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you've felt it. you type a prompt, hit send, and the response starts streaming in under a second....

Dealing with vendor invoices usually means someone is sitting there manually opening PDFs and typing...

Aprenda a substituir o velho Cron por systemd timers para ganhar logs nativos, controle de dependências e maior robustez.

Like most developers, I have a set of tools I use every day. Most of them work great, and I don't...

Angular has signals now - and as of 19.2, even a signal-based way to fetch: httpResource, built on...

Last night I was deep in a build session with an AI assistant. We picked it back up tonight. At some point I mentioned it had been a day and a half since we last spoke — and the model had no idea. None. As far as it knew, it was still the previous session. The gap was invisible to it. That tiny moment is one of the most underrated problems in AI systems right now. So let's talk about it. The model doesn't know what time it is An LLM gets a rough sense of "now" at the start of a conversation — a single timestamp, handed to it once. That's why it can greet you with "good morning." But that stamp is frozen. It doesn't update as the conversation runs, and it definitely doesn't travel into the next conversation. Each session starts cold. On its own, that's a curiosity. It becomes a real problem the moment the model reasons over retrieved context — search results, documents, database rows, another agent's output. Staleness is invisible Here's the dangerous part. When a model reads a retrieved document, that document usually carries no trustworthy signal about when it was true . So the model treats it as present-tense. It produces a confident answer from six-month-old data with nothing flagging that the data is old. A few places this bites: Pricing — quoting a number that changed last quarter. Availability — "in stock" from a cached page. Compliance — citing a policy that was superseded. People — stating someone's job title from two years ago. For a human reader, a slightly stale search result is fine — you see the date and judge for yourself. For an LLM, the staleness is silent. The wrong answer looks exactly like a right one. Why "just add a clock" doesn't fix it The instinct is: give the model the current time. But knowing it's 9 PM doesn't help if the document you're citing went stale in 2023 and nothing told you. The missing piece isn't the model's clock — it's the context's freshness . Two different things: What time is it now? — easy, a now() call solves it. How old

Have you ever wondered how apps like university portals know which courses a student is enrolled in, or how they pull up an instructor's full schedule in seconds? The answer lies in database relationships - one of the most important concepts in backend development. In this article, we'll explore: What database relationships are and why they matter The three types of relationships: One-to-One, One-to-Many, and Many-to-Many How relationship schemas work (primary keys, foreign keys) How SQL Joins let you pull connected data from multiple tables To keep things grounded, we'll use one running example throughout: a University Management System . By the end, you won't just understand the theory, you'll see exactly how these concepts connect in a real-world scenario. What Are Database Relationships? A database relationship defines how data in one table connects to data in another. Instead of storing the same information repeatedly, relational databases organize data into separate tables and link them using keys . Think about our university system. We have a table for students and another for courses . A student can enroll in multiple courses, and each course can have many students. Rather than storing a student's full details on every course record, we store the student's info once and create a relationship between the two tables. This keeps data clean, reduces duplication, and makes updates easy. If a student's email changes? Update it in one place - done. Here's a simple visual of what that looks like: +------------------+ +------------------+ | Students | | Courses | +------------------+ +------------------+ | student_id (PK) | | course_id (PK) | | name | | title | | email | | credits | +------------------+ +------------------+ \ / \ / \ / Enrollments (links students ↔ courses) Now let's look at the three types of relationships you'll encounter. Types of Database Relationships 1. One-to-One (1:1) Each record in Table A matches exactly one record in Table B and vice versa

Most of the web's foundational moments have vanished. The servers were unplugged, the code was lost, the pages 404'd into history. But the first website ever published is a striking exception: you can still read it today, more or less as it appeared when it went live on August 6, 1991. It is a plain, text-only page with a white background and blue hyperlinks, and it explains a brand-new idea called the World Wide Web. One page that described itself The author was Tim Berners-Lee, a British computer scientist working at CERN, the particle physics laboratory near Geneva. By the end of 1990 he had quietly assembled the three technologies that still define the web: HTML for writing pages, HTTP for moving them between machines, and the URL for addressing any document on any server. The first website, hosted at the address info.cern.ch , was the web explaining itself - what hypertext was, how to browse it, and how to make your own pages. It ran on a NeXT computer, the sleek black workstation designed by Steve Jobs's company during his years away from Apple. That single machine was the entire World Wide Web for a while. A handwritten label was stuck to its case: "This machine is a server. DO NOT POWER IT DOWN!!" One unplugged cable would have taken the whole web offline. Why a 1991 web page still matters to IoT It is easy to file this under nostalgia, but the first website is more than a museum piece. It is the origin point of the request-and-response model that quietly powers almost everything connected today. When an ESP32 sensor node pushes a reading to a cloud dashboard, when a smart meter checks in with a server, or when you open an app to see whether your device is online, the same basic conversation is happening: a client asks a question over HTTP, a server answers, and a URL says where to look. Berners-Lee made a deliberate choice that turned out to matter enormously. He kept the standards open and unlicensed. Anyone could implement a browser or a server without pa

What happens when thousands of people decide they're hungry at the exact same time? The Quiet Before the Storm 10:00 PM. The numbers are gentle tonight. One hundred eighty-nine requests trickle in. Someone in Lagos is ordering late-night suya. A rider in Ibadan is wrapping up his last delivery. In Bangladesh, someone is just discovering us for the first time. By 11:00 PM , things get quiet. Just 8 requests. The platform takes a breath. 2:00 AM. A mystery. 151 requests spike out of nowhere. We check the logs. Nothing unusual. Just a group of night owls ordering food, maybe shift workers, maybe students pulling an all-nighter. The beauty of a platform is we're always on, always ready. 7:00 AM. Good morning, Nigeria. Fifty-five requests. People waking up, checking their wallets, planning their day. The coffee hasn't even brewed yet, but the platform is already humming. The Morning Rush 9:00 AM. 315 requests. The workday begins. Offices buzz with conversations about lunch plans. If someone searches "foodmat site" for the third time this week, they're getting closer to finding us. A corporate client logs in to set up their employee meal program for the first time. By 10:00 AM , the traffic settles to 50 requests. A calm before the real storm. 11:00 AM. 173 requests. The hunger is building. People are making decisions about what to eat, where to order, and which vendor to choose. Our World Cup campaign notifications ping. Someone shares their referral code. The viral loop begins. The Lunch Explosion 12:00 PM. 321 requests. It's happening. The platform comes alive. 1:00 PM. 339 requests. The peak is building. Our servers are handling it smoothly. This is where the magic happens when thousands of people decide they're hungry at the exact same time. 2:00 PM. 289 requests. Still going strong. Vendor dashboards refresh. Riders accept orders. Laundry bookings come in alongside food deliveries. If someone cancels an order with a reason, we take note. Every interaction teaches us

"Prop drilling is bad, use Context" is repeated everywhere — but the actual cost stays abstract. So I put the two approaches side by side with live render counters. Click one button and the difference is impossible to miss. ▶ Live demo: https://context-vs-props-drilling.vercel.app/ Source (React 19 + TS): https://github.com/dev48v/context-vs-props-drilling Two identical 4-level trees, both React.memo 'd. One threads a value down as a prop through every level; the other provides it once via Context and reads it only at the leaf. Change the value: Prop drilling → 4 components re-render. Every component on the path receives the changed prop, so all of them re-render — and each intermediate is cluttered with a value it does nothing with except pass along. Context → 1 component re-renders. The intermediates take no value prop, so they're skipped (memoized, props unchanged). Only the consumer leaf re-renders. The summary tallies it on every click: 4 vs 1 . Why Context skips the middle This is the part that surprises people: with Context, an intermediate component can be skipped even though a descendant re-renders . < ThemeCtx . Provider value = { val } > < A /> { /* memo, no props → skipped on value change */ } </ ThemeCtx . Provider > const A = memo (() => < B />); // skipped const B = memo (() => < C />); // skipped const C = memo (() => < Leaf />); // skipped const Leaf = () => { const value = useContext ( ThemeCtx ); // ← re-renders on context change return < div > { value } </ div >; }; React re-renders context consumers directly when the provider value changes — it doesn't need to re-render the components in between. With prop drilling there's no such shortcut: the only way the value reaches the leaf is through every parent, so every parent must re-render. The catch — Context isn't a free lunch Context isn't a "no re-renders" button. Every consumer re-renders whenever the provider value changes — there's no built-in selective subscription. One big, chatty context ca

I created Agentis Lux for the purposes of entering H0 Hackathon (Vercel + AWS Databases). #H0Hackathon See Agentis Lux's Devpost.com entry . It started with a comment at a hackathon. A you.com employee said the thing out loud: the web has a second audience now. When you ask ChatGPT or Perplexity a question, a retrieval agent fetches a page and reads its HTML to answer you. Not the laid-out site with the buttons and the hero image. The markup underneath. These agents arrive by the million, and many of them rely on the raw or minimally rendered HTML rather than running your JavaScript, so they often see far less of your page than a person does. That comment sent me to build. My first answer to it was Hermes Clew , for the GitLab Duo Agent Platform Challenge. Hermes lived inside GitLab Duo Chat, no frontend, no database: a Python engine that scanned the HTML, JSX, and TSX files in a repo, scored them across six categories, and let an LLM reason over the findings. It proved the core idea. It also told developers how to fix things, lived inside one vendor's chat, and only worked on files in a repo. Agentis Lux is what happened when I took that idea to the open web and rebuilt it with a different stance. Any live URL, not just repo files. Its own product on a real cloud architecture, not a chat window. And no fix suggestions, on purpose, where Hermes used to hand them out. Same six-category bones, a new body, a sharper philosophy. It scans your site and shows you what that second audience experiences when it tries to read it. What it does You paste a URL to Agentis Lux . You get a report. The report is written from the agent's point of view. Not "this is broken." More like: "an agent landing on this page can't tell which element starts checkout, because it's a styled div and not a button." It reports findings. It does not suggest fixes, and that is on purpose. I know what the agent sees, not what you should change. That is the whole value: visibility, and you decide what

I am committed to understand how systems actually work. I'm working on a multi-node lab to follow the complete path of a request from Python APIs to Linux processes, through Docker containers, networking and observability. The idea is simple: build a system that observes another system to understand the abstraction layers behind modern infrastructure. This project is about learning by building, experimenting and understanding what happens under the hood. Link: [ https://github.com/daniloprandi/devops-network-automation-lab ] DevOps #Linux #Python #Docker #Networking #Observability #Infrastructure

These little tricks will help you spend more time driving instead of charging.

You submitted your extension, waited days for review, and got back a rejection with a violation called "Purple Potassium." Your extension looks fine to you, so what does it even mean? Here is what it is, why it happens, and how to catch it before you ever hit submit. What "Purple Potassium" actually means "Purple Potassium" is Google's internal tag for excessive or unused permissions . Your manifest requests access to something your code does not actually use, and the reviewer flags it. It is one of the most common reasons a Chrome extension gets rejected, and it is frustrating precisely because the extension works fine in testing. Review is checking something testing never does: whether every permission you ask for is justified by your code. The usual causes 1. API permissions you declared but never call. You added tabs , bookmarks , or cookies to your manifest at some point, but there is no chrome.bookmarks.* call anywhere in your code. 2. Host access that is too broad. You requested <all_urls> when your extension only touches one site: // Flagged "host_permissions" : [ "<all_urls>" ] // Better "host_permissions" : [ "https://*.example.com/*" ] Leftover permissions after removing a feature. You shipped a feature that needed downloads, later removed the feature, and forgot to remove the permission. The tabs misunderstanding. The tabs permission does not grant access to the tabs API. Basic methods like chrome.tabs.create() work without it. It only grants four sensitive Tab properties: url, pendingUrl, title, and favIconUrl. If you declare tabs but never read those, it counts as unused. How to fix it by hand List everything in permissions, optional_permissions, and host_permissions. For each one, search your code for the matching chrome. call. Remove any permission with no usage. Narrow and other broad patterns to the specific hosts you need. In your reviewer notes, write one plain sentence per sensitive permission explaining why you need it. Reviewers often lack con

The biggest challenge wasn't choosing a language model or designing prompts—it was managing context over time. Once an application grows beyond isolated conversations, memory becomes just as important as reasoning. An assistant that remembers previous architectural decisions, coding preferences, and project history can contribute much more effectively than one that starts from scratch every session. Runtime intelligence proved to be equally important. Not every request deserves the same computational resources. Routing tasks based on complexity, enforcing execution budgets, and maintaining an audit trail make AI systems more predictable and practical for real-world development. DevPilot AI brings these ideas together by combining Google Gemini for reasoning, Hindsight for persistent memory, and cascadeflow for runtime intelligence. While the project will continue to evolve, building it reinforced one idea above all else: the future of AI applications isn't just about generating better responses. It's about building systems that can remember, adapt, and make better decisions over time. If you're interested in the architecture or would like to explore the project further, you can find the source code here: GitHub: https://github.com/siddharthg-7/DevPilot-Ai- I'm always interested in feedback and discussions around persistent memory, runtime intelligence, and AI engineering. If you've explored similar ideas or approached these challenges differently, I'd love to hear your perspective.

Prediction markets just hit $3.6B in volume. I wanted to know what the biggest traders were betting on — in real time. So I built WhaleTrack. Here's how it works under the hood. The Problem Polymarket has a public leaderboard. But it only shows P&L totals — not what whales are currently betting on, not their recent activity, not their win rate. If you want to follow smart money, you're flying blind. I wanted something that answered: what are the top traders doing right now? The Stack Vanilla JS frontend (no framework, keeps it fast) Vercel serverless function as a backend proxy (avoids CORS issues) Polymarket's public data API — no auth required Step 1: Finding the Whales Polymarket exposes a leaderboard endpoint: https://data-api.polymarket.com/v1/leaderboard?limit=20 This returns traders ranked by P&L. I pull the top 10, grab their wallet addresses, and that's my whale list. Step 2: Fetching Live Activity For each whale wallet, I hit: https://data-api.polymarket.com/activity?user={address}&limit=20 This returns their recent trades — market name, size in USDC, timestamp. Refreshes every 60 seconds. Step 3: Calculating Win Rate (the tricky part) The key is the redeemable flag — redeemable: true means they won, currentValue: 0 + redeemable: false means they lost. Took a few wrong attempts with cashPnl (always negative, not useful). Step 4: The Whale Alert Banner Every 60 seconds I check for trades over $5,000 placed in the last 10 minutes. When it fires, a green banner slides down with the whale name, market, and amount. Auto-dismisses after 12 seconds. First time I saw it fire live with a $28K bet — genuinely exciting. Results 129+ users in the first few days Zero ad spend Traffic from Twitter, Reddit, Quora What's Next More whale wallets (suggestions welcome) Click-through to open the same market on Polymarket directly Email/push alerts for big trades Check it out: whaletrack.app All feedback welcome — especially if you spot a whale I'm missing.

I'm sure we're all familiar with Dark Crystal, so we know that Jim Henson can be weird and tackle slightly more mature subject matter. But there is little in his oeuvre that is quite as mind-bending as the Muppetless The Cube. This 1969 teleplay was produced for an NBC anthology series called Experiment in Television, […]

Eight months ago, a CS exam forced me to write pseudocode when I already knew how to code. Instead of studying, I rage-built an app. Today examintelligence.app is live. Here’s exactly how I got here—from vibe-coded POCs to a production hybrid AI pipeline—without the curated startup gloss. The Philosophy Behind the Build I’ve always believed studying for marks ≠ actually learning. When I was first introduced to organic chemistry, I hated it. Then I ran into GNNs in Machine Learning with PyTorch and Scikit-Learn , paired with the MoleculeNet dataset. Suddenly, everything clicked. I wanted to learn everything about it. That’s the core problem: exams optimize for pattern recognition, not curiosity. You’re forced down one prescribed path, and it rm -rf s the fun of learning in most cases. So one week before my first prelims, I decided to build exam intelligence. The plan was simple: introduce brutal efficiency using AI for what it’s actually built for: pattern recognition Parse every past paper, mark scheme, and examiner report. Distill it down to precisely what matters. Free up time for coding and creative work. Vibe-Coding the POC (and Why It Collapsed) I’m generally against vibe-coding. It’s unreliable, hard to maintain, and a security nightmare. But with prelims staring me in the face, I had no choice. I opened Claude and vibe-coded it module by module. The only code review I had time for was checking for suspicious os.system or subprocess calls. That was it. I shipped anyway. Initial stack: Gemini API (no agent frameworks, no LangGraph) Streamlit frontend PostgreSQL It validated my idea but functionally, it barely held together. After prelims, I finally looked at what the AI had actually built: Dashboard showing random stats Asked Gemini for a JSON response with 5 keys, saved only 2 Randomly created DB tables while trying to read subjects The kind of code you end up with when you let an AI cook unsupervised for a week. So I did the only reasonable thing: opened Neov

I had arrived at the final frontier. My bare-metal kernel was booting in QEMU, driving NVMe block storage, running multi-agent swarms, and rendering a force-directed canvas. But to make V.E.L.O.C.I.T.Y.-OS a truly next-generation system, I needed to close the loop: the operating system had to be able to evolve and compile itself without human intervention. The V.E.L.O.C.I.T.Y.-OS 12-Part Roadmap We are building a bare-metal, self-healing operating system running entirely inside the CPU's L3 cache. Here is the roadmap for this 12-part series: Part 1: The Spark — Exposing the "Safe-Room" security leak and building the compiler gate. Part 2: The NDA Language — Designing a content-addressed triplet representation to cure context bloat. Part 3: Ditching the Web Stack — Building a native 30MB IDE with 1,500,000x IPC latency drops. Part 4: The Closure JIT — Compiling AST blocks to nested closures and bypassing borrow checker limits. Part 5: JIT Math Optimizations — Replacing division operations with precomputed 16-bit lookup tables. Part 6: x86-64 Assembler & SCEV-Lite — Compiling scalar loops directly to native code in constant time. Part 7: Classic Compiler Passes — Implementing inter-procedural Dead Code Elimination and loop unrolling. Part 8: Reclaiming Ring 0 — Exiting UEFI boot services and transitioning the kernel to Ring 0. Part 9: Bare-Metal Drivers — Writing a PCI scanner, NVMe block storage controller, and FAT32 parser. Part 10: Synaptic Canvas — Rendering a spatial, force-directed GUI based on model token activation vectors. Part 11: Swarms & Hot-Patching — Building multi-agent scheduling and zero-downtime RCU driver updates. Part 12: Self-Evolution — Handing system control over to a local LLM Terminal that self-optimizes via telemetry. (You are here) During the final hours of my Sunday morning sprint, I completed the self-healing loop, the Biosphere P2P registry, and the Boot-to-NDA LLM Terminal handover. To achieve self-healing, I built a Ring 0 telemetry sys