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The first year class HelloWorld { public static void main ( String args []) { // Displays "Hello World!" on the console. System . out . println ( "Hello World!" ); } } The second year /** * Hello world class * * Used to display the phrase "Hello World" in a console. * * @author Sean */ class HelloWorld { /** * The phrase to display in the console */ public static final string PHRASE = "Hello World!" ; /** * Main method * * @param args Command line arguments * @return void */ public static void main ( String args []) { // Display our phrase in the console. System . out . println ( PHRASE ); } } The third year /** * Hello world class * * Used to display the phrase "Hello World" in a console. * * @author Sean * @license LGPL * @version 1.2 * @see System.out.println * @see README * @todo Create factory methods * @link https://github.com/sean/helloworld */ class HelloWorld { /** * The default phrase to display in the console */ public static final string PHRASE = "Hello World!" ; /** * The phrase to display in the console */ private string hello_world = null ; /** * Constructor * * @param hw The phrase to display in the console */ public HelloWorld ( string hw ) { hello_world = hw ; } /** * Display the phrase "Hello World!" in a console * * @return void */ public void sayPhrase () { // Display our phrase in the console. System . out . println ( hello_world ); } /** * Main method * * @param args Command line arguments * @return void */ public static void main ( String args []) { HelloWorld hw = new HelloWorld ( PHRASE ); try { hw . sayPhrase (); } catch ( Exception e ) { // Do nothing! } } } The fifth year /** * Enterprise Hello World class v2.2 * * Provides an enterprise ready, scalable buisness solution * for display the phrase "Hello World!" in a console. * * IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED * TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER * PARTY WHO MAY MODIFY AND/OR REDISTRIVUTE THE LIBRARY AS * PERMITTED ABOVE, BE LIABLE TO YOU FOR DAM

Quando voce cria um container e depois outro, eles podem nao se enxergar. O motivo quase sempre e a rede. Entender os tipos de rede que o Docker oferece resolve isso de uma vez. Comece listando as redes que ja existem no seu Docker. docker network ls A rede bridge e a rede host vem de fabrica. Nenhuma delas oferece resolucao de nomes entre containers. Para isso voce precisa criar a sua propria rede. docker network create minha-rede Agora os containers que entrarem nessa rede conseguem se comunicar pelo nome do container. docker run -d --name app1 --network minha-rede nginx docker run -d --name app2 --network minha-rede alpine sleep 3600 Teste a comunicacao. docker exec app2 ping app1 -c 2 O ping funciona. O DNS interno do Docker resolve app1 para o IP interno do container. Isso resolve 90% dos problemas de comunicacao. A rede bridge padrao nao tem esse recurso. Crie sua propria rede sempre que precisar que containers se enxerguem. Agora a rede host. Ela elimina o isolamento de rede. O container usa a placa do host diretamente, sem NAT e sem mapeamento de porta. docker run -d --name web-host --network host nginx O nginx fica acessivel em http://localhost:80 direto. Sem -p 80:80. Mas so um container pode usar cada porta, porque a porta e a do host de verdade. Para ambientes com mais de uma maquina, existe a rede overlay. Ela precisa do Docker Swarm ou de um cluster. docker swarm init docker network create -d overlay rede-overlay docker service create --name api --network rede-overlay --replicas 3 nginx Os containers em maquinas diferentes se enxergam pelo nome como se estivessem na mesma maquina. No dia a dia, a rede bridge personalizada resolve quase todo caso de uso. Host e para performance ou casos especificos. Overlay e para quando voce escala para mais de uma maquina. That's all for now. Thanks for reading!

The café was crowded that evening, but to me, the world had fallen completely silent. An open...

Two Sum Given an array of integers nums and an integer target , return indices of the two numbers such that they add up to target . (You may assume that each input would have exactly one solution, and you may not use the same element twice. You can return the answer in any order.) Python ####Double FOR Loops (Runtime: 2100ms, Memory: 13.3MB) #DECLARE nums: ARRAY of INTEGER #DECLARE target: INTEGER class Solution : def twoSum ( self , nums , target ): Length = len ( nums ) for i in range ( Length - 1 ): for j in range (( i + 1 ), Length ): if nums [ i ] + nums [ j ] == target : return i , j return None ####Hashing Algorithm (Runtime: 0ms, Memory: 12.9MB) #DECLARE nums: ARRAY of INTEGER #DECLARE target: INTEGER class Solution : def twoSum ( self , nums , target ): Seen = {} for i , Value in enumerate ( nums ): if ( target - Value ) in Seen : return Seen [ target - Value ], i Seen [ Value ] = i return None

Nice to meet you! I'm Andrew It's been a year since I joined the community. I started developing a bit earlier, and changing my career just by learning and practicing is far from what I had planned. I cannot help but be thankful for each course and tutorial, and each developer and tutor who has shared some knowledge and wisdom with me. It is still too early to know exactly what I will fix, build, or vibe to improve the world, but I will do my best... print ( " Hola mundo, aquí vamos! " ) Follow my journey on GitHub "I'm curious to hear from others—what was the biggest challenge you faced during your first year of coding? Or, if you're just starting, what's one thing you're excited to build?"

Yo! what's up guys 👋🏻 Every developer👩🏻‍💻 has a different environment where ideas turn into...

AWS Builder Center now offers free 8-hour sandbox environments for workshops. Here's how it works, who it's for, and how it compares to Free Tier, Skill Builder, and Educate.

Introduction My zsh profile is over 1000 lines at this point. A lot of that is functions I asked AI to generate for me, since it's fast, portable, and saves me a ton of typing. Here's the thing though: the shortcuts that save me the most time aren't the clever ones. They're the dumb ones. Things like clone instead of git clone && cd , or dir instead of mkdir -p && cd . Each one only saves a second or two, but I run them so often that it adds up fast. These are in no particular order, just the ones I reach for constantly. Git aliases for common commands A few one-liners I have set up as plain aliases: alias gcp = "git cherry-pick" alias git-append = "git commit --amend --no-edit -a" gcp is self-explanatory. git-append amends the last commit with your currently staged (and unstaged, thanks to -a ) changes without touching the commit message. Great for fixing up a commit you just made before you push. Create a branch or switch to it if it already exists One of my most-used functions. Normally you have to remember whether a branch exists before deciding between git checkout <branch> and git checkout -b <branch> . This just does the right thing either way: gb () { if git rev-parse --verify --quiet " $1 " > /dev/null ; then git checkout " $1 " else git checkout -b " $1 " fi } Nuke all local changes to reset the working tree When an experiment goes sideways or I just want to throw everything away and start clean, I run nah : nah () { git reset --hard git clean -df if [ -d ".git/rebase-apply" ] || [ -d ".git/rebase-merge" ] ; then git rebase --abort fi } This resets tracked changes, removes untracked files and directories. No confirmation prompt, so use it carefully. Print recent commits as ready-to-paste cherry-pick commands Useful when you need to cherry-pick a batch of commits from one branch onto another in order: logs () { if [[ -z " $1 " || " $1 " = ~ [ ^0-9] ]] ; then echo "Usage: logs <number_of_commits>" return 1 fi git log -n " $1 " --reverse --pretty = format: "g

Every API request needs to answer: who is this, and are they allowed? Session auth answers it by having the server remember every login. JWT answers it by making the client carry its own proof — no server memory needed. What's inside a token Header . Payload . Signature. Header and payload are just base64-encoded — readable by anyone, not encrypted. The signature is what matters: a hash of the header + payload, made with a secret key only the server knows. Change one character of the payload, the signature breaks, the server rejects it. Trust comes from the math, not from hiding the data. Client logs in with credentials Server verifies them, signs a token, sends it back Client attaches the token to every future request Server checks the signature — no database lookup Valid + not expired → request proceeds No session table anywhere. The auth state lives inside the token itself. The trade-off Can't instantly revoke a token — it's valid until it expires. Fix: short-lived access tokens + a revocable refresh token. Payload is readable, so never put sensitive data in it. Security comes from HTTPS + safe client-side storage, not secrecy. One-liner to remember it by Session auth: remember who logged in, check memory each time. JWT: remember nothing, verify the proof each time.

The first message ever sent across the network that became the internet was not a grand declaration. It was two letters: "LO" . Not a word anyone chose, not a slogan, just the first half of a login command that never finished because the system crashed. More than fifty years later, that accidental fragment is one of the best origin stories in computing, and it still has something to teach anyone building connected devices today. The night of 29 October 1969 At around 10:30 in the evening on 29 October 1969, a student programmer named Charley Kline sat at a computer in Leonard Kleinrock's lab at UCLA. His job was to log in to a second machine roughly 350 miles away at the Stanford Research Institute (SRI) in Menlo Park, California. The two computers were among the first nodes of ARPANET, the U.S. Defense Department research network that would eventually grow into the internet. Kline started typing the command LOGIN . To make sure the letters were arriving, he had a colleague at SRI on the phone confirming each keystroke. He typed L , and Stanford confirmed the L. He typed O , and Stanford confirmed the O. Then he typed G , and the SRI machine crashed. So the very first message transmitted over ARPANET was the truncated, unintentional "LO" . Kleinrock has enjoyed pointing out for decades that they could not have scripted anything better: the first word on the internet was "lo," as in "lo and behold." A little over an hour later, after the bug was fixed, Kline completed a full login, but the accidental version is the one history remembers. Why a crash matters more than a clean success It is tempting to treat "LO" as a cute footnote, but the crash is the useful part. ARPANET was not built to be reliable on day one. It was built to discover how to be reliable. Everything we now take for granted about networking, error handling, retransmission, acknowledgements, graceful recovery, exists because early links failed constantly and engineers had to design around failure rath

MarkDown - что это? [[Markdown]] — это язык разметки, с упрощенным до человекочитаемости синтаксисом. Markdown — создан Джоном Грубером ( John Gruber ) в 2004 году. Markdown — это фактически "микро" язык для трансляции "текста markdown"в подмножество языка XHTML. Markdown - итоговая презентация текста всегда документ HTML , и только потом если нужно следующим шагом PDF, др. стандарты документации. Markdown принципиально не может задействовать весь потенциал XHTML, результатом его работы всегда является ограниченный набор элементов. Markdown — это транслятор который при анализе интегрированного в "текст" markdown HTML/XHTML кода обязан по стандарту просто его транслировать в итоговый образ документа. Markdown — это, если цитировать автора > «Markdown — это инструмент преобразования текста в HTML для веб-писателей. Markdown позволяет писать в легко читаемом и удобном для написания текстовом формате, а затем преобразовывать его в структурно корректный HTML. ║ John Gruber » Примечание Термин транслятор , не обходимо понимать как сущность алгоритм Я не нашел программу реализующая концепт MarkDown по принципам John Gruber. Ни одна программа не умеет делать из Markdown валидный по John Gruber HTML. Все проверенные мною программы Obsidian, MarkText, Typora, на выходе из 10 строк генерируют портянку HTML в несколько тысяч строк!!! Причина Obsidian, Typora и MarkText используют Markdown не для веб-писателей и блогеров, а как формат хранения баз знаний (Knowledge Management)**. John Gruber - Wikipedia

The therapy unit at the hospital I work for had six treatment rooms. Room 1, Room 2, Room 3, and so on, each split by the kind of therapy it handled. And each room kept its own document to record patients. The problem wasn't that the documents existed. The problem was that no two of them looked alike. Same patient. Same information. But every room ordered the columns differently and named things differently. One put the date in the first column. Another put it last. One wrote "treatment time." The room next door wrote "minutes used." On their own, each form worked fine. Looked at one at a time, there was nothing wrong. The trouble showed up the moment anyone tried to combine them. The work that never ended Every so often, a request would come down from above: Can we see the overall numbers? That was when the real work began. I would open all six documents side by side. I would line up columns that didn't match, by eye, and move each value into one master table by hand. Days of this would get me a single sheet of statistics. Then the next quarter, the same request came down again. And I started over. The table I'd built last time was useless if the format had shifted even slightly. So I rebuilt it from scratch. Every time. I couldn't stand it. This was obviously a job you do right once and never touch again. We just weren't doing it right. So instead, we kept feeding people's evenings into it. The obvious answer The fix was simple. Make all six rooms use one form. Same columns. Same names. Same order, everywhere. Then there's nothing to move when you combine them. The statistics become a matter of stacking, not translating. The answer was so obvious I wondered why nobody had done it years ago. So I built a unified form in Excel and sent it around. And that's where I learned Excel has walls of its own. Where Excel broke down Once a file gets passed around, you lose track of which copy is the real one. The versions pile up. "Final." "Actually final." "Final, revised."

Git submodules have a reputation for being fiddly, but most of that pain comes down to a handful of missing commands and one config flag nobody mentions. Used well, they're a clean way to embed a shared library, a design-system repo, or a common docs folder inside another project - pinned to an exact commit so nothing shifts under your feet. This guide walks through the whole lifecycle, from adding a submodule to removing it, and calls out the gotchas that bite teams in real projects. Understanding What a Submodule Actually Is Before the commands, one mental model that clears up most confusion: a submodule embeds another git repo inside yours at a fixed path, pinned to a specific commit. Your repo doesn't track the submodule's files - it tracks which commit of the submodule to check out. That single idea explains almost every quirk that follows. Adding a Submodule Adding one is a single command: git submodule add git@github.com:org/shared-lib.git vendor/shared-lib This clones the repo into vendor/shared-lib , creates a .gitmodules file describing the mapping, and stages the pinned commit (git calls this a "gitlink"). Commit both pieces: git add .gitmodules vendor/shared-lib git commit -m "chore: add shared-lib submodule" The resulting .gitmodules entry is plain text and lives in version control: [submodule "vendor/shared-lib"] path = vendor/shared-lib url = git@github.com:org/shared-lib.git branch = main The branch line is optional: it's only used later when pulling the latest changes automatically. Cloning Without the Empty-Folder Surprise The most common submodule complaint is a teammate cloning the project and finding an empty folder where the submodule should be. The fix is knowing two commands: # Clone everything in one shot git clone --recurse-submodules <your-repo-url> # Already cloned? Initialize after the fact git submodule update --init --recursive Even better, run this once per machine so git pull and git checkout keep submodules in sync automatically - a

Introduction When learning JavaScript, one of the first concepts you’ll encounter is functions. Functions are the building blocks of JavaScript. They help you organize code, avoid repetition, and make your programs easier to understand. If variables store data, functions define behavior . You’ll use functions everywhere: handling user input, processing data, calling APIs, and structuring your code. In this article, we’ll cover: What is a function Function declarations Function expressions Parameters vs arguments Return values Arrow Functions Why Functions Matter 1. What is a Function? A function is a reusable block of code designed to perform a specific task. Think of it like a machine: Input → Process → Output function greet () { console . log ( " Hello! " ); } To run the function, you call it: greet (); // Hello! 2. Function Declaration This is the most common way to define a function: function add ( a , b ) { return a + b ; } 💡 Explanation: Defined using the function keyword Can be called before it is declared (because of hoisting) Key parts: function → keyword add → function name a, b → parameters return → output value add (); // ✅ Works! function add ( a , b ) { return a + b ; } 💡 Why does this work? JavaScript reads the code first, and function declarations are stored in memory during the initial phase (hoisting) . That’s why you can call the function even before it’s defined in the code. 3. Function Expressions Functions can also be stored in variables: function add ( a , b ) { return a + b ; } 💡 Explanation: Assigned to a variable Cannot be used before initialization add (); // ❌ Error: Cannot access before initialization const add = function ( a , b ) { return a + b ; }; 💡 Why does this cause an error? Because: const add has not been initialized yet when it is called. The function itself is not in memory at that moment . 4. Parameters vs Arguments This is a common beginner confusion: Parameter: variable in function definition Argument: actual value passed i

The moment you press Enter, billions of mathematical operations begin. Let's follow that journey. Every day, millions of people ask ChatGPT, Gemini, Claude, or other AI assistants questions. The answer appears almost instantly. But have you ever wondered what actually happens after you press Enter? Why can't a normal CPU answer these questions quickly? Why do companies spend billions on GPUs? Let's take a journey from your keyboard to the AI's brain. Imagine This... Suppose your office receives 10,000 letters. You have two choices. Option 1: One super-fast employee He opens one letter after another. Very fast. But still... One at a time. This is a CPU. Option 2: 10,000 employees Each opens one letter simultaneously. The work finishes almost instantly. This is a GPU. The difference isn't that each employee is smarter. There are simply many more workers working together. CPU vs GPU Think of it like this. CPU = CEO making decisions. GPU = Thousands of factory workers building products simultaneously. Why CPUs Are Amazing Your CPU performs tasks like Opening Chrome Playing music Running Windows Calculating taxes Managing memory Running applications These jobs require decisions branches conditions interrupts This is logical thinking. CPUs are built for this. Why GPUs Exist Originally GPUs were invented for games. Imagine rendering one image. A 4K monitor contains over 8 million pixels. Each pixel needs calculations. Every frame. 60 times every second. Instead of calculating one pixel at a time... GPU calculates millions together. Gaming accidentally created the perfect hardware for AI. AI Doesn't Think Like Humans LLMs don't "think" in English. They perform mathematics. Lots of mathematics. Almost everything inside an LLM becomes... Matrix × Matrix Vector × Matrix Addition Multiplication Normalization Softmax That's all mathematics. Billions of times. Why Matrix Multiplication Matters Imagine two tables. Table A 1 2 3 4 5 6 7 8 9 Multiply with Table B 2 4 6 8 1 3 Every n

I've lost count of how many AI side projects I started and abandoned. The pattern was always the same: a spark of excitement, two weeks of frantic coding, then the slow fade into yet another half-finished repo collecting dust on GitHub. But something changed in the last two months. I shipped three AI-powered MVPs to real users. Not all of them made money, but every single one taught me something about what it actually takes to go from "cool idea" to "working product." Here's what I learned. The brutal truth about AI side projects When I started my first real AI project back in February, I had grand ambitions. I was going to build a content summarizer that would pull articles from any URL, analyze sentiment, and generate Twitter threads. I spent three weeks obsessing over the perfect prompt engineering, containerizing the whole stack with Docker, and setting up a complex pipeline using LangChain and Pinecone. Then I showed it to a friend. "Can I just paste a link?" she asked. I had built an entire orchestration layer, but the input field was buried behind two authentication screens. The project died that weekend. Here's the thing I keep rediscovering: AI side projects fail not because the technology doesn't work, but because we over-engineer before we have users. The three MVPs that actually shipped After that failure, I changed my approach. I decided to ship something—anything—every two weeks. No matter how ugly. No matter how incomplete. The goal was to have a URL someone could visit and use. MVP #1: A dead-simple blog title generator I built this in a single afternoon. The entire frontend was a text box and a button. Backend? A single Node.js endpoint that called OpenAI's API with a prompt like: "Generate 5 catchy blog titles about [topic]." Here's the code that powered it (I've simplified it, but this is the gist): import express from ' express ' ; import OpenAI from ' openai ' ; const app = express (); const openai = new OpenAI ({ apiKey : process . env . OPENAI

When people talk about hackathons, they talk about the demo. The pitch, the UI, the "aha" moment on stage. Nobody really talks about the person who spent the whole weekend making sure the data didn't fall apart. That was me on AidStream, a blockchain-based aid distribution platform we built in a weekend and trust me it wasnt that easy as it seems. At a normal project, you can revisit your data model whenever.But during a hackathon you cant since when teamamtes start working on top of your tables it means both codes may start breaking . Serverless Postgres was the right choice for a hackathon: no local DB setup, no "wait, whose laptop has Postgres installed" problem. Everyone could connect to the same instance immediately. The gotcha was connection limits — with multiple people hitting the same database while testing features simultaneously, we ran into connection issues at the worst possible time (an hour before demo). So next time you watch a hackathon demo go off without a hitch, remember — someone probably spent the whole weekend quietly making sure the database didn't have a say in it. If you're the one holding the schema together at 2am, know this — it's not the flashy role, but it's the one that decides whether anyone else's code even runs.

Disclaimer: I'm currently learning web security through OWASP and PortSwigger Web Security Academy. These are my beginner-friendly notes rewritten as a blog to help reinforce my understanding. If you're just starting out, I hope this makes the topic easier to understand. What you'll learn: In this article you'll learn: ✔ What Broken Access Control is ✔ Vertical Privilege Escalation ✔ Security by Obscurity ✔ Parameter-Based Access Control ✔ Platform Misconfiguration ✔ Horizontal → Vertical Escalation ✔ IDOR ✔ Lessons learned from PortSwigger labs Concept Map: What is Broken Access Control? Broken Access Control happens when a user is able to access data, pages, or perform actions that they are not supposed to . Why should you care? Broken Access Control has ranked #1 in the OWASP Top 10 (2021) because it can expose sensitive data, allow privilege escalation, and let attackers perform actions they should never be able to perform. Think of a website with two types of users: Normal User Admin A normal user should only be able to view their own profile and perform basic actions. An admin, however, can manage users, delete accounts, change settings, etc. If a normal user somehow gains access to those admin features, that's Broken Access Control . In interview terms: Broken Access Control is the failure to properly enforce authorization, allowing users to perform actions beyond their intended permissions. 1. Vertical Privilege Escalation Vertical Privilege Escalation means moving up the permission hierarchy. Example: Normal User ↓ Admin A normal user should never be able to become an administrator. 1.1 Unprotected Functionality One of the simplest forms of Broken Access Control is Unprotected Functionality . Imagine a website has an admin page: /admin The developer removes the Admin button from the normal user's dashboard. Problem solved? No. If a user manually visits: /admin and the server doesn't verify whether they're actually an admin, the page opens. The mistake here

Python's Memory Model Is Not What You Think It Is Ask most Python developers how Python stores a variable and they will say "it stores the value." This is imprecise in a way that causes real bugs and real confusion in interviews. A precise mental model of how Python stores and retrieves data changes how you read and write code. Python does not store values in variables. Python binds names to objects. The distinction sounds philosophical until you trace code that involves mutation, function arguments, or aliasing. Then it becomes the most practically useful concept in the language. Names Are Not Boxes The box metaphor, which says a variable is a box that holds a value, is how most introductory programming courses explain variables. In many languages this metaphor is close enough to accurate that it does not cause problems. In Python it is wrong in ways that matter. A more accurate metaphor: a Python name is a label attached to an object. The object exists independently in memory. Multiple labels can be attached to the same object. Attaching a new label does not move or copy the object. x = [ 1 , 2 , 3 ] y = x print ( id ( x ) == id ( y )) # True (same object, two labels) When you write y = x , you are not copying the list. You are creating a second label that points to the exact same list object. The Four Operations You Must Distinguish 1. Assignment creates a new binding x = [ 1 , 2 , 3 ] x = [ 4 , 5 , 6 ] # x now labels a completely different object The first list still exists in memory until garbage collected. The name x simply stops pointing to it and now points to the second list. 2. Mutation modifies an existing object x = [ 1 , 2 , 3 ] x . append ( 4 ) # the object x labels is modified in place Any other name pointing to the same object will instantly reflect this change because they look at the same memory location. 3. Augmented assignment on mutable types mutates x = [ 1 , 2 , 3 ] y = x x += [ 4 , 5 ] print ( y ) # [1, 2, 3, 4, 5] (same object, mutated) The

Hey everyone! 👋 I'm a complete beginner and today I'm officially kicking off my #100DaysOfCode challenge with Python. I've dabbled with the idea of learning to code for a while, but this time I want to actually commit - so I'm posting daily updates here to keep myself accountable and track my progress over the next 100 days. My plan: Post a short update here every day - what I learned, what I struggled with, and what's next Eventually move into some small real-world projects once I've got the basics down Why I'm doing this: I want to build real skills, not just "watch tutorials and forget everything." Writing it down publicly (even anonymously) keeps me honest and hopefully connects me with others on the same path. If you're also learning Python or doing a 100 days challenge, I'd love any tips, resources, or just to follow along with each other's progress! Day 1 status: Just setting up my environment and going through the basics — nothing exciting yet, but everyone starts somewhere! 100DaysOfCode #Python #Beginner #LearnToCode