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Every data grid demo looks incredible with twenty rows. The columns line up. The hover state is tasteful. The checkbox has confidence. Someone scrolls three inches and everyone quietly agrees that software has advanced. Then the real product arrives. Fifty thousand rows. Twenty columns. Editable money. A custom status cell. Filters. Sorting. Horizontal scrolling. A user who pastes something suspicious from Excel. A product manager asking whether the total row can stay pinned while the server is slow. That is when a table stops being a table and starts becoming infrastructure. So we built a benchmark. Not a perfect benchmark. Those do not exist. A useful one. What we measured The fixture is intentionally boring: 50,000 deterministic rows 20 fixed-width columns 1,200 by 600 pixel viewport two editable columns sorting filtering virtual scrolling production bundles fresh browser contexts raw samples committed to GitHub No network requests. No paid-only feature tricks. No images. No grouping. No heroic demo code designed to make one library look blessed by destiny. The report measures: JS gzip : reachable JavaScript after gzip Ready median : navigation until the grid adapter mounts and two animation frames pass Scroll settle : one scripted vertical and horizontal jump plus animation frames Mounted cells : body cells in the DOM after the scroll Interaction health : heap, long tasks, estimated FPS, dropped frames Live benchmark: https://vitashev.github.io/react-data-grid-benchmark/ Source and raw samples: https://github.com/Vitashev/react-data-grid-benchmark The part most benchmarks get wrong Not every grid exposes the same surface. For example, MUI X Data Grid Community uses 100-row pagination for this workload. That is a valid product boundary, but it is not the same as continuously virtualizing 50,000 rows. So the ranked tables include only compatible continuous-scroll libraries. MUI remains in the fixture and raw data, but not in the leaderboard. That makes the benchma

A note before we start: this is about the machine, not the money. I'm not going to show you returns, positions, or a single "this strategy made X%." Partly because that's a regulatory minefield, and partly because the returns aren't the interesting part — the engineering is. If you came for a get-rich screenshot, this isn't that. If you came to see how one person ships production infrastructure with an AI, pull up a chair. The thing I built Over the last few months I built, with an AI coding agent as my pair-programmer, a fleet of ~35 automated trading bots. They run across five equity markets plus crypto. Each one is a long-running service. They share a single database, post to a live dashboard, fire alerts to my phone, and — the part that took the longest — they're built to survive restarts, reconcile against reality, and refuse to do anything stupid. I'm one person. I am not a team. The "team" is me plus an AI in a terminal, working the way you'd work with a very fast, very literal junior engineer who never gets tired and occasionally needs to be talked out of a bad idea. Here's how it's put together, and the handful of lessons that cost me the most to learn. The architecture, in one breath One Postgres database is the brain — every trade, signal, and piece of state lives there. Around it sit ~35 containerized bots, each isolated (its own tables, its own config, its own identity), orchestrated with Docker Compose. A Streamlit dashboard reads the database and renders the whole fleet — open positions, P&L curves, health. A notification layer pushes Telegram alerts on every meaningful event. Schema changes go through migrations so a new bot is never born with a stale database shape. Each bot is the same skeleton wearing a different hat: a signal module (the strategy logic), a trader that turns signals into orders, a storage layer that persists everything, a runner loop on a schedule. Strategies are swappable. The infra underneath them is identical. That sameness is

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We started using Jira to manage our internal development workflow. At first it worked fine, but once we outgrew the free tier, the cost became hard to justify. At $15 per user per month, we were suddenly looking at a bill that did not match how we actually used the product. What we Built We created WannaTrack, a lightweight project management tool designed for small dev teams that do not need enterprise complexity. The goal was not to recreate Jira. It was to remove everything we did not use. Key ideas : minimal agile board with no clutter or heavy configuration simple issue tracking flow fast interface for daily development work minimal setup and no onboarding overhead Migration from Jira One of the biggest concerns was switching tools without breaking our workflow. So we built a Jira import tool that lets you migrate existing tickets into WannaTrack without manual effort. This allowed us to switch internally without downtime. Where it is now We now use WannaTrack daily for our own development workflow and are opening it up to other teams who feel the same pain with traditional tools. If you are a small dev team, indie hacker, or startup looking for a simpler issue tracker without overhead, you can check it out here: https://wannatrack.com

Is PlayStation about to make the same mistake Microsoft made with Xbox One?

Key Takeaways You can't unit-test a coach agent the way you test a pure function — the output is non-deterministic and "good" is a judgment call, not an assertion. An LLM-as-judge harness lets you grade a whole test set automatically against a rubric, which is the only way solo-scale eval stays sustainable. But the judge is itself a fallible model. If you don't design around its known biases — position, verbosity, self-preference, and quiet drift when the judge model updates — you build a green dashboard that means nothing. The mitigations that actually work are mechanical, not prompt-magic: shuffle order on every pairwise call, pin the judge version, keep a small human-labelled anchor set, and re-check the judge against it. The problem I actually had FamNest's coach agent generates responses to parents — check-ins, encouragement, the occasional gentle redirect. I have a growing pile of these interactions, and every time I change a prompt, swap a model, or adjust the pipeline, I need to know one thing: did I just make it better or worse? For normal code, that's what tests are for. I change something, the suite runs, red or green, done. But there's no assertEqual for "was this an empathetic, useful response to a tired parent." The output changes every run even at temperature zero-ish, and the quality bar is a human judgment, not a fixed string. Two responses can be worded completely differently and both be good. One can match my "expected output" word for word and still be worse than a version that didn't. So the honest options were: read every response by hand every time I change something (does not scale past about week two), or build a harness where a model grades the outputs against a rubric. I built the harness. Then I spent an uncomfortable amount of time learning all the ways a harness like that can lie to you. What the harness actually is At its simplest, it's a loop: def evaluate ( test_cases , coach_agent , judge ): results = [] for case in test_cases : res

A few weeks ago I was in Copenhagen for my first Codegarden, and one quiet thought has stuck with me since. It didn't come from a keynote. It came from the bit the keynote leaves out. I've worked with Umbraco for years, but I'd never been to Codegarden, and I turned up without much of a fixed idea of what the two days would be. I kept that open on purpose. I wanted to take it in rather than measure it against something I'd decided in advance. What struck me most was that the value came from two places at once. The sessions were a fantastic source of inspiration; everything from keynotes to guest speakers all seemed to resonate in some way or another. The conversations in between the sessions - drifting around the event space and finding common ground with anyone and everyone - proved just as valuable. I came home more energised than I've been in a while, with a notebook full of half-formed ideas and a better feel for the community I'm part of. But the thing I kept turning over afterwards was that bit the keynote leaves out. That's what I want to write about. The easy half and the hard half Every major Umbraco release gets the same treatment. A polished keynote, a clean demo, a feature that looks effortless on stage. There's plenty in 18, and which part matters most depends on what you're building. For me it's Elements: a new Library section where you manage reusable content and reference it through a new element picker. Create once, use everywhere. It's a genuinely good direction. Reusable content has lived awkwardly in the content tree for years, and Library finally gives it a proper home. What the demos don't show you is the part I've been playing around with for the past few weeks. Taking a real Umbraco 17 site, with content pickers threaded through block lists, block grids, rich text blocks and base document properties, and getting all of it to point at the new Library without an editor ever noticing anything moved underneath them. The feature is the easy half.

Exam Guide: Developer - Associate 🏗️ Domain 3: Deployment 📘 Task 1: Prepare Application Artifacts To Be Deployed To AWS Before you can deploy anything to AWS, you need to package it properly. This task covers Lambda deployment packaging (zip vs container), managing dependencies, structuring projects for multi-environment deployment, and using AWS AppConfig for runtime configuration. 📘Concepts Lambda Deployment Packaging Options Option Max Size Build Complexity Cold Start Best For Zip Package (inline editor) 3 MB (editor limit) None Fastest Simple functions, no dependencies Zip Package (upload) 50 MB compressed / 250 MB uncompressed Low Fast Most Lambda functions Zip + Lambda Layers 250 MB total (function + all layers) Medium Fast Shared dependencies across functions Container Image 10 GB Higher Slower (first invoke) ML libraries, large dependencies, custom runtimes 💡 If a scenario is about a deployment package exceeding 250 MB, the answer is container images. If it mentions sharing dependencies across multiple functions, the answer is Lambda Layers. Zip is the default for most workloads. Lambda Layers Aspect Detail What They Are Zip archives containing libraries, custom runtimes, or other dependencies Max Layers Per Function 5 Size Limit 250 MB total (function code + all layers uncompressed) Versioning Each publish creates an immutable version Sharing Can be shared across functions, accounts, or made public Path Contents extracted to /opt in the execution environment Dependency Management Strategies Strategy How It Works Pros Cons Bundle In Zip Install deps into package directory, zip together Simple, self-contained Larger package, duplicated across functions Lambda Layers Package deps as a layer, attach to functions Shared across functions, smaller deploys Layer version management, 5-layer limit Container Image Install deps in Dockerfile Full control, large deps supported Slower cold starts, ECR management sam build SAM resolves deps from requirements.txt automatic

For years I copied cron expressions off Stack Overflow, pasted them into a config file, crossed my fingers, and moved on. 0 9 * * 1-5 ? Sure, that "looks like weekday morning." */15 * * * * ? "Every 15 minutes, probably." I never actually read them. So I did the thing that always cures this for me: I built a tool that parses a cron expression, explains it in plain English, and shows the next five times it will fire. No library. About 50 lines of real logic. Here's everything I learned. The five fields (and the order that trips everyone up) A standard cron expression is exactly five fields separated by spaces: ┌──────── minute 0 - 59 │ ┌────── hour 0 - 23 │ │ ┌──── day - of - month 1 - 31 │ │ │ ┌── month 1 - 12 │ │ │ │ ┌ day - of - week 0 - 6 ( 0 = Sunday ) * * * * * The order never changes, and the number-one beginner mistake is swapping the first two. Minute comes first. If you write 9 30 * * * thinking "9:30am," you actually get "minute 9, hour 30" — which is invalid, because hours only go to 23. Say it out loud every time: minute, hour, day-of-month, month, day-of-week. Each field answers one question: which values of this unit does the job run on? An * means "every value." Most real schedules pin down a couple of fields and leave the rest as * . Daily at 9am is 0 9 * * * — minute and hour fixed, everything else "every." Lists, ranges, and steps Beyond single numbers, each field understands three operators, and they combine: Comma makes a list: 1,15 in the day field means the 1st and the 15th. Hyphen makes an inclusive range: 1-5 in the day-of-week field means Monday through Friday. Slash makes a step, taking every n-th value: */15 in the minute field means 0, 15, 30, 45 . Steps can apply to a range too, so 0-30/10 means 0, 10, 20, 30 . That's the whole grammar. Number, list, range, step. Once you can expand a field into the concrete set of numbers it matches, you understand cron. Here's the expansion function, which is the heart of the parser: function expandFie

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Claude Code is mostly prose. Tool output, reasoning traces, permission prompts — I read paragraphs of this for hours every day. Most terminal themes are built around syntax highlighting: make keywords pop, dim punctuation, saturate strings. That's optimizing for the wrong thing when your screen is 80% English sentences. I built klein-blue to fix this for my own setup. Four variations, all built around Yves Klein's IKB pigment, all APCA-verified for body-size prose legibility in the specific ANSI slots Claude Code actually uses. The interesting constraint: pure IKB fails APCA contrast as text on a dark ground (Lc -12 — effectively invisible). So I split it across two ANSI slots. ansi:blue gets pure IKB for decorative borders and highlights where legibility doesn't matter. ansi:blueBright gets a lifted Klein-family value (A8BEF0) for readable permission-prompt text. You keep the color identity; you can actually read it. The four variations each answer the same question differently: how should Claude's brand colors live in your terminal? Claude Code uses ansi:redBright for its claude-sand brand color. That's the differentiating moment between the themes: Klein Void Refined — balanced, neutralizes brand competition Klein Void Sand & Sea — accepts claude-sand as a second hero alongside IKB Klein Void Prot — fully APCA-verified across every role (body >= 90, subtle >= 75, muted >= 45, accent >= 60); the only variation where every accent passes strict gates Klein Void Gallery — one-blue maximum void, everything else recedes One prerequisite that took me a while to document clearly: Claude Code's /theme picker must be set to dark-ansi , otherwise Claude Code ignores the Terminal.app ANSI palette entirely and falls back to its hardcoded RGB values. The theme does nothing without that. Ships as macOS Terminal.app .terminal profile files. Built from build.m with a variation-aware Objective-C builder, installed via install.sh , fully rollback-able via restore.sh . CommitMono-Re

Modern Angular Guards: Architecture, Best Practices & Enterprise Patterns A deep dive into designing lightweight, composable, and maintainable routing guards in modern Angular applications. Table of Contents Introduction Why Guards Exist The Golden Rule of Angular Guards Functional Guards: The Modern Standard CanActivateFn: Authentication Guard CanMatchFn: Permission-Based Route Matching CanDeactivateFn: Unsaved Changes Guard CanActivateChildFn: Nested Route Protection Signals + Guards: Reactive Permission State Feature Flags in Routing Guard Composition Patterns UrlTree Redirects vs Imperative Navigation Async Guards: When and How Permission Service Architecture Role-Based Access Control (RBAC) Permission-Based Access Control (PBAC) Route Data for Configuration Lazy Loading with Guards Standalone Routing with provideRouter Route-Level Providers Guards vs Interceptors Guards vs Backend Authorization Performance Considerations Navigation UX Best Practices Error Handling in Guards Testing Guards Common Mistakes Production Checklist Enterprise Routing Insights Conclusion Introduction In modern Angular applications, routing guards have evolved from class-based monoliths into lightweight, composable functions. This shift isn't just syntactic—it's architectural. As Angular applications become larger and more complex, the routing layer becomes a critical piece of the architecture. Guards are the gatekeepers of your navigation, but they should never become the orchestrators of your application logic. This article is for senior Angular developers, software architects, and team leads who are designing routing strategies for enterprise-scale applications. We won't explain what a route guard is—we'll explore how to architect them properly. Why Guards Exist Guards exist to protect navigation boundaries. They evaluate whether a transition should proceed, redirect, or be blocked. In modern Angular, this is achieved through functional guards that return: boolean — allow or block na

By the team at MailTester Ninja — a real-time email verification API that stores nothing. We verify a lot of email for a living. So we pointed our infrastructure at a representative panel of 50,000 of the world's most-linked domains and measured how email is actually configured in 2026 — MX providers, SPF and DMARC. Pure DNS, aggregate only, no personal data . Here's what the internet's mail setup looks like right now. Email is still (almost) everywhere 79.9% of these domains are mail-enabled (they publish MX records). Email isn't going anywhere. Authentication: adopted, but not enforced 75.8% publish an SPF record 64% publish a DMARC record …but only 22.6% actually enforce it with p=reject That last number is the real story. Of the domains that bother to publish DMARC, only 35.2% are on p=reject — the rest sit on p=none (37.2%, monitoring only) or quarantine (27.6%). Most of the web announces a policy it doesn't enforce. That's a deliverability and spoofing gap hiding in plain sight. Who runs the world's inboxes? Other / self-hosted — 32.6% Google Workspace / Gmail — 28.2% Microsoft 365 / Outlook — 22.5% Proofpoint — 5.5% Mimecast — 3.1% Tencent QQ — 2% Namecheap — 1.3% Cisco IronPort — 0.9% Self-hosted and the two hyperscalers (Google Workspace and Microsoft 365) dominate, but the long tail of providers is very real — which is exactly why deliverability is hard: every provider blocks, greylists and reputation-scores differently. Why we publish this We built an open, daily-updated dataset and a live dashboard because deliverability decisions should be based on data, not folklore. It's CC BY 4.0 — use it, cite it, build on it. Want to check a specific domain? Our free analyzer shows any domain's MX / SPF / DMARC in one click — no signup, nothing stored. Methodology: Live DNS scan (MX/SPF/DMARC). Aggregate only — no email sent, no personal data. Sample updated Wed, 01 Jul 2026 12:31:00 GMT.

most auto-apply tools have a dirty secret: they only autofill the form. they drop your details in and stop. some press submit. almost none read the confirmation the applicant tracking system sends back afterward, which means they cannot actually tell a click from a landed application. so they show you "applied" and hope. we read that confirmation. it is the whole point of what we build. and the side effect of reading it is that we have a status most tools do not: failed . a column that says, out loud, this one did not go through. having that column means we can be wrong out loud too. today we were. our apply agent clicked submit on a real Greenhouse form. the form went through. then, about half a second later, a downstream network blip threw an error, and the old code took that to mean the whole run had failed. it stamped a real, registered application as failed . a false negative on the one signal that matters most. the fix (in submitter.ts ) is a gate we now call submitClickIssued . once the agent has actually clicked submit, a later transport error can no longer produce a hard failed . it resolves to requires_human_review with a "likely landed, confirm this one" disposition instead. a blip after the click can no longer fake a failure. worst case, we ask you to double-check one, instead of lying to you in either direction. it is not a glamorous ship. no new feature, no screenshot. but a tool that never fails is a tool that never tells you, and the boring reliability days are the actual product. building this in public. no fabricated numbers, just the log.

Let’s face it: if your current frontend optimization strategy still involves manually auditing codebases for missing useMemo hooks, micro-managing dependency arrays, or aggressively fighting layout shifts with complex client-side state management, you are wasting your engineering leverage. As we cross the midpoint of 2026, web framework architecture has quietly undergone a massive shift. We have firmly moved out of the era of manual performance tweaking and entered the era of automated, compile-time optimization . The goal of modern development is no longer just shipping fewer kilobytes to human users—it's also about optimizing data chunk delivery for AI web crawlers that evaluate your site in real-time. Here is how the modern full-stack ecosystem redefined performance this year, and what you should focus on instead. 1. The Death of Manual Memoization (Thanks, React Compiler) For years, React developers bore the cognitive load of rendering performance. One misplaced reference and your entire component tree re-rendered down to the root. With the absolute maturity and default adoption of the React Compiler across production frameworks, that paradigm is officially legacy code. The compiler handles component memoization automatically at the build step by analyzing javascript structures directly. // ❌ THE OLD WAY (Pre-2026 Manual Overhead) const ExpensiveComponent = memo (({ data }) => { const processedData = useMemo (() => computeHeavyMetrics ( data ), [ data ]); const handleAction = useCallback (() => { ... }, []); return < DataGrid items = " {processedData} " onAction = " {handleAction} " /> ; }); // THE MODERN WAY (Zero Performance Boilerplate) export function ModernComponent ({ data }) { const processedData = computeHeavyMetrics ( data ); const handleAction = () => { ... }; return < DataGrid items = " {processedData} " onAction = " {handleAction} " /> ; } Because the compiler injects optimization markers directly into the output code, human engineers can stop arguin

HeroUI v3 is a redesigned React component library, previously NextUI, offering over 75 components, including 21 new ones, and a new React Native library with 37 components. Built on React Aria and Tailwind CSS v4, it emphasizes accessibility and customization. The library has experienced many updates since its release, and migration from the previous version is necessary. By Daniel Curtis

Rhythm Heaven isn't Nintendo's best-known series, nor its most prolific. Prior to the launch of Rhythm Heaven Groove on the Switch this week - it's out on July 2nd - there were only four previous entries, one of which was exclusive to Japan. The most recent came out more than a decade ago. Even still, […]

Smart speakers have spent the past few years searching for a compelling second act. Beyond music, timers, and controlling your lights, they've struggled to justify taking up space on the kitchen counter. AI promised to change that. Amazon debuted its new hardware powered by a revamped Alexa last fall, and now it's finally Google's turn. […]

This hefty but nimble and highly customizable ebike makes the journey as important as the destination. Get where you want, and have fun along the way.