标签：#beginners

The AI Engineer World's Fair has attracted a lot of adults, and they brought their kids too, for...

Artificial Intelligence has brought the term "AI Agent" into almost every technology conversation. As a result, many people now use the words agent and automation interchangeably. While both are designed to reduce manual work and improve efficiency, they solve problems in fundamentally different ways. Understanding this distinction is essential if you're building software, automating business processes, or deciding where AI fits into your organization. What Is Automation? Automation is designed to execute predefined instructions. You tell the system exactly what to do, in what order, and under what conditions. Every time those conditions are met, it performs the same sequence of actions. For example: A customer submits a form. An email is automatically sent. A record is created in the database. A notification is sent to the sales team. Every step is predetermined. If the process changes, the workflow must be updated. Automation excels at repetitive, predictable tasks where consistency is more important than decision-making. What Is an AI Agent? An AI agent is not focused on following instructions. It is focused on achieving a goal. Instead of executing a rigid sequence of steps, an agent observes its environment, evaluates available information, makes decisions, and adjusts its actions as circumstances change. If one approach fails, it can try another. If new information becomes available, it can revise its strategy without requiring a developer to define every possible scenario in advance. In simple terms: Automation asks: "What steps should I execute?" An agent asks: "What is the best way to accomplish this objective?" This ability to reason and adapt is what makes agents fundamentally different from traditional automation. A Simple Example Imagine you're booking a business trip. An automated workflow might: Book the airline you specified. Reserve the hotel you selected. Email you the itinerary. It completes exactly what it was programmed to do. An AI agent, howev

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

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

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

"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've been building with AI for a while now. I can get these tools to do what I want, but I want to go a level deeper, past "it works" into actually understanding why. So I'm sharpening the fundamentals and the applied side, and writing it down here as I go. Expect short, honest posts on what I'm learning and building.

Hello everyone! Me and my friend. We are still learning, and recently we tried to make a very simple, hobby antivirus/security project written in C. We call it, F4us. It is far from perfect and still in a very early development phase. We just wanted to learn how some basic security concepts work under the hood. What we tried to build: A simple honeypot script to log suspicious access. A basic entropy function to check file structures. A background daemon loop to monitor files. Since we are still beginners, we know there are probably a lot of bugs and messy code in it. We just wanted to share this hobby journey here. Thank you for reading! here is the link(github), sorry if include many bugs or critical,it just an experiment: https://github.com/CornelI5/F4us my friend was out, he are in crashout cause this project is too hard. btw, dont use this in your real computer, trust me. just use VM(Virtual Machine),or you will get BSOD(blue screen of death) or kernel panic.

Ever planted seeds at the wrong time and watched them die? Me too. That's why I built PlantingCalendar.net - a free tool that tells you exactly what to plant every single day based on your climate zone. Built with AI coding tools in about 4 hours. 365 pages, each with unique planting instructions. Static site on Cloudflare Pages, zero server cost. Free, no sign-up.

For the last two years, one skill dominated every AI conversation: Prompt engineering. People spent hours crafting the "perfect" prompt. They built prompt libraries. They sold prompt templates. They believed that better prompts meant better AI. But AI has evolved. The bottleneck is no longer the prompt. It's the context . The Prompt Was Never the Problem Imagine asking an AI: "Build me a secure authentication system." A perfect prompt isn't enough. The AI also needs to know: Which programming language you're using Your existing codebase Your framework Your database schema Your security requirements Your coding standards Your deployment environment Your team's conventions Without that information, even the best model is forced to guess. And AI is terrible at guessing. What Is Context Engineering? Context engineering is the practice of giving AI everything it needs to solve a task—not just instructions. It's about designing the right environment for the model to think. Context includes: Source code Documentation Project architecture Previous conversations Git history APIs Logs Tool outputs User preferences Business requirements Memory Constraints The prompt tells AI what to do. The context tells AI how to do it correctly. Why Prompt Engineering Is Reaching Its Limits A prompt is static. Real work isn't. Projects change. Requirements evolve. Files get updated. Tests fail. New bugs appear. The AI must continuously receive fresh information. That's impossible with a single prompt. Instead, modern AI systems constantly rebuild their context as they work. Think About AI Coding Agents Why do AI coding agents feel dramatically smarter than a normal chatbot? Not because they have better prompts. Because they constantly gather context. They can: Read your repository Search across files Run terminal commands Execute tests Inspect logs Read documentation Fix errors Verify changes Remember previous actions Every step adds more context. Every iteration makes better decisions. Cont

Eight kids, eight chairs, one rule: explaining FIFA's best-thirds draw to my 8-year-old Rahul Devaskar Rahul Devaskar Rahul Devaskar Follow Jun 27 Eight kids, eight chairs, one rule: explaining FIFA's best-thirds draw to my 8-year-old # webdev # soccer # math # worldcup Add Comment 14 min read

This is part of my build-in-public series where I document everything honestly — the problems I face,the observations I make,and what I'm trying to build. There's a moment that happens in almost every class. The teacher finishes explaining something. Looks around the room. And asks: Everyone clear? And the entire class says "yes." Including me. Even when I understood absolutely nothing. The Loop Nobody Talks About Here's what actually happens — at least for me and I suspect for a lot of you reading this: Teacher is explaining a concept. I'm trying to follow. Somewhere in the middle, I lose the thread. Maybe the explanation was too fast. Maybe the concept needed something I didn't know yet. Maybe I just zoned out for 10 seconds and missed the part that made everything else make sense. Now I have two choices: Option A: Raise my hand. Ask the question. Risk looking like I wasn't paying attention or worse ask something that makes me look stupid in front of everyone. Option B: Stay quiet. Nod. Say "yes" when the teacher asks. And hope it makes sense later. I always pick Option B. And then "later" comes — sitting alone at home, textbook open, trying to study for a test and I have no idea where to even begin. The concept is still missing. The gap is still there. But now there's no teacher, no classroom, no one to ask. So I either text a friend (who's also confused), scroll YouTube for 40 minutes looking for the right explanation, or just… close the book and tell myself I'll figure it out tomorrow. I never figure it out tomorrow. This Isn't Just a "Me" Problem I'm an engineering student in Pakistan. Maths, Physics, Chemistry — subjects where one missing concept breaks everything that comes after it. And I genuinely believe most of my classmates feel exactly the same way. We just don't say it out loud. Because saying "I don't understand" in a classroom full of people takes a kind of courage that most of us don't have. So we all nod together. And we all go home confused toget

【互動藝術 DIY】用 p5.js 做一塊會呼吸的粒子背景 先問自己：阿哲會想動手嗎？ 看完這個效果，阿哲可能會想： 「那如果我把粒子排成自己的名字，滑鼠靠近時會散開嗎？」 這就是正確的方向—— 讓讀者想自己動手改參數 ，而不是背程式碼。 這個方法厲害在哪？ p5.js 官網有很多炫技的粒子效果，但大部分只是給你看「很厲害」。 這次不一樣——我要教你 用最少程式碼，做出最有呼吸感的互動 。 秘密是：用「距離」控制行為，用「lerp」讓移動變溫柔。 教學順序 先建立粒子：讓一群點組成畫面 教動畫：用 sin() 做出呼吸節奏 教距離感：用 dist() 偵測滑鼠 教溫柔散開：用 lerp() 柔和移動，不是瞬移 最後加美感：透明度、殘影、暖色 第一步：讓粒子回家 class Particle { constructor ( x , y ) { this . homeX = x ; // 記住家的位置 this . homeY = y ; this . x = x ; this . y = y ; } // 讓粒子回家的力量 returnHome () { this . x = lerp ( this . x , this . homeX , 0.05 ); // 每次移動5%的距離 this . y = lerp ( this . y , this . homeY , 0.05 ); } show () { noStroke (); fill ( 255 , 180 , 120 , 200 ); // 暖橙色 ellipse ( this . x , this . y , 4 ); } } 第二步：偵測滑鼠距離 function draw () { for ( let p of particles ) { let d = dist ( mouseX , mouseY , p . x , p . y ); if ( d < 100 ) { // 滑鼠靠近時，輕輕推開 let force = 0.05 * ( 100 - d ) / 100 ; p . x += ( p . x - mouseX ) / d * force ; p . y += ( p . y - mouseY ) / d * force ; } p . returnHome (); p . show (); } } 第三步：加呼吸節奏 let breathPhase = 0 ; function draw () { breathPhase += 0.02 ; let breath = sin ( breathPhase ); // -1 ~ +1 來回循環 background ( 10 , 8 , 5 ); // 暖暗色背景 for ( let p of particles ) { let d = dist ( mouseX , mouseY , p . x , p . y ); if ( d < 100 ) { let force = 0.05 * ( 100 - d ) / 100 ; p . x += ( p . x - mouseX ) / d * force ; p . y += ( p . y - mouseY ) / d * force ; } p . returnHome (); p . show ( breath ); // 把呼吸相位傳進去 } } function show ( breath ) { noStroke (); // 呼吸時變亮，吐氣時變暗 let alpha = map ( breath , - 1 , 1 , 150 , 255 ); fill ( 255 , 180 , 120 , alpha ); ellipse ( this . x , this . y , 4 ); } 第四步：殘影效果 function draw () { // 不要每幀清掉背景，而是蓋一層半透明黑色 fill ( 10 , 8 , 5 , 30 ); rect ( 0 , 0 , width , height ); // ... 其餘粒子邏輯 } 這樣粒子移動時會留下淡淡的光跡——很有沉浸式裝置的 feel。 阿哲可以怎麼玩？ 參數 預設值 改成... 效果 感知半徑 100px 50px 只有非常靠近才有反應 回家速度 0.05 0.02 超級慢，像在水裡 回家速度 0.05 0.2 快一點回覆 粒子數量 200 50 稀疏的星塵感 粒子颜色 暖橙 淡粉 更柔和的感覺 延伸練習 把粒子排成自己的名字 ：讓粒子組成「阿哲」或英文字母輪廓，滑鼠靠近時文字散開，離開後慢慢聚回來。 滑鼠不是破壞者，是一陣風 ：不只是排斥，而是讓粒子沿著滑鼠移動方向飄

I've been thinking about this problem for a while. Developers and businesses need quality websites fast — but the options are either overpriced custom builds, outdated templates, or starting from scratch every single time. So I decided to build the solution myself. Softchic is a premium template and ready-made website marketplace — production-ready, built on modern stacks, designed to actually look good. This is Week 1 of building it in public. Why Softchic The market exists. Developers need templates. Businesses need websites. But most template stores are either bloated, outdated, or built on stacks nobody wants to touch in 2026. Softchic is different — every template ships with: Modern stack (Next.js, TypeScript, Tailwind CSS v4) Clean, production-ready code Premium design out of the box The name went through 25+ candidates across multiple languages before landing here. Clean, available, memorable — Softchic. The Stack Framework: Next.js 14 (App Router) Language: TypeScript Styling: Tailwind CSS v4 Components: shadcn/ui Payments: Lemon Squeezy (international) + Paystack (Nigeria) Email: Resend Deployment: Vercel Design language: dark and premium — #0D0D0D background, #2563EB blue, #F97316 orange accents. Week 1 — What Got Built ✅ Waitlist page — designed and ready to deploy ✅ Navbar — responsive, dark-themed ✅ WaitlistForm — wired to Resend for email capture ✅ Brand system — colors, typography, full design identity locked ✅ Payment architecture — Lemon Squeezy + IP-based currency detection via ipapi.co with PPP pricing for global fairness The waitlist goes live very soon. Follow me here on Dev.to — I'll drop the link the moment it's live. Early subscribers get first access when the store launches. The launch goal: 200 waitlist subscribers before opening the store. That's the benchmark. No exceptions. What's Next Waitlist page goes live 🚀 Product listing page Template preview system First upload — a SaaS landing page template The Real Talk Building a marketplace fr

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is free and source-available on Github. Star git-lrc to help devs discover the project. Do give it a try and share your feedback. A single honeybee has exactly one move: find nectar, fly it home. Impressive aviation. Add a few thousand more bees and something strange happens. Now they're making honey, cooling the hive, and defending the colony against threats ten thousand times their size, with no Jira board, no standup, and nobody handing out tickets. That jump from "can fetch nectar" to "runs a self-regulating honey factory" is the best mental model I've found for multi-agent AI systems . So let's steal it xD First, what even is an "agent"? Before we throw thousands of them at a problem, it's worth pinning down what one actually is. An AI agent is an autonomous system that performs tasks on behalf of a user (or another system) by designing its own workflow and using available tools . Three things decide how good an agent actually is: The LLM powering it i.e the brain. Its tools which is the hands. The reasoning framework is how it turns tool outputs into the next decision. A single agent is fine. It's our lone bee, and it can do real work. But ask it to research a topic, run heavy calculations, scrape five websites, and write the summary, and you start to feel the ceiling. Multi-agent systems: bees, but for compute A multi-agent system keeps each agent autonomous but lets them cooperate and coordinate inside a structure . The magic isn't any single agent, it's the choreography between them (claude which is famous for that). And there are a few classic ways to choreograph it. 1. The decentralized network (a.k.a. "everyone's a peer") Every agent can talk to every other agent. They share information and resources, and they all operate with the same authority . No boss. Just message-passing. This is your agent network . It's great for emergent, collaborative problem-solv

I started treating a one-wheel like a folding bike replacement for a 3 km bus gap. The spreadsheet looked fine. Real life did not.## What broke my first estimates* Sticker range is not commute range. * Hills, cold mornings, and stop-and-go ate about 3% of the rated number on my route. I now plan at ~6% of brochure range and keep a buffer for a wrong turn. Weight shows up on stairs, not on paper. Carrying the wheel through a station twice a day mattered more than top speed ever did. Rain is a policy decision, not a gear decision. Some days I bail to transit. Pretending I will always ride made me resent the wheel.## A simple checklist I use now1. Measure your worst leg, not your best day.2. Count how many times you pick the wheel up per trip.3. Decide where you charge (home only vs. desk outlet).4. Set a weather cutoff before you are tired and annoyed.## DisclosureI work around electric unicycles professionally, so take this with that bias. I am still trying to optimize my own commute, not sell anyone a model.If you want plain spec tables while comparing wheels: https://www.kingsong.com/collections/electric-unicycle What would you add for mixed transit + one-wheel days?

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.

You don't have a million labeled images or a GPU farm — and you don't need them. Transfer learning lets you stand on a model someone else trained and reach high accuracy with a few examples in minutes. Here's the idea, visualized. ♻️ Race scratch vs transfer: https://dev48v.infy.uk/dl/day17-transfer-learning.html The insight The early layers of a trained network learn general features — edges, textures, shapes — that are useful for almost any vision task. Only the last layers are task-specific. So why relearn edges from scratch? Two ways to do it Feature extraction: freeze the pretrained backbone, replace the final classifier with a small new "head," and train only the head on your data. Fast, needs little data. Fine-tuning: also unfreeze the top few backbone layers and train them at a low learning rate so you adapt without wrecking what they learned. The demo races two accuracy curves: "from scratch" crawls up and plateaus low (not enough data); "transfer learning" starts high and climbs fast. Tweak the example count and freeze/fine-tune to see them respond. Why it matters now This is exactly why fine-tuning an open LLM works: a foundation model already learned language; you adapt it cheaply. Transfer learning is what makes deep learning practical for the rest of us. 🔨 Full recipe (load pretrained → freeze → new head → train → optionally fine-tune low-LR) on the page: https://dev48v.infy.uk/dl/day17-transfer-learning.html Part of DeepLearningFromZero. 🌐 https://dev48v.infy.uk

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