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Ever felt like the Next.js App Router is a super cool superpower, but sometimes it feels like we accidentally left a few things behind during the setup? It happens to the best of us! Building with the App Router is incredibly powerful, giving us server first capabilities and an asik developer experience. Yet, with great power comes a few hidden quirks that often sneak past our radar until runtime. Let's dive deep and spot those common pitfalls together, making sure our Next.js apps run smoother than a freshly brewed cup of coffee. The Great Divide Understanding Client versus Server Components One of the biggest paradigm shifts with the App Router is the clear distinction between Client and Server Components. This isn't just a fancy label it dictates where your code runs and what it can access. Forgetting this fundamental difference is a top contender for unexpected errors. Server Components by Default We often forget that, by default, all components in the App Router are Server Components. This is awesome because it means zero client side JavaScript for many parts of our UI, leading to blazing fast initial loads and better SEO. Server Components can directly access server resources like databases, file systems, or environment variables without exposing them to the browser. They run once on the server, generate HTML, and send it to the client. When 'use client' Becomes Our Best Friend The 'use client' directive is like waving a flag saying, "Hey, this component needs to run in the browser!" We use it when a component relies on browser specific APIs like window or document , handles user interaction like click events, or uses React Hooks such as useState or useEffect . The common mistake here is forgetting to add 'use client' to components that need interactivity, leading to build errors or hydration mismatches when the server rendered HTML doesn't quite match what the client expects. We might also accidentally try to import a server side utility into a client compone

The Quest Begins (The "Why") I still remember the first time I walked out of a coding interview feeling like I’d just lost a lightsaber duel. The problem was a simple array‑rotation task, but I dove straight into typing, eyes glued to the screen, and barely said a word. When the interviewer asked, “What are you thinking right now?” I froze, mumbled something about “just trying to get it done,” and watched the seconds tick away. The feedback later? “Great coding skills, but we couldn’t follow your thought process.” That moment stung because I knew I could solve the problem—I just hadn’t learned how to show my thinking. After a few more silent attempts, I realized the interview isn’t a solo boss fight; it’s a co‑op mission where the interviewer wants to see how you navigate the terrain. If they can’t hear your internal monologue, they have no way to gauge your problem‑solving instincts, communication style, or ability to catch mistakes early. I went on a quest for a repeatable, low‑effort way to narrate my thinking without turning the interview into a monologue. What I found was a three‑step verbal framework that felt like unlocking a new Force power—simple, repeatable, and surprisingly effective. The Revelation (The Insight) The technique I now swear by is State → Plan → Execute . At each stage you say out loud exactly what you’re doing, using a tight, repeatable script. It’s not about over‑explaining; it’s about giving the interviewer a clear map of your mind. Here’s the exact wording I use, broken down by phase: State – Clarify the problem, assumptions, and constraints. “Okay, so we need to rotate an array to the right by k steps. I’m assuming k can be larger than the array length, so I’ll use modulo to normalize it. The array can contain any integers, and we should aim for O(n) time and O(1) extra space.” Plan – Outline the high‑level approach before writing a line of code. “My plan is to use the three‑step reversal algorithm: reverse the whole array, then reverse

Cyclosporiasis isn’t the only thing spreading across the US. So is anxiety about getting hit with it.

The company endorsed landmark AI transparency laws in California and New York last year, but its head of US state and local policy says they may already be outdated.

Metron was the Greek word for a measure: the standard you judge a thing against. It gives us metric, and metronome. Pick the wrong metron and everything you count against it comes out wrong, no matter how carefully you count. Ask most teams how they know AI adoption is working, and the answer is some version of: we’re shipping more. More PRs, more tickets closed, more velocity on the board. None of those numbers were ever measuring what people thought they were measuring. They were proxies, and bad ones, for whether the team was creating value. AI didn’t break that. It just made the proxies lie louder. An agent can produce PRs faster than any team can review them. It can close tickets all day. None of that tells you whether the thing built was worth building, or whether it fixed the actual problem, or whether anyone downstream is better off. Burning tokens isn’t the same thing as creating value. It just looks the same on a dashboard built to reward the first thing. The same DORA numbers I pointed at earlier are blunt about it: individual output climbs while delivery throughput and stability drop. Same bottleneck, counted at the wrong end . This is Theory of Constraints in one sentence: speeding up a step that wasn’t the bottleneck doesn’t speed up the system. It just piles more work in front of whatever the real bottleneck is. Implementation used to be the constraint. Now it isn’t. Review is. Coordination is. Making sure everyone agrees on what “done” even means is. Agents made the fast part faster and left the slow part exactly where it was, except now it’s buried under more output arriving to be reviewed by fewer people who understand any of it. Measuring individual speed after that shift is measuring the wrong clock. The team can be faster and worse off in the same quarter. The instinct, when this gets noticed, is to add another skill to the agent. Automate the review step too. Automate the coordination. Whatever’s slow, throw a capability at it. That doesn’t fix

I built Commit Cron , a small GitHub Actions experiment that creates one automated commit every day. The project updates a text file with the latest execution time, commits the change using the github-actions[bot] account, and pushes it back to the repository. View the project on GitHub: Commit Cron How It Works The workflow runs every day at 10:00 AM Asia/Manila time. on : schedule : - cron : " 0 10 * * *" timezone : " Asia/Manila" workflow_dispatch : The workflow_dispatch trigger also lets me run the workflow manually from the GitHub Actions tab. The workflow checks out the repository, creates the bot directory when needed, and updates bot/last-run.txt : mkdir -p bot printf "Last automatic update: %s \n " \ " $( TZ = Asia/Manila date '+%Y-%m-%d %H:%M:%S %:z (Asia/Manila)' ) " \ > bot/last-run.txt The file contains a timestamp similar to: Last automatic update: 2026-07-17 10:03:24 +08:00 (Asia/Manila) After updating the file, the workflow configures the GitHub Actions bot identity and creates the commit: git config user.name "github-actions[bot]" git config user.email \ "41898282+github-actions[bot]@users.noreply.github.com" git add bot/last-run.txt git commit -m "chore: daily automated update" Before pushing, it pulls the latest branch changes with rebase: git pull --rebase origin " ${ GITHUB_REF_NAME } " git push origin "HEAD: ${ GITHUB_REF_NAME } " This helps prevent the push from failing when another commit is added while the workflow is running. Repository Structure . ├── .github/ │ └── workflows/ │ └── daily-commit.yml ├── bot/ │ └── last-run.txt ├── LICENSE └── README.md Why I Built It Commit Cron is a small demonstration of: Scheduled GitHub Actions workflows Manual workflow triggers Automated file updates Bot-generated Git commits Repository write permissions using GITHUB_TOKEN The workflow uses: permissions : contents : write This allows the built-in GitHub token to push the generated commit. Important Note The automated commits only confirm that the work

Roblox's new "Build" feature lets users generate basic games using a single text prompt.

Most "I built a home SOC" posts show you the pretty dashboard and stop there. This one is about what happened after the dashboard loaded — because that's where the real learning was. 🖥️ The setup Three Mac Minis, one control machine, all Ubuntu. Mac Mini 1 runs the Wazuh manager, indexer, and dashboard — the security monitoring core. Mac Mini 2 handles AWS IAM permission scanning (Cloudsplaining, ScoutSuite). Mac Mini 3 is a documentation hub plus a custom vulnerability-intelligence pipeline pulling from the NVD API and NIST's CPRT compliance framework data. A fourth machine, my daily driver, orchestrates all three over SSH. Today's goal: get every machine reporting into Wazuh, so I'd have real, fleet-wide visibility — not just a single monitored box. 🐛 Bug #1: the silent feed stall After installing the Wazuh agent on Mac Mini 3, vulnerability scanning just... sat there. The log showed Initiating update feed process and then nothing. No error, no timeout, no progress. I ruled things out methodically: DNS resolved fine, curl could reach Wazuh's CTI feed endpoint and complete a full TLS handshake, disk space was nowhere close to full, and there were no stale lock files sitting around. Every individual piece of the pipeline worked in isolation — but the feed download itself never completed. The actual fix: upgrade Wazuh from 4.14.5 to 4.14.6. A newer point release fixed whatever was silently stalling the content-updater module. Once upgraded, the feed downloaded, decompressed, and the scanner came alive within minutes. Lesson: when every individual network/system check passes but a specific module still won't progress, check for a version-specific bug before going deeper down the debugging rabbit hole. 🔒 Bug #2: locking myself out with fail2ban Mid-troubleshooting, my control machine's SSH connection to Mac Mini 3 started getting flatly refused — no prompt, no error, just Connection refused. Ping worked fine. Every other port responded. Just port 22, silently dead. fai

If the large, rocky planet is losing helium, then we can infer what is left behind.

Sometimes it feels like keeping your floors clean is one of those never-ending chores, which is why it's nice to have a versatile robot vacuum take it off your hands. The Ecovacs Deebot X11 robovac / mop hybrid is designed to do just that, and it's now $699 ($400 off) at Amazon and directly from […]

BP Ventures is shutting down, ending a nearly 20-year run that was marked by reportedly lackluster returns.

Uber has agreed to acquire Delivery Hero in a $14.8 billion all-stock deal that would nearly double the company’s global footprint and create one of the world’s largest food-delivery platforms outside China.

When the enemy occupies favorable terrain, don't attack head-on. Let them think they're safe, let...

Beehiiv is launching an AI Copilot to help publishers with user growth and analytics.

Disclaimer: NO AI bullshit here. We are in April 2024, I am working in Mexico City and a friend of mine contacts me. She is an architect and she works in Merida. Her and her colleague would like to have a solution to analyze architectural cadastral documents. She told me that she called friend for Mexico and USA and they all say it is impossible. Interested by the challenge I said: "Hold my beer" and I made a POC in 75h of intensive work. However, it was not enough. They had other requirements and getting to this was much more complicated. Complicated but still not impossible. As a Data Scientist, I started to develop a full solution (mostly Computer Vision) to read and analyze automatically specific cadastral documents. I decided to continue working on the project. Hours and hours. There was no money involved. Only challenge. Thanks to that I was able to do everything with the time I wanted. Putting all the passion in my side-project. With time the project grew and grew. But I was prepared for this. I made every part of the code separable, isolated. 6 repositories: aitt-core: core backend + Streamlit app -> Docker image deployment on ECR aitt-core-deploy: service deployment on the server via Ansible aitt-symbol-clf: ML + API -> Docker image deployment on ECR aitt-symbol-clf-deploy: service deployment on the server via Ansible 5 .aitt-infra: Terraform on AWS aitt-platform: Vue.js frontend (private repo) I finally came to a solution for what they needed in more than 625 hours of work. My work contract ended in 2025 in Mexico so I came back to France (I am French, yes I love pain - joke for bilingual). This project made me realized several things: I am capable of doing great things. We all are. The only thing that matters is discipline. This is not some sort of personal development bullshit. This is an advice for life. Einstein said: "I have no special talents. I am only passionately curious." Time is a powerful thing . With time and dedication you can achieve great t

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Roblox is about to let people make games with AI right inside its mobile app, which could make a platform that's already filled with content of questionable quality feel even more overloaded. The company has embraced AI with open arms, including a preview of an ambitious take on AI world models similar to Google's Project […]

X will use Grok AI to better detect stolen content, redirect payouts to original creators, and crack down on engagement bait.

OnePlus promises to continue supporting the phones it has already released.

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