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This is the first article in a four-part series where I document how I turned a 10€/month VPS into a production-grade platform hosting my portfolio, a university group webapplication and a SaaS product, all isolated from each other with Kubernetes. In this part, we take a fresh Ubuntu server and lock it down properly before installing anything else. Why bother with hardening? The moment your VPS gets a public IP address, it starts receiving attacks. Not "might receive", it starts . Within minutes, automated bots will probe port 22, try root:root , admin:admin123 and thousands of other credential combinations. If you skip this step and jump straight to deploying your apps, you are building on sand. The good news: an hour of work is enough to eliminate the vast majority of these threats. Here is what we will set up: A non-root user with sudo privileges SSH key authentication, with passwords and root login disabled UFW as a simple, effective firewall Fail2ban to ban brute-force attackers automatically Automatic security updates What you need A fresh VPS running Ubuntu 24.04 LTS or newer. I use a Hostinger KVM 2 (2 vCPU, 8 GB RAM, 100 GB NVMe), but any provider works: Hetzner, DigitalOcean, OVH, Contabo. The root password or SSH key your provider gave you. A terminal on your local machine (macOS, Linux, or WSL on Windows). Throughout this tutorial, replace YOUR_SERVER_IP with your server's IP address and deploy with the username you want to use. Step 1: First login and system update Connect as root for the first and last time: ssh root@YOUR_SERVER_IP Update everything before touching anything else: apt update && apt upgrade -y apt autoremove -y If a kernel update was installed, reboot now: reboot Wait a minute, then reconnect. Step 2: Create a non-root user Working as root is like driving without a seatbelt: fine until it isn't. One mistyped rm -rf and the party is over. Create a dedicated user: adduser deploy Choose a strong password (you will still need it for sudo ,

Banking software cannot afford to be casual about anything. Every transaction needs to be verified, logged, protected from tampering, and traceable if something goes wrong. This is exactly the kind of environment where NestJS quietly shines, since its architecture was built around structure and discipline from the start, not added on as an afterthought. Financial institutions and fintech companies increasingly choose NestJS for banking applications, investment platforms, and trading systems, largely because it gives teams a consistent, testable structure for handling something as sensitive as money moving between accounts. Here is what that actually looks like underneath. Why structure matters more in banking than almost anywhere else In most applications, a messy folder structure or inconsistent error handling is annoying. In a banking application, it is a liability. If five different developers write five different ways of validating a transaction, you end up with five different ways something could slip through unnoticed. NestJS solves this by enforcing a consistent pattern across the entire application, modules, controllers, providers, all following the same shape no matter who wrote them. A new developer joining a banking backend built with NestJS already knows where to look for validation logic, where authorization happens, and where a transaction actually gets processed, because the framework itself dictates that structure. Guards, the first line of defense Every request that touches a bank account should be verified before it does anything else. NestJS handles this through guards, which run before a request ever reaches your actual business logic. @ Injectable () export class TransactionAuthGuard implements CanActivate { canActivate ( context : ExecutionContext ): boolean { const request = context . switchToHttp (). getRequest (); const user = request . user ; if ( ! user || ! user . isVerified ) { throw new UnauthorizedException ( ' Account verification req

Alright, real talk moment. Last week I watched someone with zero coding background build a fully...

Andy Brinkmeyer shares how engineering leaders and architects can use Rust to build failure-proof systems. Moving beyond memory safety, he explains how ownership, enums, and the typestate pattern embed complex runtime protocols into compile-time checks. Learn to eliminate entire classes of bugs, manage real-world resources safely, and maximize codebase robustness effortlessly. By Andy Brinkmeyer

Baek, a 35-year-old manager at the South Korean semiconductor titan SK Hynix, was enrolled in Sunoo, a matchmaking company based in Seoul, a year ago. In a move typical of anxious South Korean parents, his mother signed him up, hoping to find a good wife for her son. Lately, says Baek (who asked to be…

Distributed backups, cyber resilience, and a half-million records are preserving Palestinian history beyond any single building or border.

I had a /image endpoint capped at 3 requests per minute. Simple express-rate-limit, keyed by IP,...

The Model Context Protocol team has promoted its Enterprise-Managed Authorisation extension to stable status, adding a centralised way for organisations to control access to MCP servers through their identity provider. The project states the aim is to replace per-server consent prompts with a zero-touch flow in which users sign in once and then access approved servers without further setup. By Matt Saunders

GitHub热门项目 | A Python library for building AI agents that leverage the full power of Google Antigravity. | Stars: 2,250 | 21 stars today | 语言: Python

When a bug is fixed, most teams retest the exact failure path once and move on. That is understandable, but it leaves a gap: the team learned something from a real failure, then failed to turn that learning into reusable regression coverage. Here is a lightweight template I use for turning resolved bugs into regression test cases that can be copied into a spreadsheet, Jira, TestRail, Qase, Xray, Zephyr, or any other QA workflow. The CSV fields For a bug fix regression test, I like these columns: Test ID Bug ID Feature Area Regression Scenario Original Failure Preconditions Test Data Steps Expected Result Negative Check Priority Regression Risk Test Type Automation Candidate Notes This is enough structure to make the test reusable without turning every bug fix into a heavyweight test plan. Example bug Bug ID: BUG-1842 Bug title: Non-admin users could resend workspace invitations. Original failure: A workspace member could open Pending Invitations and click Resend, even though only owners and admins should be allowed to resend invitation emails. Fix summary: The resend invitation action now checks the user's workspace role before sending the email. Example regression test case Test ID: REG-BUG-1842-001 Feature Area: Workspace invitations Regression Scenario: Workspace member cannot resend a pending invitation. Preconditions: Workspace has at least one pending invitation. Test user is a workspace member, not an owner or admin. User is logged in. Steps: Log in as the workspace member. Open Workspace Settings. Go to Pending Invitations. Locate the pending invitation. Check whether the Resend action is visible or available. If the action can be triggered through the API, attempt the resend request. Expected Result: The member cannot resend the pending invitation. The UI hides or disables the action, and the API rejects unauthorized resend attempts. Negative Check: Confirm that an owner or admin can still resend the invitation if product rules allow it. Priority: High Regr

With a Real CI Automation Example Loop Engineering is suddenly everywhere, and honestly, I wanted to understand it properly instead of just repeating the buzzword. The simplest way I can explain Loop Engineering is this: it replaces me as the person constantly prompting the agent. Instead of me manually noticing a problem, deciding what it means, writing the next prompt, and pushing the process forward, I design a system that keeps moving on its own until it reaches the outcome I want. That is the whole point of Loop Engineering. I stop acting like the operator and start acting like the system designer. To make that idea concrete, I built a practical software engineering workflow around CI failures. Whenever a GitHub Actions CI run fails, the system automatically classifies the failure, creates a Jira bug for real issues, sends a Slack notification, and records the outcome so it does not process the same failure twice. What Loop Engineering actually means Early AI workflows were mostly linear. I would give a prompt, the model would return an answer, and if the answer was incomplete or wrong, I would jump back in and prompt again. That worked, but it kept me trapped inside the process. Loop Engineering changes that dynamic. I am no longer the person babysitting each step. I build an autonomous loop that can observe, decide, act, and persist state. The system keeps iterating until the task is done, without needing me to micromanage it. That distinction matters. In a normal prompt based workflow, the human is still the glue. In Loop Engineering, the human creates the machine, and the machine runs the loop. The five building blocks of Loop Engineering When I break down Loop Engineering, I think of it as five core building blocks working together. 1. Automations These are the event driven triggers that start the whole system. They are the heartbeat of the loop. Something happens, and the automation fires. Without this, nothing starts. 2. Skills Skills give the agent stru