标签：#beginners

My learning so far I absolutely love learning to code. I gave it up to vibe code earlier last year and completely regret it. At first it felt like I was moving faster, but over time I realized I was skipping the part that actually made me better. My learning journey is fueled by passion and the hopes to move into a Go/SWE/Cloud type role. I do not know exactly how I will go about doing so, but I will work until I am noticed. Right now I am trying to focus on building real understanding. Not just getting something to work, but knowing why it works. I want to be able to read errors, debug my own code, understand the tools I am using, and slowly become the kind of developer that can solve problems without panicking. Learn to code! If anyone has any doubts on if coding is "worth it" still, I can account for how personally fulfilling it is. Solving a bug/problem in your own code gives me a personal high. There is something different about struggling with something, walking away, coming back, and finally seeing it click. It reminds you that you are actually learning. Every small fix feels like proof that you are getting better. I am not against using tools or AI. I still think they can be helpful. But I do think there is a big difference between using them to learn and using them to avoid learning. I had to learn that the hard way. So if you are new, or if you stopped for a while like I did, I really think you should keep going. Build small things. Break stuff. Fix it. Read docs even when they are boring. Ask questions. Take notes. Let yourself be bad at it for a while. I do not know where this journey will take me yet, but I know I want to keep showing up.

Recentemente percebi uma coisa meio curiosa: eu simplesmente tinha um problema ao consumir o conteúdo do 4noobs do jeito que ele é organizado hoje. Não porque a organização seja ruim — muito pelo contrário. Acho que a comunidade fez um trabalho incrível organizando o projeto. O ponto é que eu percebi que meu jeito de estudar é diferente: tenho muito mais facilidade quando consigo seguir listas, trilhas ou um caminho de aprendizado mais visual. Foi aí que pensei: "Se esse problema existe para mim, talvez exista para mais alguém. E se, de quebra, eu aproveitar isso para praticar consumo de APIs?" Foi assim que nasceu a Central 4noobs . A proposta era simples: consumir todo o conteúdo disponível no GitHub do 4noobs e apresentá-lo de uma forma que fizesse mais sentido para o meu jeito de estudar, organizando os materiais em listas e trilhas de aprendizado. A ideia nunca foi substituir a organização do projeto original, mas oferecer uma forma diferente de navegar pelo mesmo conteúdo. Essa era a ideia inicial... mas, como acontece com praticamente todo projeto pessoal, ela foi crescendo conforme o desenvolvimento avançava. Mas ainda é uma alternativa . Tenham em mente isso. :) O que aprendi durante o projeto O projeto foi desenvolvido utilizando Next.js , TypeScript , Drizzle ORM e Supabase como banco de dados (e hoje já não tenho tanta certeza se essa foi a escolha mais inteligente 😅). O maior aprendizado foi entender melhor como funciona o consumo de APIs. Antes eu entendia o conceito na teoria (com o próprio 4noobs , inclusive), mas foi durante o desenvolvimento da Central que realmente comecei a compreender como tudo se conecta. Depois desse projeto, passei a enxergar melhor como uma API é estruturada e, principalmente, como consumir seus dados sem simplesmente despejar tudo na tela. Outra parte interessante foi aprender a tratar os dados recebidos. Uma coisa é receber uma resposta gigantesca da API. Outra completamente diferente é filtrar apenas as informações que re

When I first started programming, I thought the best developers had one superpower. They remembered everything. Every function. Every method. Every API. Every piece of syntax. So I spent hours trying to memorize things. JavaScript methods. SQL queries. Regex. CSS properties. I thought that would make me valuable. I was wrong. The Day Everything Changed One day I watched a senior developer solve a difficult production issue. They opened Google. They opened the documentation. They searched Stack Overflow. They experimented. They tested. They failed. Then they fixed it. That's when I realized something. They weren't valuable because they remembered everything. They were valuable because they knew how to solve problems. Google Doesn't Make You Less of a Developer For a long time I felt guilty every time I searched for something. "Real developers shouldn't need Google." That's what I believed. Then I realized... Even experienced engineers search for documentation every day. Not because they're bad. Because technology changes constantly. Nobody remembers every detail. Syntax Is Temporary Think about the last five years. How many frameworks have changed? How many libraries disappeared? How many APIs were deprecated? Technology moves fast. Problem-solving doesn't. If you know how to think... You can learn any syntax. Companies Don't Hire Human Compilers Nobody pays you because you know where to put a semicolon. Nobody promotes you because you memorized every React hook. Companies pay developers who can: understand problems communicate clearly debug effectively make good decisions work with people deliver reliable software Those skills don't disappear when a framework becomes outdated. The Questions That Matter Instead of asking: "Do I know this syntax?" I started asking: Can I understand the problem? Can I break it into smaller pieces? Can I explain my thinking? Can I find reliable information quickly? Can I learn something new when I need it? Those questions changed the wa

I had a lot of work to get through, and for once I didn't want to crawl through it one ticket at a...

When something breaks in Linux, experienced engineers don’t guess. They check the logs. 👉 Logs are the “black box recorder” of a Linux system. They tell you: what happened when it happened why it failed If you can read logs properly, you can debug almost anything. What Are Logs? Logs are records of system and application activity. Linux constantly records: System events Errors User activity Application behavior Linux constantly records: Where are Logs Stored? Most Linux logs are stored inside: /var/log Check logs directory: cd /var/log ls This is the first place DevOps engineers check during system issues. Important Log Files Log File Purpose Command to View /var/log/syslog General system messages tail /var/log/syslog /var/log/auth.log Login attempts & authentication tail /var/log/auth.log /var/log/kern.log Kernel & hardware messages dmesg or tail /var/log/kern.log /var/log/nginx/error.log Web server errors (Nginx) tail /var/log/nginx/error.log /var/log/dmesg Boot and hardware logs dmesg /var/log/apache2/ -> Apache logs These logs help you identify system, security, and application-level issues. View Logs Using cat cat /var/log/syslog Good for small files. Using less less /var/log/syslog Useful keys:: Space → Next page b → Previous page q → Quit 👉 Best for large log files. Using tail tail /var/log/syslog Show last 10 lines. Real-Time Monitoring (tail -f) tail -f /var/log/syslog 👉 -f = follow live updates This is one of the most-used debugging commands in production servers. Stop with: Ctrl + C Searching Logs with grep grep error /var/log/syslog Case-insensitive: grep -i failed /var/log/auth.log Show latest matching errors: grep error /var/log/syslog | tail -n 50 👉 Essential for filtering huge logs quickly. Boot & Hardware Logs (dmesg) dmesg Shows: Boot messages Hardware detection Kernel events Useful for startup and hardware troubleshooting. Modern Log System: journalctl Modern Linux systems use systemd logs . journalctl Recent errors: journalctl -xe Specific servic

A database without relationships is just a spreadsheet with delusions of grandeur. If you've ever stared at a Power BI report showing wrong numbers...totals that don't add up, filters that filter nothing, there's a good chance your data model was broken. Not a bug. Just two tables that should've been talking to each other… and weren't. This is your practical guide to data modeling, schemas, relationships, and joins in Power BI, what they are, how they connect, and how to stop getting burned by them. What Is Data Modeling and How Does It Work? Data modeling is the process of defining how your tables connect to each other inside Power BI's engine (called VertiPaq). Think of it like drawing a map between your tables, telling Power BI this column in Table A is the same thing as this column in Table B. When you load multiple tables into Power BI, it doesn't automatically know they're related. A Sales table and a Products table, sitting separately, can't filter each other. Data modeling builds the bridges. Power BI's model view lets you: Define relationships between tables Set cardinality and cross-filter direction Build star or snowflake schemas Create calculated columns and measures using DAX Under the hood, Power BI compresses and stores each column separately ( columnar storage ). Relationships are resolved in-memory at query time, which is why a well-structured model is blazing fast, and a messy one will bring your report to its knees. Key Concepts | Concept | What It Means | |--------------------------|-----------------------------------------------------| | Fact Table | Stores measurable events (sales, transactions, logs)| | Dimension Table | Stores descriptive context (products, customers) | | Primary Key (PK) | Unique identifier column in a dimension table | | Foreign Key (FK) | Column in a fact table referencing a PK in a dim | | Relationship | The defined link between a PK and FK across tables | | Cardinality | Describes how many rows on each side match | | Cro

Starting in IT I started in IT at a local school in my small town in December 2024. It was my first job out of college after earning my B.S. in Cybersecurity. None of the infrastructure was updated. Everything was failing, and luckily, I had one other IT person there: my director. I honestly think he knew less than I did, and he would get frustrated at almost every ticket. Even then, I knew I wanted a role where I could code. Fast forward to more recently, he had a freak out and quit. Now we have a two-person team, and everything is finally up to date and functioning. Even with things improving, I still knew I wanted to move toward a SWE-type role. Learning to Code I first decided to learn C# for Unity. I was really into game dev while I was in college, so it felt like a natural place to start. I began with the Microsoft/freeCodeCamp C# certification, and I surprisingly really enjoyed it. I made a few small games on itch.io that no one cared about, but I had fun building them. After that, I went on a bit of a language-hopping spree. I jumped from C# to C++, then into full-stack web development. I actually stuck with web dev for a while and really enjoyed it. But this cycle went on for awhile of just constant swapping. Wannabe Founder Then something switched overnight. I went from writing maybe 0-5% AI-generated code to using AI for nearly everything. I started spam-building startup ideas that did not really go anywhere. I may have made around $2-3k from them, but most of the time I was just chasing money and building whatever I thought had the quickest path to making some. I got seriously addicted to vibe coding. I tried Codex, Cursor, Claude, and basically anything with AI in it. I did like Codex the most, though. Eventually, I realized I had almost completely stopped coding by hand. I was not passionate about the startup ideas I was building. I loved coding, and I knew I had to step back. Back to Coding Now I am back to coding without AI assistance. I will eventua

AI makes it easier to build the wrong thing with confidence. That is the part I think a lot of beginner builders and freelancers miss. The obvious story is that AI makes execution faster. That is true. I can ask an AI coding tool to explain an error, compare implementation options, inspect a project, write code, refactor a screen, generate a QA checklist, or help me pick up where I left off. That is a huge change. But speed is not the whole story. When the tool gets faster, your judgment becomes more important, not less. You have to decide what the project is allowed to become. You have to decide which tradeoffs are acceptable. You have to decide whether the output actually matches the user's job. You have to decide when the AI is solving the real problem and when it is decorating the wrong one. In my freelance work, AI changed the job from searching and stitching to directing, reviewing, and verifying. That sounds cleaner than it feels. Directing means you need to know what outcome you want. Reviewing means you need to notice when the answer is plausible but wrong. Verifying means you cannot treat a green checkmark, a pretty screen, or a confident explanation as proof that the app actually works. The beginner mistake is believing AI removes the need to think clearly. The better rule is this: AI removes some friction from execution, then hands you more responsibility for scope. The Faster Tool Still Needs A Smaller Job When I started using AI heavily for software work, the old research loop changed immediately. Before modern AI tools, a lot of software work meant digging through documentation, old forum posts, Stack Overflow answers, YouTube videos, outdated examples, and half-related blog posts until something clicked. You stitched pieces together and hoped the tutorial you found still matched the version of the framework you were using. Now you can ask the tool directly. That is better. It is also dangerous if you confuse a fast answer with a good product decision

Last week, our company needed to hire an on-site operations engineer. I used AI to screen 310 resumes...

Semantic HTML and Accessibility: Building Better Websites Introduction Semantic HTML is the practice of using HTML elements that clearly describe the purpose of the content on a webpage. Instead of using many <div> elements, semantic tags such as <header> , <nav> , <main> , <section> , <article> , and <footer> make the page easier to understand. Semantic HTML is important because it improves accessibility, helps search engines understand web pages, and makes code easier to read and maintain. Before: Non-Semantic HTML <div class= "header" > <h1> My Portfolio </h1> </div> <div class= "navigation" > <a href= "index.html" > Home </a> <a href= "about.html" > About </a> </div> <div class= "content" > <p> Welcome to my portfolio website. </p> </div> After: Semantic HTML <header> <h1> My Portfolio </h1> </header> <nav> <a href= "index.html" > Home </a> <a href= "about.html" > About </a> </nav> <main> <section> <p> Welcome to my portfolio website. </p> </section> </main> Accessibility Issues I Found 1. Images Missing Alternative Text Before: <img src= "images/profile.jpg" > After: <img src= "images/profile.jpg" alt= "Profile picture of Grace Loko" > Adding alternative text allows screen readers to describe images to users with visual impairments. 2. Navigation Was Not Semantic Before: <div> <a href= "index.html" > Home </a> <a href= "about.html" > About </a> </div> After: <nav> <a href= "index.html" > Home </a> <a href= "about.html" > About </a> </nav> Using the <nav> element helps assistive technologies identify the website navigation. 3. Form Inputs Had No Labels Before: <input type= "text" placeholder= "Your Name" > After: <label for= "name" > Name </label> <input type= "text" id= "name" name= "name" > Labels improve accessibility by helping screen readers identify each form field. Conclusion This accessibility audit helped me understand the importance of semantic HTML and accessible web design. By replacing non-semantic elements with semantic tags, adding image alt text,

We’ve all been there. You scroll through your feeds, see a flashy ad promising a high-paying tech job in 3 months, and think, “This is it. This is my golden ticket.” You buy the bootcamp, spend sleepless nights watching lectures, stack up a dozen colorful certificates on your LinkedIn, and then... nothing. No callbacks. No interviews. Just a lingering feeling of frustration and the nagging thought: Are bootcamps and online courses just a massive scam? I used to think so. When I was trying to break into tech, I bought courses like crazy. I collected certificates like they were Pokémon cards. Yet, my first real developer job didn't show up until five or six years later. And let me tell you a secret: it wasn’t the certificates that got me the job. It was because I finally figured out how to actually learn. The truth is, almost every bootcamp or course—even the mediocre ones—has something valuable to offer. The problem isn’t always the material; it’s how we interact with it. If you feel stuck in "tutorial hell," here is a positive, practical guide to changing your approach, reclaiming your time, and turning any learning material into real, career-changing expertise. 1. Curate Your Sources (Choose Your Battles Wisely) Before we talk about how to study, we need to talk about what to study. Even though you can extract value from almost any course, your time is highly valuable. Don't waste it on low-quality content. When choosing a course or bootcamp, look for these four green flags: The Instructor Has Real-World Mileage: Is the instructor a practitioner, or are they just reading the official documentation back to you? If they don't work with the technology daily, they won’t be able to explain the nuances, edge cases, and real-world trade-offs. A Project-First Curriculum: Avoid courses that are just endless lectures of "theory first, practice never." Look for curriculums that build actual applications. Good Pacing and Editing: We've all watched those tutorials where the ins

We’ve all been there. You scroll through your feeds, see a flashy ad promising a high-paying tech job in 3 months, and think, “This is it. This is my golden ticket.” You buy the bootcamp, spend sleepless nights watching lectures, stack up a dozen colorful certificates on your LinkedIn, and then... nothing. No callbacks. No interviews. Just a lingering feeling of frustration and the nagging thought: Are bootcamps and online courses just a massive scam? I used to think so. When I was trying to break into tech, I bought courses like crazy. I collected certificates like they were Pokémon cards. Yet, my first real developer job didn't show up until five or six years later. And let me tell you a secret: it wasn’t the certificates that got me the job. It was because I finally figured out how to actually learn. The truth is, almost every bootcamp or course—even the mediocre ones—has something valuable to offer. The problem isn’t always the material; it’s how we interact with it. If you feel stuck in "tutorial hell," here is a positive, practical guide to changing your approach, reclaiming your time, and turning any learning material into real, career-changing expertise. 1. Curate Your Sources (Choose Your Battles Wisely) Before we talk about how to study, we need to talk about what to study. Even though you can extract value from almost any course, your time is highly valuable. Don't waste it on low-quality content. When choosing a course or bootcamp, look for these four green flags: The Instructor Has Real-World Mileage: Is the instructor a practitioner, or are they just reading the official documentation back to you? If they don't work with the technology daily, they won’t be able to explain the nuances, edge cases, and real-world trade-offs. A Project-First Curriculum: Avoid courses that are just endless lectures of "theory first, practice never." Look for curriculums that build actual applications. Good Pacing and Editing: We've all watched those tutorials where the ins

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.