标签：#ev

Handling dates in JavaScript is notoriously error-prone. While ECMAScript's native Date object has well-documented pitfalls—uncontrolled mutability, 0-indexed months, and automatic local-timezone conversions—there is an even larger blind spot in existing libraries like date-fns , dayjs , and luxon : non-Gregorian calendar systems and regional legal date semantics. Global and regional enterprise applications (e.g., banking, fintech, tax compliance, healthcare, public sector, and international travel) frequently operate under official non-Gregorian legal rules: 🇹🇭 Thai Buddhist Era ( พ.ศ. = CE + 543) with official government numbering and Royal Gazette formatting presets. 🇯🇵 Japanese Imperial Era (Reiwa 令和, Heisei 平成, Showa 昭和) with exact historical day-of-event rollover boundaries (e.g., May 1, 2019 Reiwa 1 Gannen). 🇹🇼 Taiwan Minguo (民國紀年) used across municipal and legal filings. 🇸🇦 Islamic Hijri (Astronomical Umm al-Qura, Islamic Civil, and Tabular systems). 🇮🇷 Persian / Solar Hijri (Jalali Khayyami 33-year astronomical leap cycle). 🇮🇳 Indian National Saka Calendar adopted as the official civil calendar of India. To solve this without bloating runtime bundles, dragging in heavy astronomical dependencies, or resorting to loose string parsing and any , we engineered Chronera — an open-source, zero-dependency date and multi-calendar engine written in strict TypeScript. In this deep dive, we'll examine the architectural design decisions, mathematical foundations, and type-level techniques used to model complex multi-calendar domains safely. 1. The Architectural Dilemma: Monolithic Objects vs. Tagged Primitives Most date libraries wrap a native timestamp inside a single monolithic object. The instant you create a date to represent someone's birth date (e.g., 1995-05-15 ), the engine binds it to an hour, minute, second, and UTC timezone offset. When that object is serialized to JSON or transferred across servers in different timezones, classic off-by-one errors happen: //

JupyterGIS is a GIS-focused extension for Jupyter notebooks. The recent 0.16 release enhances collaborative features, real-time editing, and support for large-scale data processing, including remote sensing. It introduces better visualisation tools and extends compatibility to R users. Community feedback highlights practical concerns and a desire for improved portability. By Olimpiu Pop

useEffect fired twice, on mount, every single time, in development only. The API call inside it — a POST that created a resource — ran twice, and for about a day we had duplicate records showing up in a table that should have had exactly one insert per page load. The first reaction, and why it was wrong The instinct is to assume a bug — a rerender loop, a missing dependency, something actually broken. React 18's Strict Mode, in development, deliberately mounts, unmounts, and remounts every component once, specifically to surface effects that aren't properly cleaned up. It's not a bug in your code causing a double-fire; it's a bug in your code being caught by a feature built to catch exactly this. useEffect (() => { console . log ( ' mount ' ); // logs twice in dev, once in production const subscription = subscribeToUpdates (); // no cleanup — this is the actual problem Strict Mode is surfacing }, []); Production builds don't do this double-invocation — it's development-only, and specifically Strict-Mode-only, which is why the duplicate inserts we saw locally would eventually have shown up in production too, just less predictably, under a race condition instead of a guaranteed double-fire. Why this is a feature and not noise to suppress An effect that safely tolerates being mounted, torn down, and mounted again is an effect that correctly declares its dependencies and cleans up after itself — which is exactly the property you need for effects to behave correctly under React's concurrent features generally, not just under Strict Mode specifically. The double-invocation in development is a cheap, automatic test for that property, running on every single page load without you writing a test for it. The actual fix useEffect (() => { const subscription = subscribeToUpdates (); return () => subscription . unsubscribe (); // cleanup makes remounting safe }, []); For our specific case — a POST that shouldn't fire twice regardless of mount behavior — the deeper fix was recogn

TL;DR — I put an AI code reviewer on a pull request written by an AI coding agent. On the default setting it found nothing. On the strict setting it found a real vulnerability. And the patch it offered would have quietly broken every negative number in the exported file. I ship small browser tools written by Claude Code, and I am not a good enough reviewer to catch a security bug in code I did not write. That is the awkward kind of gap: the code looks fine, the page works, the tests pass. So I installed CodeRabbit on the repository and gave it something real to read: a CSV export for a pricing calculator. One row per material line, then other costs, total cost, selling price, profit, margin. About sixty lines of vanilla JS. My own checks passed first — a static site audit, plus a headless browser run of the tool, 14 of 14. Round 1: silence No actionable comments were generated in the recent review. That is the default. CodeRabbit ships a review profile called CHILL , tuned not to nag. For a team drowning in review comments that is probably right. For someone who cannot fully audit their own code, silence is the least useful answer available. So I committed a config file: # .coderabbit.yaml reviews : profile : assertive Same commit. Same diff. Same reviewer. Only the setting changed. Round 2: a real bug, checked the hard way The strict pass flagged CSV formula injection (CWE-1236) , and it was right. A spreadsheet treats a cell that begins with = , + , - or @ as a formula. Name a product =1+1 , export it, and the number two appears in the file the other person opens. Pick a nastier formula and it stops being a curiosity. My csvCell() escaped quotes and commas correctly and did nothing at all about this. What surprised me was how it checked. Folded into the comment was a shell command it had actually run against the repo — a ripgrep over every place a product name or unit flows into the exporter — to see whether something upstream already sanitised the value. It did n

An eight-frame animation does not have a fixed duration. At 8 fps it lasts one second; at 12 fps it lasts two-thirds of a second; at 16 fps it lasts half a second. Before drawing or generating more frames, check whether the problem is missing poses or the time each pose stays on screen. We maintain FrameSprite, a browser workspace for game assets. This is a timing and export note, not a claim that a particular frame count makes AI animation reliable. The equations work with hand-drawn sprites too. Three numbers that are easy to mix up Source FPS describes how a recording was sampled. Frame count is the number of entries you put in an animation. Playback FPS controls how fast those entries advance in the game. A 24 fps source video can provide eight selected poses that you play at 12 fps. You do not need to preserve every source frame. For equal holds, forward playback and a speed multiplier of 1: duration_seconds = frame_count / playback_fps frame_hold_ms = 1000 / playback_fps fps_for_target = frame_count * 1000 / target_duration_ms Same eight frames Hold per frame Full loop 8 fps 125 ms 1.000 s 12 fps 83.333… ms 0.667 s 16 fps 62.5 ms 0.500 s You changed the cadence without changing one pixel of the sprite sheet. A test you can reproduce Use the public eight-frame sample . Keep the same frames, order, canvas and pivot for all three trials. Change only playback FPS between 8, 12 and 16. Check the animation alone at its intended game size. Run it beside actual movement or attack timing. If cadence improves but a foot or weapon still jumps, inspect the missing phase instead of raising FPS again. If every frame jumps by a small amount, inspect canvas and pivot alignment. If the pause happens only at the seam, look for an accidental duplicate endpoint. The sample makes the arithmetic test repeatable. It is not evidence that eight frames is the right budget for every character or action. Do not accumulate rounded timestamps At 24 fps, one hold is 41.666… milliseconds. St

The Hidden Cost of React Server Components When React Server Components (RSC) were first introduced, they were hailed as the solution to the "bundle bloat" problem. By shifting rendering logic to the server, we promised users faster initial page loads and a cleaner separation of concerns. However, after deploying RSC at scale in production environments throughout 2026, many teams are discovering a harsh reality: RSC is not just a syntax update; it is a fundamental shift in architectural paradigm that punishes lazy design. If you aren't careful, your "performance-first" architecture can quickly become a massive bottleneck. Let’s dive into three critical lessons learned from the trenches of production RSC development. 1. The Sequential Waterfall Regression In the traditional client-side React world, we were accustomed to useEffect data fetching patterns. Moving to an async/await model in Server Components feels intuitive, but it introduces the risk of sequential waterfalls that block your entire render pipeline. The Anti-Pattern Consider a scenario where you need to fetch user profile data and their associated posts. A naive implementation might look like this: // ❌ The Waterfall: This will block the render until both finish async function Profile ({ id }) { const user = await getUser ( id ); const posts = await getPosts ( id ); return < ProfileView user = { user } posts = { posts } /> ; } In this example, the server must wait for getUser to resolve before even initiating the getPosts request. This doubles your latency. The Optimization: Parallelism and Streaming To fix this, you must leverage Promise.all to initiate requests concurrently. Even better, you should push these fetches into separate sibling components to allow React to stream the results as they arrive. // ✅ The Optimized Approach function Profile ({ id }) { return ( <> < Suspense fallback = { < UserSkeleton /> } > < UserComponent id = { id } / > < /Suspense > < Suspense fallback = { < PostsSkeleton /> }

How we built a reliable pipeline to split long texts for LLM translation without losing context or breaking the bank At LectuLibre, we translate entire books using Claude. The challenge: a 300-page book is roughly 90,000–120,000 words, which translates to 120,000–160,000 tokens. While Claude 3 models have a 200k context window, sending an entire book in one API call is impractical. It's slow, expensive, and often degrades translation quality due to attention dilution. We needed a robust chunking strategy that preserved context and stayed within token limits. The Problem: One Book, Too Many Tokens When we first started building LectuLibre, we naively assumed we could just pass the whole book to Claude and get a translation back. We quickly hit three walls: Rate limits : A single request with 150k tokens triggered API timeouts and 429 errors. Cost : Even if it worked, processing 150k tokens per request with Opus would cost over $13 per book, and most of the input would be wasted on repeated context. Quality : Long contexts tend to make the model "forget" early chapters, leading to inconsistent character names and terminology. Clearly, chunking was necessary. But how do you split a book without losing narrative flow? First Attempt: Naive Splitting by Paragraphs Our initial approach was simple: split the text into chunks of roughly 10,000 tokens by paragraphs. We used a regex to split on double newlines and then concatenated paragraphs until we hit the token limit. import re def split_into_paragraphs ( text : str ) -> list [ str ]: return re . split ( r ' \n\s*\n ' , text ) def chunk_by_paragraphs ( paragraphs : list [ str ], max_tokens : int = 10000 ) -> list [ str ]: chunks = [] current_chunk = [] current_tokens = 0 for para in paragraphs : # Estimate tokens using character count / 4 (quick and dirty) para_tokens = len ( para ) // 4 if current_tokens + para_tokens > max_tokens and current_chunk : chunks . append ( ' \n\n ' . join ( current_chunk )) current_chunk = []

Are you pushing your body to the limit or just driving it into the ground? In the world of high-performance athletics and biohacking, Heart Rate Variability (HRV) has become the "North Star" for recovery. But raw numbers from your Garmin or Oura Ring only tell half the story. To truly understand the relationship between sleep quality , exercise load , and stress perception , we need more than a dashboard—we need a predictive pipeline. In this tutorial, we will build a multi-dimensional analysis system using Scikit-learn , LSTM (Keras) , and the Terra API to predict overtraining risks. By the end of this guide, you'll know how to turn messy wearable data into actionable health insights. The Architecture: From Bio-Signals to Insights To handle the complexity of time-series data (HRV) and categorical features (activity types), we use a hybrid approach. We use Random Forest to identify which lifestyle factors impact recovery the most and LSTM to predict future HRV trends based on historical sequences. graph TD A[Garmin / Oura Ring / Apple Watch] -->|Webhook| B(Terra API) B --> C{Data Preprocessing} C -->|Feature Engineering| D[Random Forest Classifier] C -->|Sequence Processing| E[LSTM Neural Network] D -->|Feature Importance| F[Stress Analysis Engine] E -->|Trend Prediction| F F --> G[FastAPI Endpoint] G --> H[End User Dashboard] Prerequisites To follow along, you'll need: Terra API Keys : For unified access to wearable data (Garmin, Oura, etc.). Tech Stack : Python 3.9+, Scikit-learn, Keras/TensorFlow, and FastAPI. The Mindset : A passion for Health Tech and Wearable Data Science . Step 1: Ingesting Data with Terra API Standardizing data across different wearables is a nightmare. The Terra API acts as an abstraction layer, giving us a unified JSON structure for heart rate, sleep, and activity. import requests def get_wearable_data ( user_id , start_date ): # Using Terra API to fetch aggregated daily health data url = f " https://api.tryterra.co/v2/daily?user_id= { use

Hi DEV! I got tired of free PC cleaners that bundle a registry cleaner nobody needs, count up a scary number of "issues", and then dangle a paid version at the end. So I wrote the boring version. CleanGeek finds and clears: Temp folders, user and system Browser caches across the installed browsers Windows Update leftovers and Delivery Optimisation cache Crash dumps and error reports Thumbnail cache and font cache Recycle Bin, if you tick it Every item shows what it is and how much space it is worth. Nothing is deleted until you press the button, and you can untick anything you want to keep. Why I built it There is no registry cleaner in it and there never will be. Cleaning the registry has not meaningfully sped up a Windows machine in about fifteen years, and the risk of breaking something is real. Same reason there is no "optimise your PC" button. The tool does one job and tells you exactly what it did. The other reason is the business model. Free cleaners generally are not free, they are a funnel. CleanGeek has no paid tier to funnel you into, no upsell screen, and no telemetry. Tech stack .NET 8, net8.0-windows Avalonia for the UI, with Avalonia.Desktop and Avalonia.Themes.Fluent No third party cleanup engine, the scanning is all in the app Avalonia was the right call. WinForms would have been quicker to get moving but the styling story is grim, and WPF ties you down harder than I wanted. Honest caveat The installer is not code signed yet, so SmartScreen may warn on first run. I am sorting that out. If that is a dealbreaker for you, fair enough. Links Site: https://techygeekshome.info/cleangeek/ Source: https://github.com/techygeekshome/CleanGeek Video: https://youtu.be/Z2s2p3nkIvY If it misses something obvious on your machine, tell me and I will add it.

I built a tool that draws architecture diagrams from a repository, where every edge cites the file, line and commit it came from. Then I ran it against thirteen repositories it had never seen, and every single one of them found something wrong with it. There were thirteen. These are the ones worth writing down. The list says nothing about those codebases. It says something about testing: a tool that reads other people's repositories has to be tested against other people's repositories, and there is no substitute. The rule the tool works by Nothing is drawn that cannot be cited. Every edge in the output carries the file, the line and the commit that justifies it — click an arrow, see the import statement. If a reference cannot be resolved to something in the repository, it is not quietly dropped and it is not guessed at. It is reported as a gap. That second half is what made these bugs findable. A tool that silently drops what it cannot resolve looks perfect and is useless. A tool that reports gaps by name and count tells you, loudly, every time it is confused. Java: a library sharing your package prefix is not you Guava declares com.google.common . Truth is a separate library, and it lives in com.google.common.truth . My resolver matched on package prefixes, so Truth looked like Guava's own code, and every reference to it became a gap against a package Guava does not contain. 834 false gaps — 28% of the repository. The fix is to require the next path segment to look like a type before peeling, because com.google.common.truth.Truth peels to a package and com.google.common.collect.ImmutableList peels to a class, and those are different shapes. Java: a file importing its own nested type Java requires the import for a nested enum constant even inside the same file. Treating that as a dependency has you drawing an arrow from a file to itself. It accounted for all 137 remaining gaps on Spring Boot and all 34 on Guava. Java: static imports point one segment too deep import

Solid-Vue is a lightweight Vue + Vite framework for small and growing businesses. File-based routing, a built-in server layer powered by h3, and zero extra config to wire together. Features File-based routing — every file in src/pages becomes a route automatically, via unplugin-vue-router. A server, built in — src/server/api holds your API endpoints, served through h3 alongside your frontend. One dev server, one deploy. Vite underneath — instant startup and near-instant HMR. State management ready — Pinia is wired in out of the box. Extensible via add-ons — install Tailwind CSS, icon sets, form validation, i18n, and more with the companion solid-vue-cli. Quick start Don't install this package directly — scaffold a new project instead: npm create solid-vue@latest my-app cd my-app npm install npm run dev Usage vite.config.ts import { defineConfig } from ' vite ' import { solidVue } from ' solid-vue ' export default defineConfig ({ plugins : [ solidVue ({ mode : ' spa ' }) ] }) src/main.ts import { createSolidApp } from ' solid-vue/client ' import App from ' ./App.vue ' const { app , router } = createSolidApp ( App ) router . isReady (). then (() => { app . mount ( ' #app ' ) }) src/server/api/hello.ts import { defineEventHandler } from ' solid-vue/server ' export default defineEventHandler (() => { return { message : ' Hello from Solid-Vue! ' } }) Plugin options solidVue ({ mode : ' spa ' , // 'spa' | 'ssr' | 'ssg' — default: 'spa' apiPrefix : ' /api ' , // prefix for file-based API routes — default: '/api' optimizeCWV : true , // inject Core Web Vitals meta/preconnect tags — default: true }) Package exports Entry Use solid-vue The Vite plugin ( solidVue ), used in vite.config.ts solid-vue/client createSolidApp() — bootstraps Vue, Vue Router, and Pinia solid-vue/server Re-exported h3 utilities ( defineEventHandler , readBody , useSession , etc.) for your API routes Add-ons Add optional integrations to an existing project with the CLI: npx solid-vue add tailwind npx so

The Shift from Syntax to “Vibe Coding” Writing code used to be the bottleneck. Now, code is free and that’s precisely the problem. If you had told me Five years ago that my terminal would routinely spin up background execution agents, draft full-stack features, and push PRs before I finished my morning coffee, I would have assumed you were selling a tech startup pipe dream. Back then, GitHub Copilot was a neat trick: an glorified tab-completion tool that occasionally saved you from typing out a boiler-plate fetch request or regex string. Fast forward to today, and we’ve entered the era of “Vibe Coding.” You state intent in natural language. You direct agents in your editor. You prompt terminal workflows. Code manifests at conversational speed. You aren’t typing out syntax line-by-line; you’re steering an autonomous orchestra. The developer experience feels almost magical, fluid, and dizzyingly fast. But as the velocity of code creation hits lightspeed, an uncomfortable truth is beginning to surface across engineering teams: the cognitive load of software engineering hasn’t disappeared — it has simply shifted. We traded the friction of writing syntax for the far more taxing chore of evaluating architectural integrity, managing context drift, and catching silent edge-case failures in code we didn’t actually write. Code is easier to generate than ever, but system comprehension is at an all-time low. And nowhere is this trade-off creating more friction than in the middle tier of the software engineering workforce. The “Middle-Tier” Squeeze Senior engineers act as directors; AI handles the grunt work. Where does that leave everyone in between? For decades, the career progression of a software engineer followed a reliable, well-trodden path. You entered the industry as a junior, grinding away on bug fixes, writing unit tests, and building basic CRUD endpoints. Slowly, through hundreds of hours of raw syntax exposure, you built up mental models. You learned how state manag

I built a checker for a configuration directory. The time went not into adding rules, but into deciding what not to add . Things you could detect are easy to think of. That was never the constraint. One false positive is enough to get the tool thrown out A checker is asymmetric. A miss goes unnoticed. The cost is only that you did not learn something you could have. A false positive stops the reader and demands a decision: is this actually wrong? And once someone has been burned, they read every finding with suspicion . Twice, and the tool comes out of CI. So a checker that calls a valid configuration broken is worse than no checker. Better ten rules with no false positives than thirty with one. That is obvious in the abstract and hard in practice, because while you are writing the code, every "oh, I could check that too" pulls in the other direction. No citation, no rule So I fixed one condition for adding a rule: Only check what the official documentation states outright — as an error, as skipped, or as ignored. If the documentation does not say it, the rule does not go in, however wrong the pattern looks. What this buys is that the judgement stops living in my memory. "I'm fairly sure that form was invalid" is not a citation, and my memory goes stale the moment the tool it describes releases a new version. In the implementation, every finding carries its reason: export interface Finding { severity : " error " | " warn " ; file : string ; line ?: number ; /** what is wrong, in one sentence */ message : string ; /** why that can be claimed — includes the source URL */ because : string ; } Making because required is the point. A rule you cannot justify cannot be written , because the type will not let you leave the field out. If no source comes to mind, the rule never gets implemented. The tests enforce it too: for ( const f of findings ) { if ( ! f . because . includes ( " https:// " )) fail ( `no source: ${ f . message } ` ); } One finding without a source URL fai

Architecting Enterprise Angular with Signals: Zoneless Reactivity and 60fps Performance For nearly a decade, Angular relied on Zone.js to intercept asynchronous browser events and trigger top-down dirty checking across the entire component tree. In large enterprise dashboards displaying live telemetry, grid streams, and complex forms, this model leads directly to frame drops and memory leaks. With Angular 19+, fine-grained Signals provide a reactive paradigm where the framework tracks exact DOM dependencies at compile-time and updates only the precise DOM nodes that changed, unlocking 60fps zoneless execution . Architecture & Interview Cheat Sheet Feature Legacy RxJS / Zone.js Angular Signals (Modern) Change Detection Dirty-checks entire component tree Fine-grained single DOM node updates Memory Lifecycle Manual takeUntilDestroyed subscriptions Automatic graph cleanup without memory leaks Derivations Complex combineLatest / switchMap Lazy, memoized computed(() => ...) Zone.js Overhead Monkey-patches all browser async APIs 0 overhead ( provideExperimentalZonelessChangeDetection() ) 1: Clean Reactive State with Signals import { Component , computed , signal , effect , inject } from ' @angular/core ' ; export interface TelemetryPacket { id : string ; latencyMs : number ; status : ' healthy ' | ' degraded ' | ' critical ' ; } @ Component ({ selector : ' app-telemetry-monitor ' , standalone : true , template : ` <div class="card"> <h3>Live Ingestion Monitor</h3> <p>Total Packets: {{ packetCount() }}</p> <p>Average Latency: {{ averageLatency().toFixed(2) }}ms</p> <span [class.badge-warn]="isDegraded()"> {{ isDegraded() ? 'DEGRADED PERFORMANCE' : 'NOMINAL' }} </span> </div> ` }) export class TelemetryMonitorComponent { // Primary Writable Signal readonly packets = signal < TelemetryPacket [] > ([]); // Derived Computed Signals (Memoized, evaluated lazily on read) readonly packetCount = computed (() => this . packets (). length ); readonly averageLatency = computed (() => {

Four algorithms. Same grid. Very different dungeons. I implemented BSP trees, cellular automata, random walk, and room placement, ran each one 20 times on an 80x40 grid, and measured everything: connectivity, open space, path length, speed. The Results Algorithm Open Space Connected Rooms Path Length Speed BSP Tree 42.1% 100% 1.0 105 steps 0.88 ms Cellular Automata 55.8% 0% 15.2 78 steps 52.8 ms Random Walk 35.0% 100% 1.0 73 steps 274.7 ms Room Placement 18.9% 100% 1.0 81 steps 0.29 ms The big surprise: cellular automata never produces a connected map. Zero percent connectivity across 20 runs. Every single cave system has unreachable areas. The Maps BSP Tree (structured rooms, always connected) ################################################################################ ################################################################################ #####.........#####.............###################################....#......## #####.........#####.............##..........##############........#....#......## #####...........................##..........##############....................## #####.........#####.............##..........##############.............#......## #####.........#####.............##..........##############........#....#......## ##########.#######################..........##############........#....#......## ##########.#######################..........################..################## ######..........##################..........################..################## ######..........##################..........################..######..........## ######..........##################..........################..######..........## ######..........##################..........################..######..........## ######.............###############..........################..######..........## ######..........##.###############..........################..######..........## ######..........##.###############..........################..######..........##

Other archives and libraries around the world may also contain genetic traces of past disease outbreaks.

A green test suite is evidence. It is not independent evidence. The current release of badBANANA Threat Observatory passes all 108 automated tests in its own development and CI environments. That tells me the implementation satisfies the assertions I wrote against the conditions I expected. It does not tell me whether an independent developer can check out the same commit in a clean environment and obtain the same result. That distinction matters more than the number 108. The dangerous failure is a believable one The Observatory presents source-backed threat-intelligence records, freshness information, and material-change events. In that kind of interface, an obvious crash is not necessarily the worst outcome. A more dangerous failure is one that looks healthy: An expired cached snapshot presented as current An invalid expiry value treated as usable A failed upstream source displayed as a successful zero-result response Demo or fallback data appearing without explicit disclosure A disabled or offline state silently normalized into success Those failures do not merely inconvenience the user. They change what the interface appears to know. For v1.2.2, the intended behavior is deliberately fail\ closed: Condition Required behavior Cached snapshot has expired Report it as stale Expiry value is invalid Fail closed to stale Source is offline, disabled, or failed Preserve that state Source data is missing or unavailable Do not present a successful zero result state Ingestion requests overlap Enforce the runtime concurrency limit deterministically Feed credentials are configured Keep them server side and absent from client output The test suite exercises these boundaries. The remaining question is whether the release reproduces cleanly outside the environment in which it was built. Passing tests and independent verification are different claims When the source, tests, build assumptions, and execution environment all come from the same maintainer, a successful run demonstrat

If you're prepping for the Certified Kubernetes Application Developer (CKAD) exam, you've probably already found killer.sh, Killercoda, or one of the paid mock-exam platforms. I wanted something different: no account, no cloud dependency, no subscription — just a simulator that runs entirely against my own cluster, so I could rerun the same drills as many times as I wanted without worrying about usage limits. That's CKAD Dojo — free, open source, self-hosted. What it actually does 20 free mock exams, 398 questions, mapped to the official CKAD v1.35 curriculum A 120-minute countdown timer that mirrors the real exam (turns yellow at 15 min, orange at 5, red at 1) An embedded web terminal (ttyd) right next to the question panel — same gesture as the real exam UI, no window-juggling Instant, real scoring: bash functions query the actual state of your cluster against 400+ criteria, question by question. You don't have to wait until the end to know if you got it right. Runs against your own cluster — kubeadm, minikube, or kind (1.28+). Nothing leaves for the cloud. Why "dojo"? Each of the 20 practice sets is themed after a figure from Japanese mythology or the four celestial guardians (Suzaku, Byakko, Genbu, Kirin...). Resources inside each dojo follow the theme, so kubectl get pods genuinely reads like a small story instead of pod-1, pod-2, pod-3. Small detail, but it makes repeated drilling less soul-crushing. The loop Open a dojo — namespaces, workloads and Helm releases get provisioned for you. Scripts are idempotent, so you can rerun them freely. Train in the terminal — question on the left, real shell on the right, resizable divider. Arrow keys to navigate, F to flag a question, collapsible hints if you're stuck. Score whenever you want — not just at the end. Wipe and redo — read solutions.md, clean the cluster, and run the same dojo again tomorrow. The goal is reflex, not memorized answers. It's community-built 14 of the 20 dojos come from contributors — 9 as fully

In what appears to be a follow-up to the mighty “CasiOak,” the rugged Japanese watch brand is expected to release an all-new luxury homage.

Up front: this is my own project, so I'm not exactly neutral here ;-) Where I'm coming from I work completely differently than I did two or three years ago. For most of my career I wanted to write pretty much every line myself, and I was a bit proud of that. That has changed a lot. Instead of programming I now mostly write specifications and review what the AI generates. On the one hand that's great, I can turn new ideas into working software much faster than before. On the other hand there's the risk of stepping into the same traps with AI-generated code again and again. And that's where I noticed something. Every time I started a new project, I found myself explaining the same things to the AI. This goes into a plugin. That stays out of the core. No domain logic in the shell. Please don't invent a third way of doing tabs. The AI would nod, generate something that looked right, and two days later I'd find a slightly different version of the same sidebar with a slightly different bug. There are things I really don't want to explain over and over. A good, preferably deterministic base is getting more important, not less. I don't want to explain proven architectures from scratch every time. I'd rather build on established solutions where I can, ones the AI understands and can just use. So for my new projects I built exactly that, and put it on GitHub as open source. Why Angular? Well, simply because I think it's a great framework and I've had a lot of good experiences with it over the last 10 years. What it is, and what it isn't LoomWeaver is a workbench shell for Angular. Not a component library. Think of the frame VS Code gives you: a rail on the left, sidebars, a top bar, a status bar, and in the middle tabs and panes you can split and drag around. That frame is what most workbench-style products build themselves, every time, slightly differently. LoomWeaver gives you that frame, and your own domain moves in as plugins. The core contains zero domain logic. Even my