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You know that feeling when you join a new project or want to contribute to an open-source repo, and you spend the first two days just trying to figure out where everything is? I did. Every single time. Clone the repo. Open the files. Stare at 47 folders. Wonder which one actually matters. Grep for the entry point. Follow imports down a rabbit hole. Give up and ask someone. That's not learning. That's just wasted time. So I built CodeAutopsy. What it does Paste any GitHub URL. That's it. CodeAutopsy clones the repo, parses every file into an AST (Abstract Syntax Tree), traces every import and dependency, and gives you: An interactive dependency graph showing exactly what imports what The entry points — where execution actually starts A blast radius map — click any file and instantly see everything that breaks if you change it An AI-generated architectural summary explaining what the codebase does, how it's structured, and how to get started Live Health Telemetry: An Edge API that generates a live SVG health badge (A to F grade). Drop the markdown in your README once. Every time you refactor and re-scan, your badge updates everywhere instantly. My own CodeAutopsy repo just hit 99/100. Drop the markdown snippet once and forget about it. What used to take days now takes about 10 seconds. The real problem it solves Every developer has been here: You're onboarding at a new job. The codebase has 200 files. Your tech lead says "just read the code." You spend a week feeling lost. You want to contribute to an open-source project. The repo has no architecture docs. You don't know where to start. You're doing a code review on a PR that touches 15 files. You have no idea what the blast radius of those changes is. CodeAutopsy solves all three. The interesting engineering problems The hardest part wasn't the AST parsing — it was keeping it serverless without hitting Vercel's 504 timeout limits, while making the AI analysis feel instant. The Serverless Timeout Hack: Doing AST extra

🗓️ This Week Completed two more sections of the SwiftUI tutorial 🦾 As I continue working through the tutorial, I can feel my understanding of SwiftUI fundamentals becoming more solid 🔥 It was my first time posting a standalone article about reverse engineering📝 If you're interested, feel free to check it out 👇 A Curious Journey Into Reverse Engineering an AI-Generated Python .exe Umitomo Umitomo Umitomo Follow May 26 A Curious Journey Into Reverse Engineering an AI-Generated Python .exe # beginners # reversing # security # python 5 reactions Comments Add Comment 5 min read I started creating UI designs for my future portfolio website in Figma. I was able to roughly sketch out the overall structure of the site, but I also realized how difficult it is to create modern and stylish UI designs. (It really made me realize I don’t have much design sense yet 😂💦) While struggling with the design process, I came across several articles about Figma MCP . That made me interested in exploring how generative AI could help with UI design ideas, so I decided to start researching Figma MCP further. Completed Securing AI Systems room from the AI Security Learning Path on TryHackMe this week🤖 📱 iOS (SwiftUI) Worked through the SwiftUI tutorial and completed "Create an Algorithm for Badges" and "Add inclusive features" 🌐 Web Development Posted my weekly dev log on Dev.to and a standalone article about my first attempt at reverse engineering 📝 Created rough portfolio website UI layouts in Figma Used shadcn/ui component library design templates in Figma Started learning UI design in Figma using community resources 🔐 Security (TryHackMe) Completed Securing AI Systems room (part of the AI Security Learning Path) on TryHackMe. 💡 Key Takeaways 📱 SwiftUI Learning Add inclusive features Learned that SwiftUI automatically adapts UI elements for Light and Dark Mode by default. Learned how to preview and compare Light and Dark Mode layouts in the Xcode canvas. Understood that system-provided sema

Recently, I was working on creating a website on a cloud-based IDE (CodeHS). One night, I was editing, and then when I was done, I simply turned off my monitor and disabled my mouse and keyboard. Then, the next day at school, I continued to work on the website, and made significant changes. When I got home, I made more significant changes, but then realized something very important. What I realized is that when I continued to work on the project at home, the cloud-based IDE hadn't refreshed to the new code, and overwrote the work I did at school when I saved my new work. So, what is the purpose of me telling you this? Is it to say that you should always close your code editor when you're done working, or some other trick to prevent this from happening? No, it definitely is not. After the initial panic, I realized that the cloud-based IDE had a history section, which has a detailed log of every change that happened to every file. I then looked back at my old copy, copied the changes, and put them back into my new code. Now, imagine you aren't using a cloud-based IDE, and your just editing a file on your computer, in an IDE, or simply your terminal. To clarify, Git is not cloud-based, it is local, (sitting on your computer), that connects to the cloud (GitHub). What happens when something breaks? For some environments, this could be catastrophic, and make you loose a lot of work. When you make an update to a file, the last state and all the states before it are gone. But, this isn't the only thing that can happen. When you use a version control system (VCS), like Git, you can always go back to your previous commits, (snapshots of your code). The work I almost lost wasn't very important; that work didn't effect anyone, except myself. Imagine what would happen if I was working on something more important, or even just working on something for a job. Before learning how to manage your project, it helps to understand what Git actually is. Git is overwhelmingly the most po

Last Friday at 4:30 PM, my product manager walked over: "Hey, can you grab the titles from the Hacker News homepage and send me an Excel file?" I thought: That's it? Five minutes tops. Two hours later, I was still debugging CSS selectors. How Things Spiraled Out of Control Step 1: Initialize the Project mkdir hacker-news-scraper && cd hacker-news-scraper npm init -y npm install puppeteer Hit enter, waited three minutes. Puppeteer needs to download a full Chromium browser — over 200 MB. I stared at the progress bar and started questioning my life choices. Step 2: Write the Code "It's just a document.querySelectorAll , right?" That's what I thought. Then I opened my editor: const puppeteer = require ( ' puppeteer ' ); ( async () => { const browser = await puppeteer . launch ({ headless : true , args : [ ' --no-sandbox ' , ' --disable-setuid-sandbox ' ] }); const page = await browser . newPage (); try { await page . goto ( ' https://news.ycombinator.com ' , { waitUntil : ' networkidle2 ' , timeout : 30000 }); await page . waitForSelector ( ' .titleline > a ' , { timeout : 10000 }); const titles = await page . evaluate (() => { const items = document . querySelectorAll ( ' .titleline > a ' ); return Array . from ( items ). map ( el => ({ title : el . textContent , url : el . href })); }); console . log ( JSON . stringify ( titles , null , 2 )); } catch ( err ) { console . error ( ' Scraping failed: ' , err . message ); } finally { await browser . close (); } })(); I counted: 27 lines. And this is the minimal version — no User-Agent spoofing, no retry logic, no proxy support, no concurrency control. Add all of that and you're well past 50 lines. Step 3: Run It node index.js Error: Navigation timeout of 30000 ms exceeded . Switched to domcontentloaded , got past that. But then waitForSelector timed out — because .titleline was a relatively new class name. Hacker News had silently changed it from .storylink at some point, and nobody sent me the memo. Step 4: Debug Set head

Recently, Apache SeaTunnel welcomed several talented and highly motivated new Committers, and Wang Xuepeng is one of them. As a long-time contributor, Wang Xuepeng’s promotion to Committer was no coincidence. Over the years, he has quietly contributed a tremendous amount to the community, and everyone has witnessed his dedication. From first stepping into the open-source world to becoming a Committer of an Apache top-level project, he has accumulated plenty of stories and valuable insights along the way. What inspired his journey? What experiences and lessons does he want to share with the community? Let’s take a closer look at this exclusive interview with him! Personal Introduction Interview Transcript How long have you been involved in open source? What attracts you to open source? I started getting involved in open source in 2023. What attracts me most is the sense of achievement when the code I write can actually be used within the industry. When did you start contributing to SeaTunnel? What was the trigger? I joined WhaleOps in 2023, which was also when I first started engaging with open source. Now that you’ve been elected as a SeaTunnel Committer, could you summarize your contributions to the community, including both code and non-code contributions? Most of my major feature PRs have focused on building SeaTunnel’s metadata capabilities. When running SeaTunnel jobs and writing job configurations, users often need to manually enter datasource connection information. For file-based tasks, users also need to manually define field mappings. To address these issues, I designed an SPI interface called MetadataProvider . The interface mainly exposes two methods: Map<String, Object> datasourceMap(String connectorIdentifier, String metaDataDatasourceId); Optional<TableSchema> tableSchema(String metaDataTableId); Previously, some users in the community mentioned that datasource usernames and passwords were stored in Nacos with read-only access permissions. In scenario

That "authenticity of host can't be established" message isn't just noise. Here's what's actually happening — and why blindly typing "yes" is a security mistake. Every developer has seen this: The authenticity of host 'example.com (203.0.113.1)' can't be established. ED25519 key fingerprint is SHA256:abc123xyz... Are you sure you want to continue connecting (yes/no/[fingerprint])? Almost everyone types yes without reading it. Then they move on. This message is SSH trying to protect you from one of the most dangerous attacks in network security: the man-in-the-middle attack. Understanding what's happening here — and what the ~/.ssh/known_hosts file actually does — will change how you think about every SSH connection you make. The Problem SSH Is Solving When you connect to ssh user@example.com , how do you know you're actually talking to example.com ? You can't rely on the IP address — IP addresses can be spoofed or rerouted. You can't rely on DNS — DNS can be poisoned. You can't rely on the network path — traffic can be intercepted at any point between you and the server. Without verification, an attacker positioned between you and the server could intercept the connection, pose as the server, decrypt everything you send, re-encrypt it, and forward it along. You'd type your password or authenticate with your key and never know the attacker saw every keystroke. This is a man-in-the-middle (MITM) attack . It's not theoretical. It happens on compromised networks, corporate proxies, malicious Wi-Fi hotspots, and misconfigured infrastructure. SSH's defense is host key verification . Every SSH server has a unique cryptographic identity — its host key. Before you exchange any sensitive data, the server proves it holds the private key corresponding to a public key you've previously verified. If the keys don't match, SSH warns you — loudly. What a Host Key Actually Is When OpenSSH is installed on a server, it automatically generates a set of host key pairs. These live in /etc

AI Glossary: A to Z An A-to-Z glossary of AI terms, created with help from AI itself. Because in 2026, the best way to study AI is apparently to ask AI itself. 🤣 Written for beginners and practitioners alike. Each term includes a plain English definition and a real-world example. Quick Navigation A · B · C · D · E · F · G · H · I · J · K · L · M · N · O · P · Q · R · S · T · U · V · W · X · Y · Z ↑ Back to top A Term Definition Example Agent (AI Agent) An AI system that perceives its environment, makes decisions, and takes autonomous actions to achieve a goal A coding agent that writes, runs, and debugs its own code without human intervention AGI (Artificial General Intelligence) A hypothetical AI that can match or exceed human-level intelligence across any task — does not yet exist Often cited as a long-term goal by companies like OpenAI and DeepMind AI (Artificial Intelligence) The field of computer science focused on building machines that can perform tasks normally requiring human intelligence ChatGPT writing an essay, an algorithm detecting cancer in X-rays AI Ethics The principles and practices for developing and deploying AI in ways that are fair, transparent, and safe Auditing a hiring algorithm to ensure it doesn't discriminate by gender or race AI Safety The field dedicated to ensuring AI systems remain reliable, controllable, and beneficial as they grow more capable Research into preventing AI from pursuing goals that harm people Alignment The challenge of ensuring an AI system's goals and behaviour match what its designers and users actually intend Preventing a powerful AI from optimising for a metric in a way that causes unintended harm Annotation The process of labelling raw data so it can be used to train supervised learning models Humans drawing bounding boxes around cars in images to train a self-driving model API (Application Programming Interface) A defined interface that lets software systems communicate with each other Calling the OpenAI API to

Most trading ideas start as vague thoughts. "Buy when RSI is oversold and price bounces from support." It sounds reasonable. But the moment you try to test or automate it, the ambiguity becomes obvious. What exactly counts as oversold? How is support defined? What qualifies as a bounce? When do you exit? Without precise answers, the idea cannot be tested, measured, or executed consistently. This gap between intuition and execution is exactly what no-code strategy builders are designed to close. Why vague trading ideas fail Most traders think in concepts rather than rules. "Buy the dip." "Trade strong momentum." "Enter when the trend looks healthy." These ideas feel intuitive, but they are unusable in practice unless translated into explicit logic. Without clear definitions, you cannot backtest a strategy, cannot repeat decisions consistently, and cannot diagnose why results change over time. Ambiguity leads to second-guessing. Second-guessing leads to inconsistent execution. Inconsistent execution makes performance impossible to evaluate. What a no-code strategy builder actually does A no-code strategy builder is a visual system that forces clarity. Instead of writing code, you select indicators, define conditions, combine logic using AND/OR rules, specify entries and exits, and then test the strategy on historical data. Conceptually, it works like assembling building blocks. Each block represents a condition such as "RSI below 30" or "price above moving average." When combined, those blocks form a complete, testable trading system. The key benefit is precision. From idea to testable strategy The transformation follows a predictable workflow. You begin with a loose idea, such as buying when a stock is oversold and starting to recover. You then break that idea into components. What defines oversold? What signals recovery? How do you enter? How do you exit? How much do you risk? Once those questions are answered, the idea becomes a set of explicit rules. For example,

Introduction In my previous IAM article we learnt basics of IAM and how to create Users, Groups and attach Policies. You can refer here: https://dev.to/kadhamvj23/aws-identity-and-access-management-explained-for-beginners-cn7 After setting up secure access to our AWS account using IAM, the next question we mostly have is where do we actually run our application? The answer is Amazon EC2 - Elastic Cloud Compute. EC2 is one of the most widely used AWS services and understanding it well is essential for anyone starting their cloud journey. In this article we will cover what EC2 is, why it exists, the different types of instances, pricing models, Regions and availability Zones and finally hands-on walk through of creating your first EC2 instance. Breaking Down the Name -EC2 Let us understand what each word in the name actually means: Elastic --> In AWS you will notice many services have this prefix "Elastic". The reason is simple. Whenever AWS provides a service that can be scaled up or scaled down based on our needs, that service is called Elastic . With EC2 you can increase resources when traffic is high and decrease them when the traffic is low. So in simple terms EC2 = A virtual server on the cloud that you can resize anytime. Cloud: EC2 runs on AWS's public cloud infrastructure, meaning the servers are owned and managed by Amazon across the world. Compute: The word compute means you are asking AWS to provide you CPU, RAM and Disk - basically a virtual machine or server that can run your applications. How does EC2 actually work? When you request a Virtual server from AWS, here is what happens behind the scenes: You request a virtual machine on AWS ⬇️ request goes to a Hypervisor(a software layer sitting on top of physical servers that creates and manages VMs) ⬇️ Hypervisor creates your VM ⬇️ You get the access to your EC2 instance You never touch any physical hardware. AWS manages all of that for you. Why use EC2? Imagine your company wants to host an application. T

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Discover how to use VS Code to interact with GitHub and maintain your projects. The post GitHub for Beginners: Getting started with Git and GitHub in VS Code appeared first on The GitHub Blog .