-4o" , "metadata" : {} } ] } That's it. Plain JSON. Human-readable. Portable. Why this matters Right now, the AI agent ecosystem is exploding. Every week there's a new memory provider, a new framework, a new cloud service. But every one of them uses a proprietary format. This means: Developers are locked to their first choice forever Agent memories can't travel between clouds Switching providers = losing everything your agent learned This is the biggest hidden tax in the agentic AI stack. TMX fixes it with a single open spec that anyone can implement — for free, with no approval needed. The 5 core principles 1. Open — No license required. Implement TMX in any product, commercial or otherwise. 2. Model-agnostic — Works with GPT-4, Claude, Gemini, Mistral, Llama, or any future model. 3. Framework-agnostic — LangChain, CrewAI, Mastra, AutoGen — doesn't matter

Everyone is selling AI to small businesses right now. Most of it is hype. But some of it is genuinely useful — and knowing the difference can save you thousands in wasted tooling. I run a small agency in Stuttgart that builds websites and automations for local service businesses: coaches, doctors, beauty studios, consultants. Here's what actually moves the needle for them in 2025. What "AI Automation" Actually Means for Small Businesses Forget the generic pitch. For a local service business, AI automation is useful in exactly three places: Client communication at scale — responding to inquiries 24/7 without hiring a receptionist Reducing admin time — intake forms, follow-ups, reminders, invoicing triggers Content creation — but only as a speed boost, not a replacement for your voice Anything beyond that is usually overkill for a business under 10 employees. The One Automation Every Service Business Should Have Automated follow-up after initial contact. Here's the typical flow without automation: Client fills out contact form You see it 4 hours later You write a reply If you're busy, it takes a day Client has already booked elsewhere With automation: Client fills out form Immediate confirmation email ("Got your message, here's how to book a slot") Link to booking calendar You're notified. If they don't book in 48h, a follow-up email goes out automatically This alone converts 20-40% more inquiries into booked clients. No AI model needed — just a simple workflow in n8n, Make, or Zapier. Where LLMs Actually Help Language models (ChatGPT, Claude, etc.) are genuinely useful for small businesses in these areas: Intake Forms → Personalized Responses A coaching client fills out a detailed intake form. Normally, you'd spend 20 minutes reading it and writing a personalized welcome email. With a simple LLM integration: Intake form submitted Webhook fires to n8n LLM reads the form, generates a personalized summary + welcome You review it in 30 seconds and hit send Same personal

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. An AI model writes a paragraph. It sounds fluent. It looks convincing. But how do you know whether it's actually good? This deceptively simple question has occupied researchers for more than two decades. Long before ChatGPT, machine translation researchers faced exactly the same problem. Human evaluation was expensive, inconsistent, and painfully slow. If every new model required thousands of humans to compare translations, research would crawl. That necessity gave rise to BLEU , one of the most influential evaluation metrics in AI history. Years later, as language models became better at paraphrasing and reasoning, BLEU started to show its age. Researchers responded with learned metrics like BLEURT and COMET , which use neural networks to judge language much more like humans do. Interestingly, this mirrors software engineering itself. We first wrote simple unit tests, then integration tests, and today we increasingly rely on sophisticated observability systems. Evaluation metrics for LLMs have undergone a similar evolution. Let's see why. Before BLEU: The Evaluation Bottleneck Imagine you're building Google Translate in 2001. Every time your team improves the model, someone has to read thousands of translated sentences and score them. Suppose a single sentence pair takes only 20 seconds to judge. Evaluating 50,000 sentences would require nearly 280 human-hours . Now imagine dozens of experiments every week. Evaluation—not training—quickly becomes the bottleneck. Researchers at IBM, led by Kishore Papineni , introduced BLEU (Bilingual Evaluation Understudy) in 2002 to automate this process. Their idea was surprisingly simple: If a machine translation resembles what professional translators write, it's probably good. This became one of the most cited papers

Originally published at malaymehta.com The Interview Trap If you look at most system design tutorials, you get an extreme use case. Design Twitter. Design YouTube. Scale it to a billion users. Draw boxes on a whiteboard for 45 minutes. Do you think your app will be used by a billion users on day one? The answer is almost always no. But the tutorials don't teach you what to do when you have 500 users, unclear requirements, a team of four, and a quarter to ship something that works. Real system design is nothing like a whiteboard interview. You don't get clean requirements, you don't design from scratch, and nobody asks you to handle a billion requests per second on day one. Real System Design Starts with Questions, Not Diagrams The very first thing that matters in system design is something most tutorials skip entirely: unclear and chaotic requirements. In the real world, requirements don't come as a clean problem statement. They come from non-technical business teams, and you need to navigate through cross-questions to get all the clarity you need. Ask as many questions as possible. Understand your functional and non-functional requirements. Which features need to be synchronous and which can be async? What are the read and write load patterns? What is the maximum and average number of concurrent users right now? What does authentication look like? Do you need role-based access control? These questions drive your choices. You don't always need an axe where a knife will do. Being minimalist with a reasonable growth prediction and a 3, 6, 9 month plan will take you in the right direction. There will be things the situation demands immediately but would take more time than expected. Taking a predictable hit now and fixing it at the right future time without missing that balance is truly important. Weighing what will be expensive to change later, in terms of dollar cost or human effort, is how real architectural decisions get made. Pushing Back on Bad Requirements Many

Hey everyone 👋 Is anyone here playing RF Online Next? I recently built a fan guide website for it: 👉 https://rf-online-next.net RF Online Next Guide — Starter Finder & Beginner Tips New to RF Online Next? Answer 3 questions to get your starter Biosuit, faction lean, and first-day checklist — personalized for your playstyle. rfonlinenextguide.com The idea is pretty simple. When a new MMO launches, information is usually all over the place — Discord messages, random posts, outdated guides, fake code pages, and long videos when you only need one quick answer. So I wanted to make a cleaner guide hub for players who just want to know: how to download and play which faction to pick what Biosuits/classes are good whether there are any real codes how to fix server full/login issues how Mining War / Chip War works what Sacred Weapons do The site focuses a lot on Mining War, the big 450-player faction war between Bellato, Cora, and Accretia. I also tried to keep the content honest. For example, the codes page doesn’t list fake “working codes” just for clicks. If there are no confirmed codes, it says that clearly. From the dev side, I structured the site around search intent instead of a normal blog feed. So the homepage points players directly to the guide they probably need. It also has multilingual sections for different regions, since RF Online Next has players from many countries. Would love to hear feedback from other devs, especially on: site structure SEO approach guide layout content clarity anything that feels confusing If you’re into MMOs, gaming websites, or niche SEO projects, feel free to check it out: 👉 https://rf-online-next.net RF Online Next Guide — Starter Finder & Beginner Tips New to RF Online Next? Answer 3 questions to get your starter Biosuit, faction lean, and first-day checklist — personalized for your playstyle. rfonlinenextguide.com

Instagram/WhatsApp Stories have a signature UI: those segmented bars across the top, one filling at a time. It looks fancy but it's a simple pattern. Here's a live, tappable rebuild in vanilla JS + CSS. 📸 Try it (tap left/right, hold to pause): https://dev48v.infy.uk/design/day17-instagram-stories.html The segmented bar One bar per story. The rule: only the active segment animates its width 0→100%; segments before it are full, segments after are empty. When the active one completes, advance to the next and reset the rule. Driving the fill A single requestAnimationFrame loop tracks elapsed time vs the per-story duration (~4s) and sets the active bar's width. On completion → next story. The interactions that sell it Tap the right half = next, left half = previous (split the screen into two zones). Press-and-hold = pause ( pointerdown pauses the timer, pointerup resumes) — so users can actually read. Reset past/future segment states whenever you jump. Why rAF over CSS animation A timer loop makes pause/resume and tap-to-skip trivial — you control the clock. Pure CSS animations are harder to interrupt mid-fill. 🔨 Full build (segments → animate active → advance → tap zones → hold-to-pause) on the page: https://dev48v.infy.uk/design/day17-instagram-stories.html Part of DesignFromZero. 🌐 https://dev48v.infy.uk

Check how on-premises AI solutions empower enterprises to safeguard sensitive code, ensure data residency, and maintain full compliance without compromising performance. Why privacy and security matter in AI-powered development? As enterprises increasingly adopt AI to automate code reviews, testing, and vulnerability scanning, ensuring data privacy becomes paramount. Cloud-based AI tools may expose sensitive source code, customer data, or intellectual property to external risks. By contrast, on-premise AI tools allow organizations to keep data within their controlled environments by aligning with data sovereignty and compliance requirements like GDPR and CCPA. According to Gartner, by 2026, 75% of organizations will demand AI solutions that guarantee strong data residency and compliance assurances. What are on-premise AI tools for software development On-premise AI tools are artificial intelligence solutions that are deployed and operated within an organization’s own infrastructure, rather than relying on external cloud services. In the context of software development, on-premise AI allows teams to leverage advanced AI capabilities such as code analysis, automated testing, and security scanning while keeping all data and processes within their own controlled environment. Core components of on-premise AI infrastructure include: Hardware: servers, GPUs, and storage devices physically located on-site or in a private data center. Software: AI models, orchestration tools, and management platforms installed and maintained by the organization. Security Measures: firewalls, access controls, and monitoring systems tailored to the organization’s specific needs. Examples of on-premise AI tools in software development: AI-powered code review platforms installed on internal servers automated vulnerability scanners running within the company’s network machine learning models for test automation, hosted locally. Primary connection to data privacy: on-premise AI ensures that sensit

Deixa eu adivinhar. Você tá com um projeto Laravel rodando, tem uns 5, 10, talvez 15 formulários que recebem CPF. Cadastro de cliente, cadastro de fornecedor, atualização de perfil, checkout, área administrativa… e em cada um desses lugares tem aquela mesma lógica de validação de CPF. Copiada. Colada. Com pequenas variações. E tá tudo bem. Até o dia em que o cliente pede pra mudar uma regra. Ou um bug aparece em um formulário e funciona normal no outro. Aí você abre o projeto, dá um Ctrl+Shift+F procurando "cpf" e… surpresa: tem oito lugares diferentes com a mesma validação. Com mensagens de erro escritas de oito jeitos. Uma delas até com erro de digitação. Já passou por isso? Então senta que essa conversa é pra você. O crime acontecendo em câmera lenta Olha esse cenário aqui, que eu garanto que você já viu (ou escreveu): // app/Http/Requests/StoreClienteRequest.php public function rules () { return [ 'cpf' => [ 'required' , function ( $attribute , $value , $fail ) { $cpf = preg_replace ( '/[^0-9]/' , '' , $value ); if ( strlen ( $cpf ) !== 11 ) { $fail ( 'CPF inválido.' ); return ; } // ... mais 20 linhas do algoritmo }], ]; } E aí, três dias depois, no outro Form Request: // app/Http/Requests/StoreFornecedorRequest.php public function rules () { return [ 'cpf' => [ 'required' , function ( $attribute , $value , $fail ) { $cpf = preg_replace ( '/[^0-9]/' , '' , $value ); if ( strlen ( $cpf ) !== 11 ) { $fail ( 'O CPF informado não é válido!' ); // mensagem diferente, claro return ; } // ... mais 20 linhas quase iguais, mas não exatamente }], ]; } Multiplica isso por 8 telas. Agora imagina o seu "eu do futuro" tentando manter isso. Dá pra sentir a dor daqui. DRY: a sigla que vai salvar seu projeto (e sua sanidade) DRY significa Don't Repeat Yourself . Em bom português: não se repita, caramba. A ideia é simples: cada pedaço de conhecimento (uma regra de negócio, um cálculo, uma validação) deve existir em um único lugar no seu sistema. Se precisar mudar, você muda em u

I run Vite on almost everything. Astro sites, Nuxt projects, a small group of libraries I maintain on the side. The build tool is the part of the stack I think about least, because it just works. So when the thing under all of that changes twice in three months, I read the release notes properly. Here is what actually changed, what breaks, and the part that made developers argue for a week straight. For Five Years, Vite Ran on Two Bundlers When Vite launched, it made a pragmatic bet. esbuild for the dev server, because it is fast. Rollup for production, because its output is well optimized. Two tools, two jobs. It worked. But it had a cost. Two bundlers meant two configs, two sets of quirks, and output that could drift between dev and prod. You tuned one, and the other behaved slightly differently. Vite 8 ends the split. It shipped on March 12 with a single bundler called Rolldown, written in Rust, with the Rollup plugin API on top. Under Rolldown sits Oxc, a Rust parser and transformer that does the TypeScript and JSX work Babel used to do. One language. One pipeline. Dev and prod finally agree. This Is a Pattern, Not a One-Off esbuild (Go) made webpack look slow. Bun did the same to Node for some workloads. Biome replaced Prettier and ESLint and runs many times faster. Now Rolldown does it to Rollup and esbuild at the same time. Every time a core JavaScript tool gets rewritten in a compiled language, the same thing happens. The speed jump is large enough to make the old version look broken. The interesting part is not the speed. It is the compatibility. These Rust tools do not ask you to relearn your stack. Rolldown speaks the Rollup plugin API. Biome follows ESLint and Prettier conventions. The migration is designed to be boring, and boring is the point. The Numbers, With a Grain of Salt The headline figure is real. Linear cut its production build from 46 seconds to 6 . Vite reports builds 10 to 30 times faster than the old Rollup path. Other large projects repor

Making of Aantraa aantraa.site — AI audio & video translation, caption generator, and viral shorts cutter. Under the Hood I run a small YouTube channel. I'm not a full-time content creator, but YouTube is a solid platform to gain traffic for your online work, business, project, or idea. Aantraa is what I built in a week. The main concept is simple: Video translation into multiple languages Audio translation — including text-to-audio, with MP3 output for Premiere Pro Long-form to shorts — convert YouTube long-form video into short clips At that time, only three features were needed, so website development wasn't the heavy lift. The real work was building APIs, backend infrastructure to integrate AI into video, and dealing with heavy storage. Breaking the execution into steps: How I made Aantraa AI LLM layering and provider Aantraa is heavily dependent on AI APIs — we need reliable infrastructure for LLM providers. OpenRouter, Portkey, Vercel AI SDK labs, and individual APIs for Anthropic, Deepseek, and OpenAI are solid options. I prefer OpenRouter for Aantraa for one reason: multiple model support — it's easy to pick the cheapest capable model for each job. Easy to integrate, strong community support, free model access, and more. AI LLM APIs are needed at almost every stage in the backend: Understanding video context and creating a script Translating the script into target languages Recording the script into MP3 or WAV format Summarising the video Generating captions Cutting videos into shorts Building APIs and servers Each layer needs heavy AI context and prompt engineering. Loop engineering is the trend here — and it's required for aantraa. For example, video translation works in multiple connected steps: Video translation API breakdown AI understands the video, fed into the LLM via the ffmpeg module AI generates a script/caption from the video AI translates the script into the desired language AI generates audio (MP3 or WAV) of the new translation AI glues audio a