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Hey everyone! 👋 I'm a developer who got tired of visiting 10 different websites to do simple tasks like formatting JSON, compressing images, or generating QR codes. So I built DevToolBox — a single place with 15+ free online tools, all running in your browser with no sign-up required. 👉 https://toolbox-site.asia Why I Built This Every time I needed a quick tool, I'd end up on a site full of ads, popups, or "create an account to continue" walls. I wanted something clean, fast, and respectful of users' time and privacy. The idea was simple: one website, all the tools you need, zero friction. What's Inside Here are some of the tools available: Developer Tools: JSON Formatter & Validator — Format, validate, minify JSON with syntax highlighting Base64 Encoder/Decoder — Encode and decode Base64 strings instantly UUID Generator — Generate v4 UUIDs in bulk 🔧 Unix Timestamp Converter — Convert between timestamps and human-readable dates 🔧 Regex Tester — Test regular expressions with real-time matching 🔧 Markdown Preview — Write Markdown and see the output live Hash Generator — MD5, SHA-1, SHA-256, SHA-512 🔧 Diff Checker — Compare two texts side by side Daily Tools: 🖼️ Image Compressor — Compress images right in your browser Image Format Converter — Convert between PNG, JPG, WebP Password Generator — Create strong, customizable passwords 📱 QR Code Generator — Generate QR codes with custom colors BMI Calculator — Calculate Body Mass Index 🎂 Age Calculator — Calculate exact age from birth date 📝 Word Counter — Count words, characters, sentences 📏 Unit Converter — Length, weight, temperature, and more How It's Built The whole site is a Vue 3 + TypeScript + Vite project with Tailwind CSS for styling. Everything runs client-side — no data is ever sent to a server, which means your data stays on your device. Key tech: Vue 3 with Composition API TypeScript for type safety Vite for blazing fast dev experience Tailwind CSS for styling Vue Router with history mode for clean URLs vue-i1

submitted by /u/r_retrohacking_mod2 [link] [留言]

Fire toast notifications in Laravel from PHP, Alpine and plain JavaScript with one notify call, plus positioning, auto-dismiss and grouping, and no CSS framework in your bundle Here is a problem I hit on every project. A Livewire action finishes and I need to tell the user it worked, but the toast library I grabbed assumes Tailwind, or ships its own huge runtime, or only works from JavaScript when half my triggers actually live in PHP. Wiretoast is my answer to that, and this post is the fast path to using it. The problem You want to fire a toast from PHP, from Alpine, and from plain JavaScript with the same call, and you do not want to drag a CSS framework into your bundle to get it. How to install Start with Composer, then wire up the assets. I bundle with Vite, so I import the package CSS and JS into my entry files. // resources/js/app.js import ' @wiretoast/js/wiretoast.js ' ; import ' @wiretoast/css/wiretoast.css ' ; That @wiretoast alias is optional, and you set it up by pointing Vite at the vendor resources folder so the imports stay short. // vite.config.js resolve : { alias : { ' @wiretoast ' : path . resolve ( __dirname , ' vendor/edulazaro/wiretoast/resources ' ), }, }, Then the component goes once into your layout, and on the Vite path it injects no tags of its own. <x-wiretoast /> How to use it The fastest possible win is a one-liner in a Livewire component right after something succeeds. The helper is a component macro named notify , registered for you when Livewire is present. $this -> notify ( 'Profile updated' , 'success' ); Under the hood that dispatches a notify browser event, which is exactly what Alpine fires too. So the same toast from a purely front-end button looks like this. <button @ click= "$dispatch('notify', { message: 'Copied', type: 'info' })" > Copy link </button> The five types you can pass are success , error , warning , info and neutral , and a message can be a plain string or an object with a title and a message when you want a he

I was optimizing the Lua transpiler for my modding language grug and ran into a really weird LuaJIT performance bug. The same benchmark could randomly run 20× slower, and it turned out a LuaJIT NYI could silently blacklist an unrelated hot loop. I wrote up the investigation here: The LuaJIT NYI That Silently Poisoned an Unrelated Hot Loop It goes from the benchmark mystery through LuaJIT's trace recorder internals, and ends with a PR to get unpack off LuaJIT's NYI list. Feedback is very welcome! :) submitted by /u/MyNameIsTrez [link] [留言]

What the report found The FIDO Alliance — the industry group behind the passwordless authentication standard — released its State of Passkeys 2026 report in May, based on research across 11,000 consumers and 1,400 enterprise decision-makers in ten countries. A few numbers stand out: passkeys now see a 93% sign-in success rate compared to 63% for passwords, and average sign-in time drops to roughly 8.5 seconds versus over 30 seconds for password-based logins. Awareness has also jumped to 90% of consumers, with about 5 billion passkeys now active worldwide. The security case is the more important one. Passkeys are built to be phishing-resistant by design — unlike a password, there’s no shared secret that can be typed into a fake login page, because the credential is cryptographically tied to the real site and your device. That’s a structural fix, not a behavioral one — it doesn’t depend on you spotting a scam email, which is precisely where most password-based breaches start. Why adoption still lags Here’s the more interesting number: even among organizations that have rolled out passkeys, the majority still keep passwords running in parallel as a fallback, and a large share of individual users still don’t use passkeys everywhere they’re offered. The barrier at this point isn’t awareness — it’s habit. People default to what’s familiar, even when the safer option is one tap away. The practical takeaway Most major platforms — Google, Apple, Microsoft, and a growing list of banks and retailers — now offer passkeys as a login option, usually sitting quietly in account security settings labeled “passkey” or “sign in without a password.” The action worth taking today: pick your two or three most important accounts (email first, since it’s the recovery path to everything else) and set up a passkey where it’s offered, instead of waiting for a breach to force the decision. Passkeys aren’t foolproof — device loss and account-recovery flows are still an active area of security r

TL;DR — composer require bouda/laravel-make-pattern → php artisan make:pattern Post → 9 consistent files in seconds. DDD-ready, rollback included, every stub is yours to override. The problem I kept running into Every new Laravel project starts the same way. You know the architecture you want: Repository, Service, Controller, some Form Requests, a Resource, a Policy, a test. You've written this stack dozens of times. And every time, you either: Copy-paste from a previous project — and immediately introduce inconsistency between how PostRepository is structured vs CategoryRepository . Write everything from scratch — which is slow and error-prone. Use make:model -a — which gives you the Model, Migration, Factory, Controller, but nothing about repositories, services, or policies wired together. None of these feel like the right answer when you want a clean, layered architecture. So I built laravel-make-pattern . What it does One command: php artisan make:pattern Post Generates 9 files : app/Models/Post.php app/Repositories/Contracts/PostRepositoryInterface.php app/Repositories/PostRepository.php app/Services/PostService.php app/Http/Controllers/PostController.php app/Http/Requests/PostStoreRequest.php app/Http/Requests/PostUpdateRequest.php app/Http/Resources/PostResource.php app/Policies/PostPolicy.php tests/Feature/PostTest.php All consistently named, all using the same conventions, all generated from stubs you own and can override . The generated code Here's what the repository looks like out of the box: <?php namespace App\Repositories ; use App\Models\Post ; use App\Repositories\Contracts\PostRepositoryInterface ; class PostRepository implements PostRepositoryInterface { public function all () { return Post :: all (); } public function find ( string $id ) { return Post :: findOrFail ( $id ); } public function create ( array $data ) { return Post :: create ( $data ); } public function update ( string $id , array $data ) { $model = $this -> find ( $id ); $model -> u

submitted by /u/mariuz [link] [留言]

Building software is easy. Building something people actually want to use is the hard part. For the last few months, I've been working on CV Mimarı, a resume builder designed to make creating ATS-friendly resumes simple, fast, and accessible. 👉 https://cvimarı.xyz My goal wasn't to build "another resume builder." I wanted to remove the usual pain: confusing editors unnecessary account creation complicated formatting resumes that look good but fail ATS screening The idea was simple: Spend your time improving your experience, not fighting with Word formatting. What it currently does Today the project includes: Resume templates AI-powered resume improvements ATS score checking Resume optimization Cover letter generation Resume examples and guides PDF export Modern responsive interface I tried to keep everything clean and straightforward instead of adding dozens of unnecessary options. Sometimes software tries so hard to become "professional" that it forgets people just want to click a button and move on with their lives. Why I'm posting here I'm not looking for compliments. I'm looking for problems. Imagine you were using this to apply for your next job. I want brutally honest feedback. Things like: Is something confusing? Does the UI feel slow? What would make you leave the site? Which feature feels unnecessary? What's missing? Would you actually trust this with your resume? If something is bad... Tell me. If something is ugly... Tell me. If something makes you want to close the tab... Definitely tell me. The biggest challenge One thing I've learned is that building features is much easier than understanding users. I can spend a weekend implementing a new AI feature. But discovering why someone leaves after 20 seconds? That takes dozens of real users. That's why I'm asking for feedback before continuing to add more features. The roadmap Some ideas I'm considering: More resume templates Better AI suggestions Portfolio integration LinkedIn import Resume version history

Most mobile apps ship a privacy policy as a link that opens Safari. A smaller number ship an in-app rendered policy. A tiny minority ship what I think is the single highest-leverage privacy screen: a summary that mirrors your Nutrition Label in plain language, designed to be read. Why it's high-leverage The privacy summary sits at the intersection of three concerns: Users read it before granting sensitive permissions. Especially for camera, contacts, location, and health data. App Store reviewers check it exists and matches your store listing. Regulators (GDPR, CCPA) reward it. Clarity is a compliance signal, not a legal defense — but it's what an investigator asks for first. Sixty minutes of design work. Meaningful trust dividends. Often the difference between first-submission approval and a rejection loop. What to include The privacy summary should mirror your Privacy Nutrition Label but with real language humans can parse: Each data category you collect — displayed as a card or a row, with a clear icon. Why you collect it — one line, plain language. "So you can log in on another device," not "for authentication purposes." Where it's used — is it stored on our servers, shared with third parties, only on your device? Be specific. How to opt out or delete it — a link or a button to the settings page where the user can act on that category. Six to eight cards, one per data category. No more. Visual patterns that read as trustworthy Design choices that consistently score high in user-testing for trust signal: Real language, not legalese. "We store your email address so you can log in" beats "Personal identifiers are retained for authentication purposes." Muted, confident colors. No warning reds, no compliance yellows. A neutral surface with soft accent for the data-category icons. Readable typography. 16pt body, generous line-height (1.5x), enough paragraph spacing that scanning is easy. Icons per category (not just text). A camera icon for camera data, a location pin

Dumanshu Goyal discusses optimizing data layers for low-latency workloads like AI feature stores. Drawing lessons from NASA's Space Shuttle, he explains how proxy architectures introduce hidden CPU costs, elevated tail latencies, and blast-radius risks. He demonstrates how direct-access Valkey architectures achieve microsecond latency, improve resilience, and slash infrastructure costs. By Dumanshu Goyal

OpenAI has asked a federal judge to toss out Apple's landmark lawsuit accusing the ChatGPT maker of stealing trade secrets, describing the allegations as "meritless." In a motion filed yesterday to dismiss the complaint, OpenAI says that Apple is mischaracterizing both the actions of the AI startup's employees as theft, and "generic" product development information […]

I put handheld, wearable, and misting fans through a sweltering summer to see which ones kept me coolest.

A tiny crew of monsters for your Claude Code agents Discussion | Link

I maintain Fulmine , a drop-in replacement for Express 5 that runs on µWebSockets.js instead of node:http . One line changes: const express = require ( " fulmine.js " ); // instead of require("express") Your middleware keeps working: helmet , cors , passport , morgan , multer , express-session and the rest. The numbers are not mine Benchmarks published by a project about itself deserve suspicion, so let me use somebody else's. HttpArena runs every framework on the same 64-core machine, in containers, under the same rules, and publishes the results. Express and Fastify are on that board too. Requests per second, from their published runs: Profile Fulmine Express Fastify Baseline (query parsing) 1,220,308 607,777 711,263 JSON (dataset + serialization) 1,111,187 395,361 522,201 Short-lived connections 1,026,789 278,163 298,779 Pipelined 7,259,814 1,009,543 1,671,338 Mixed API workload, 16 CPUs 126,282 67,724 75,633 Async Postgres 222,701 169,687 179,169 Upload (20 MB body) 2,154 2,104 1,902 That is 2.0x Express on the baseline, 2.8x on JSON, 3.7x on short-lived connections, 7.2x pipelined , and 1.9x on the mixed API profile. Against Fastify, on the same board, it is 1.7x on the baseline and 2.1x on JSON. Now the honest parts, which matter as much as the table. Look at the upload row: 1.02x. A 20 MB body is memory bandwidth and syscalls, not framework code. Everywhere the cost belongs to a library both servers call, the difference disappears: JSON.parse , zlib, OpenSSL. Speed comes from the framework only where the framework is doing the work. My entry runs in the arena's "tuned" mode, Express's and Fastify's run in "standard". On two profiles I left out of the table, static files and compressed JSON, that difference is decisive, because tuned mode allows hand-written compression and negotiation. Those rows would show 23x and 8x, and they would be measuring my entry's tuning, not the framework. I would rather not quote them. Where the speed comes from Not from one trick

2.5.1. Code Contracts Your code, whether explicitly or implicitly, contains a contract. A contract has two sides: A contract is a requirement, which is a restriction on how the code is used A contract is a promise, which is a guarantee about how the code behaves When designing APIs, there is a useful rule of thumb: avoid imposing unnecessary restrictions, and only make promises you can keep . Why? Adding restrictions or removing promises requires a major semantic version change and may break other code When you first design an API, loosening restrictions and later adding extra promises is usually backward-compatible 2.5.2. Restrictions and Promises Common forms of restrictions in Rust are: Trait bounds Argument types Common forms of promises are: Trait implementations Return types Some Examples Let's look at an API evolving through three versions: fn frobnicate ( s : String ) -> String The first version takes a String and returns a String Its contract is that the caller performs allocation (because both the parameter and return value are owned, allocation is inevitable), and its promise is that it returns an owned String The problem with this function is that, without changing the signature, it cannot later be turned into a “no-allocation” function, because both the argument and return value are owned fn frobnicate ( s : & str ) -> Cow < '_ , str > The second version relaxes the contract a bit Its contract is that it accepts only a string reference, and its promise is that it returns either a string reference or an owned String , namely the Cow type This version is still somewhat rigid. For example, the argument is &str ; if I pass in a String , I still have to convert it first. Also, because the return value is Cow , it cannot return string-owning types other than String and &str (for example, OsString ) fn frobnicate < T : AsRef < str >> ( s : T ) -> T The third version relaxes the contract further Now both the parameter and the return value only require a type th

If your Sentry bill is climbing faster than your signups, the usual cause isn't more users — it's more events per user . Error trackers meter on event and transaction volume, and a single bad deploy, a noisy third-party SDK, or one uncaught exception in a hot loop can burn a monthly quota in an afternoon. Before you migrate, the honest first move is to fix what you're sending. If you've already done that and the economics still don't work, GlitchTip, self-hosted Sentry, Bugsnag, Rollbar, and an OpenTelemetry-based stack are the realistic exits — each with a different trade. Why does the bill scale with events instead of users? Error tracking is priced on the thing that's expensive to store and index: individual events. Sentry, Rollbar, Bugsnag, and most SaaS competitors bill primarily on captured errors (and, increasingly, performance/tracing spans and session replays as separate meters). A product with 500 daily active users can generate millions of events if one component throws in a render loop or a retry storm hammers a failing endpoint. That decoupling is the whole problem. Your revenue tracks users; your observability bill tracks failures and instrumentation depth . When you add performance monitoring and session replay — both of which emit far more events than plain error capture — the meters multiply independently of how many humans are actually using the app. The takeaway: before you evaluate a single alternative, confirm whether you have a pricing problem or a volume-hygiene problem, because migrating won't fix a firehose. Can you cut the bill without switching tools? Often, yes — and it's worth an afternoon before any migration. The levers that matter most: Sample transactions, not just errors. Performance/tracing volume is usually the bigger line item once enabled. A tracesSampleRate of 0.1 or lower is fine for most apps; you rarely need every transaction. Filter noise at the SDK, before it's billed. ignoreErrors , denyUrls , and beforeSend let you drop

Intro Day 20! I lined up 10 AIs that turn a single photo into a few seconds of video. Half ran locally on my DGX Spark, half in the cloud 🐱 What I used: DGX Spark (LTX-2.3 / Wan 2.2) / 8 cloud models via fal.ai / ComfyUI / ffmpeg The setup Item Value Input One identical photo (my cat on a desk) Length 6 seconds Settings Identical The only variable The prompt Easy prompt The cat looks at the camera and meows once. It opens its mouth, meows, then closes it. Its tail flicks and its ears twitch. Hard prompt The cat stands upright on its hind legs in a kitchen, wearing a small apron, holding a knife in its front paws and chopping vegetables on a cutting board. Steam rises from a pot behind it. Please, just watch it Some of the cats came out with very long legs. Anyway. First half is the easy prompt, second half the hard one. On the easy prompt, local and cloud were a fair match . On the hard one... cloud, I think...! Three rankings below. Ranking 1: Time Time per 6-second clip on the hard prompt. Rank Model Where Time 🥇 LTX-2.3 Cloud 41s 🥈 Wan 2.7 Cloud 92s 🥉 Happy Horse 1.1 Cloud 97s 4 Veo 3.1 Cloud 128s 5 Kling 3 Pro Cloud 205s 6 Seedance 2.0 Cloud 210s 7 LTX-2.3 Local 315s 8 Wan 2.2 Local 651s 9 daVinci-MagiHuman Cloud 710s 10 HunyuanVideo 1.5 Cloud 796s A 19x spread. Look at 1st and 7th. Same model, LTX-2.3 , nearly the same resolution. The only difference is where it ran — 7.6x . Local setup DGX Spark (GB10, 128GB unified memory, ~273GB/s). ComfyUI headless, workflows over its API. LTX-2.3 is distilled fp8 at 8 steps. At 1088×1920 peak memory hit 77.8GB, about 60% of 128GB. That was the ceiling. Dropping to 512×768 finishes in 70s, but with one-fifth the pixels. Wan 2.2 is I2V-A14B fp8, 20 steps, 480×640. Higher resolution does not finish in reasonable time. Ranking 2: Cost Rank Model Per 6 seconds 🥇 Local Electricity only 🥈 LTX-2.3 (cloud) $0.36 🥉 Wan 2.7 $0.90 4 Kling 3 Pro $1.01 5 Happy Horse 1.1 $1.08 6 Veo 3.1 $2.40 7 Seedance 2.0 $4.09 — HunyuanVideo / MagiHum

Originally published in Spanish on El Rack. Browser translation handles the rest of the site fine if you're into homelab/self-hosting content. Moonshot AI released Kimi K3 on July 17, 2026, and made the weights publicly downloadable on July 27. At 2.8 trillion parameters, it's the largest open-weight model ever published — and according to multiple benchmarks, it rivals Claude Opus and GPT on coding, reasoning, and general knowledge work, at a fraction of the training cost. The New York Times ran an in-depth piece on it a few days after release, which tells you this isn't just another model drop. What "open weights" actually gets you here Publicly downloadable weights mean any company or researcher can run this locally and modify it without depending on a third-party API. If you already run Ollama or LM Studio in your homelab, that's the tempting part: a frontier-level model, no monthly quota, running on your own hardware. The practical reality is different. "2.8 trillion parameters isn't a number that runs on homelab hardware — it needs an enterprise-grade GPU cluster. The weight release is real, but "downloadable" and "runnable" are very different things at this scale." The bigger debate this reopened What makes Kimi K3 interesting isn't just the benchmark numbers — it's what it represents in the ongoing dispute over AI's geopolitics. The same fracture that opened up around DeepSeek-R1 in January 2025 is back: some argue US labs need to close up more in response to Chinese competition, others see openness as the only real way to stay relevant against an ecosystem that ships open weights at a pace closed labs can't match on transparency. There's also a real technical concern underneath: the possibility that outside actors use massive querying of closed American models to distill their outputs and train competing open models. Where this actually matters for a homelab Even though K3 itself is unrunnable on consumer hardware, its release pushes down what smaller, actu

Immich is the better choice if you own a machine that stays powered on and you care where your photos live. It gives you the parts of Google Photos people actually use every day, mobile auto backup, face grouping, map view, albums and shared links, without a storage meter that raises your bill as your library grows. Google Photos still wins on zero maintenance and on search that understands a sentence. If you are willing to spend one evening on setup and roughly an hour a quarter on updates, Immich replaces it. TL;DR by reader profile: Family archivist with 15 years of photos (Marta, two phones, one shared library): move to Immich on a small always on box, because a growing archive is exactly the case where a per gigabyte subscription compounds against you forever. Photographer shooting RAW every weekend (Tomas, 40 megapixel bodies): Immich, because RAW files eat cloud tiers fast and you already keep a local working copy that you can point the server at. Non technical user with one phone and no home server (Elena, iPhone, no NAS): stay on Google Photos for now, because Immich needs someone to own updates, backups and remote access, and that someone would be you. Privacy sensitive professional handling client images (lawyer, therapist, journalist): Immich on hardware you control, because the legal question is not whether the provider is trustworthy but who can be compelled to hand over the data. Homelab owner already running Docker (Sam, existing NAS and reverse proxy): Immich, because the marginal cost is one compose stack on infrastructure you maintain anyway. Small team or studio sharing a shoot library (five people, one archive): Immich with per user accounts and shared albums, because Google Photos was built for one person and gets awkward the moment several people need write access. The central tradeoff: Google Photos sells you freedom from maintenance and pays for it with a recurring bill and a library you do not control, while Immich hands you control and a o

title: How to Convert Images to Buildable Minecraft Pixel Art with Exact Materials published: true tags: minecraft, tutorial, gaming, opensource Originally published at blockartlab.com Disclosure: I built BlockArtLab, the free browser tool used in this guide. Most image-to-pixel-art tools stop at a preview. That is useful for seeing the idea, but it leaves the difficult questions unanswered: How large should the build be? Which real blocks should I collect? How many stacks of each color do I need? This tutorial covers the complete workflow from source image to a blueprint you can construct. 1. Pick an image that survives low resolution Minecraft pixel art works best when the source has one recognizable subject, a clear silhouette, and strong contrast. Logos, flags, game characters, and illustrated portraits usually survive conversion better than photographs with a busy background. Before uploading the image: Crop unused space around the subject Remove distracting background objects Make important features such as eyes or lettering larger Increase contrast if the subject blends into the background ## 2. Choose dimensions by material cost One converted pixel equals one placed block. The total block count is: width × height = total blocks | Size | Total blocks | Best use | |------|-------------|----------| | 16 × 16 | 256 | simple symbols and prototypes | | 32 × 32 | 1,024 | small survival logos and characters | | 64 × 64 | 4,096 | portraits, shading, and medium text | | 128 × 128 | 16,384 | a classic single-map-sized canvas | Doubling both sides multiplies the material count by four. For a first wall build, start between 32 and 64 blocks wide. ## 3. Choose a practical block palette I use three simple palette strategies: Concrete only for logos, flags, cartoons, and saturated colors Survival friendly for accessible concrete, wood, stone, sandstone, moss, and similar materials Full palette when a closer color match matters more than collection cost ## 4. Decide between