标签：#webdev

Indian government exam portals have a rule that has quietly shaped a lot of my code: your photo must be under 50KB . Not "small". Not "optimised". Under 50KB, or the upload is rejected. Every free tool I found for this wanted me to upload the photo to a server, wait in a queue, and create an account. For a file I just wanted to shrink. So I wrote it myself, in the browser. This post is about the actual technique — hitting an exact byte target with canvas — and, honestly, about where my implementation still falls short. The naive version, and why it fails The obvious approach: canvas . toBlob ( blob => download ( blob ), ' image/jpeg ' , 0.7 ); Pick a quality, hope for the best. The problem is that JPEG quality has no predictable relationship to output size. Quality 0.7 on a flat, low-detail portrait might land at 18KB. The same 0.7 on a noisy, high-detail photo lands at 210KB. You cannot compute the quality you need — the encoder decides, and it depends entirely on image content. So you can't calculate it. You have to search for it. Binary search on quality toBlob is cheap enough to call repeatedly, and quality is monotonic — higher quality never produces a smaller file. That's exactly the setup binary search wants. function encode ( canvas , quality ) { return new Promise ( resolve => canvas . toBlob ( resolve , ' image/jpeg ' , quality ) ); } async function compressToTarget ( canvas , targetBytes ) { let lo = 0.05 , hi = 0.95 , best = null ; for ( let i = 0 ; i < 8 ; i ++ ) { const mid = ( lo + hi ) / 2 ; const blob = await encode ( canvas , mid ); if ( blob . size <= targetBytes ) { best = blob ; // fits — remember it, try for better quality lo = mid ; } else { hi = mid ; // too big — back off } } return best ; } Eight iterations over the range 0.05–0.95 narrows quality to about ±0.002, far finer than anyone can see. Each iteration is one encode; on a typical phone photo the whole loop runs in well under a second. Two details that matter more than they look: Keep

Sharing a holiday home with family or friends is great until the admin starts. Who’s in next weekend? Did someone already claim Easter? Who was meant to book the cleaner? Where’s the WiFi password / insurance cert / “how to winterize the outdoor taps” note? For most groups this lives in five group chats, a half-maintained Google Calendar, and a Drive folder nobody trusts. I kept running into that pattern — so I built Shared Holiday Homes : software for families, friends, and co-owners who already share a place and need less chaos, not another generic calendar. The problem isn’t “finding a free date” Generic calendars are fine at showing blocks of time. Shared holiday homes need more than that: Double-booking protection that isn’t “hope nobody overwrites the event” Rules for peak weeks, min/max stays, booking windows, and optional approval Fairness visibility — who actually used the place this year Named jobs with owners and due dates (cleaning, maintenance, “fix the pump”) A home for house knowledge — docs, arrival notes, appliance quirks, emergency info If your group is small and high-trust, Google Calendar can work. Once you’re coordinating multiple households, peak seasons, and maintenance, the “calendar + WhatsApp” stack starts creating the arguments it’s supposed to prevent. I wrote a longer comparison here if you want the practical breakdown: Shared Holiday Homes vs Google Calendar What I built (and what I didn’t) The product is intentionally narrow. Private co-owner groups don’t need a full property-management system or a fractional-ownership marketplace. They need an operating layer for one shared house. In scope: One shared booking calendar Booking rules / seasonal rotations Shared task list Document library House guides (the handbook people can actually find) Out of scope on purpose: Selling property shares Matching investors Full bookkeeping / STR channel management That boundary mattered. Every time I was tempted to add “just one more admin feature,” I a

Every senior developer, engineering manager, and tech recruiter I’ve talked to recently is echoing...

Handling Asynchronous Webhook Notifications & Callbacks in Joget via BeanShell When integrating Joget DX with external platforms—such as payment gateways, SMS providers, or ERP systems—requests are often processed asynchronously. The external system accepts a request immediately and dispatches an HTTP POST webhook callback to Joget minutes or hours later when processing completes. Receiving webhook callbacks inside BeanShell API endpoints requires two key tasks: Safe Variable Type Coercion: Handling parameter arrays ( String[] ) versus single strings ( String ) safely without throwing ClassCastException . FormDataDao Persistence: Saving or updating the notification payload inside a Joget form database table using FormDataDao . In this guide, we'll write a defensive Java/BeanShell script that receives asynchronous webhook callbacks and logs them cleanly into Joget. Architecture Overview Webhook Endpoint: An external system hits your Joget API endpoint with callback parameters (e.g. process_id , status , response_payload , recipient ). Type Extraction: A safe helper function handles parameter type variations (whether passed via URL query params or JSON request bodies). FormDataDao Save: Instead of executing raw JDBC queries, the script uses FormDataDao to persist a FormRowSet directly into Joget's form storage engine. The BeanShell Script Place this code inside your API Builder BeanShell script or custom REST endpoint: import org.joget.apps.app.service.AppUtil ; import org.joget.apps.form.dao.FormDataDao ; import org.joget.apps.form.model.FormRow ; import org.joget.apps.form.model.FormRowSet ; import org.joget.commons.util.LogUtil ; import java.util.UUID ; // 1. Safe Type Extraction Helper public String safeExtract ( Object param ) { if ( param == null ) return "" ; try { if ( param instanceof String []) { String [] arr = ( String []) param ; return arr . length > 0 ? arr [ 0 ] : "" ; } if ( param instanceof String ) { return ( String ) param ; } } catch ( Throwable t

Generating Multilingual HTML Reports with Attachment Download Links in Joget Creating customized executive report summaries in Joget DX often requires more than simple database lists. Real-world business reports frequently need to join multiple tables, translate status labels based on the user's active locale ( #platform.currentLocale# ), and generate secure file download links for form attachments. In this guide, we'll build a Java/BeanShell script that queries main records and history logs, resolves internationalization ( i18n ) message keys dynamically, and generates interactive HTML reports embedded with secure attachment links. Key Components Dynamic i18n Translation: Uses AppUtil.processHashVariable("#i18n.key#", null, null, null) to convert database status codes into localized text matching the user's language setting. File Attachment Links: Formats secure file download URLs ( /jw/web/client/app/{appId}/{version}/form/download/{tableName}/{recordId}/{fileName} ) so users can open uploaded documents directly from the report summary. Multi-Table SQL Join: Merges main request details, audit transaction history, and custom review tables into a clean HTML document layout. The BeanShell Script Place this code inside a BeanShell Form Bounding Box or an HTML Report Generator tool step: import java.sql.Connection ; import java.sql.PreparedStatement ; import java.sql.ResultSet ; import java.net.URLEncoder ; import javax.sql.DataSource ; import org.joget.apps.app.service.AppUtil ; import org.joget.apps.app.model.AppDefinition ; import org.joget.commons.util.LogUtil ; // Helper: Resolve i18n hash variables dynamically public String getLocalizedText ( String messageKey ) { if ( messageKey == null || messageKey . isEmpty ()) return "" ; String hashVariable = "#i18n." + messageKey + "#" ; return AppUtil . processHashVariable ( hashVariable , null , null , null ); } String recordId = "#requestParam.id#" ; if ( recordId == null || recordId . trim (). isEmpty ()) { return "<di

If you've ever used an online PDF tool, you know the routine. You need to merge two files. You find a site. You upload. Then: "Sign in to download." "Free plan: 2 tasks per day." "Your result is ready — with our watermark on it." "Upgrade to remove limits." A five-second job turns into an account, a countdown, and a branded output you can't send to a client. That's the friction that made me build PDFKing — 23 PDF tools in one place, with none of the catches. No sign-up. No watermarks. No daily limits. Nothing to install. The Problem With Most "Free" PDF Tools "Free" almost always has a shape: Free tier, capped at a couple of tasks a day Sign-in wall before you can download Watermark on the output unless you pay File-size limits that push you to a premium plan None of that is about the PDF. It's about converting a person in a hurry into an account. I wanted the opposite: open the tool, do the job, close the tab. No relationship required. The Approach A few principles shaped everything: Every common PDF job in one place — no bouncing between five single-purpose sites Name tools by what they do , not by how the code works Same short flow for all of them — pick, add file, run, download Nothing gatekept — no login, no watermark, no per-day counter What's Actually In It 23 tools, grouped by what you're trying to do. Organise & optimise Merge, Split, Compress, Organise pages, Delete pages, Extract pages, Rotate, plus an Image Compressor for JPG/PNG/WEBP. Convert to & from PDF PDF to Word, Word to PDF, HTML to PDF, JPG to PDF, PDF to JPG, PDF to Text. Secure & sign Watermark, Sign, Redact, Protect (password), Unlock. Edit Crop, Add page numbers, Edit PDF (text, shapes, highlights, annotations), Edit metadata. The ones people hit first: Merge PDF , Compress PDF , PDF to Word , and Sign PDF . Privacy Isn't a Feature, It's the Default With PDFKing: there's no account, so there's nothing to log against you [confirmed on site] there's no watermark added to anything you make [con

How to Update Joget App Environment Variables Programmatically in BeanShell In Joget DX, App Environment Variables are commonly used to store global configuration values—such as API endpoints, tax rates, batch counter sequences, or feature flags. While administrators can update these variables manually through Joget App Center, enterprise workflows often need to update environment variables programmatically (for example, incrementing a daily batch sequence counter or updating an OAuth access token). In this guide, we'll write a short BeanShell script using Joget's EnvironmentVariableDao to fetch and update App Environment Variables dynamically. How It Works Obtain App Context: AppUtil.getCurrentAppDefinition() retrieves the active application definition. Access the DAO Bean: AppUtil.getApplicationContext().getBean("environmentVariableDao") retrieves Joget's internal DAO for environment variables. Load & Update: environmentVariableDao.loadById(envVarId, appDef) retrieves the target variable instance. Modifying .setValue() and executing environmentVariableDao.update(envVar) persists the updated value immediately. The Code Place this BeanShell snippet inside a BeanShell Tool workflow step or a Form Post-Processing Tool : import org.joget.apps.app.dao.EnvironmentVariableDao ; import org.joget.apps.app.model.AppDefinition ; import org.joget.apps.app.model.EnvironmentVariable ; import org.joget.apps.app.service.AppUtil ; import org.joget.commons.util.LogUtil ; public void updateAppEnvironmentVariable ( String variableId , String newValue ) { AppDefinition appDef = AppUtil . getCurrentAppDefinition (); if ( appDef != null ) { // Retrieve Joget's Environment Variable DAO bean EnvironmentVariableDao envDao = ( EnvironmentVariableDao ) AppUtil . getApplicationContext (). getBean ( "environmentVariableDao" ); // Load target environment variable by ID EnvironmentVariable envVar = envDao . loadById ( variableId , appDef ); if ( envVar != null ) { LogUtil . info ( "EnvVar Manager

Custom Cell Renderers & Action Buttons in Joget Spreadsheet Elements The built-in Spreadsheet Element in Joget DX provides a spreadsheet-like interface for managing tabular records inside forms. However, standard spreadsheet columns only support basic text or dropdown inputs out of the box. If you want to add row-level action buttons (like a Delete Row button) or turn plain cell text into an interactive Modal Popup Link , you can supply custom Handsontable renderer functions directly inside your Spreadsheet column properties. In this guide, we'll look at two practical examples: adding a custom row-deletion button and rendering interactive drill-down links. Example 1: Adding a Custom Delete Row Button In your Joget Spreadsheet element, open column properties for an action column and configure the custom renderer function below: {{ renderer : function ( instance , td , row , col , prop , value , cellProperties ) { // Render custom HTML button inside the cell td . innerHTML = " <button type='button' class='btn-delete-row'>Delete</button> " ; td . style . textAlign = " center " ; // Attach click handler to remove the target row from the Handsontable instance const btn = td . querySelector ( " .btn-delete-row " ); btn . onclick = function ( e ) { e . preventDefault (); e . stopPropagation (); // Get underlying Handsontable instance from the form field const hotInstance = FormUtil . getField ( " your_spreadsheet_field_id " ). data ( " hot " ); if ( hotInstance ) { hotInstance . alter ( " remove_row " , row ); } }; } }} Key Highlights: instance.alter("remove_row", row) removes the target row directly from the underlying data model. e.stopPropagation() prevents Handsontable from entering cell-edit mode when the button is clicked. Example 2: Interactive Drill-Down Popup Links To display a clickable link in a grid cell that opens a detailed record inside a Joget modal dialog (popup iframe), use this cell renderer: {{ renderer : function ( instance , td , row , col , prop , va

We need to talk about the way we launch products, because right now, the architecture is fundamentally flawed. Launching on the standard major platforms today is the marketing equivalent of renting RAM. You get a massive spike in resources on Day 1, it looks amazing on your dashboard, but by Day 30, the garbage collector comes along and wipes your traffic back to zero. I recently dug into the analytics of 2026 SaaS launches, and the reality is brutal: a directory launch is just borrowed reach. You are renting a platform's homepage for 24 hours. Worse, the ecosystem has become a pay-to-win script. Funded startups are paying "launch agencies" $2,000+ to optimize their assets, schedule their upvotes, and game the leaderboards. As solo developers, we don't need a 24-hour spike. We need persistent state . We need SEO and dofollow backlinks. A backlink from a high Domain Authority site compounds over time. A "Product of the Day" badge is just /dev/null a week later. I got so annoyed by this that I started hacking on a concept called Flamas (flamas.io) to see if a "backlinks over badges" model could actually work. The idea is to build a daily board that rewards genuine maker upvotes with permanent SEO value, rather than just a 24-hour traffic burst. But I’m stuck on the system design and need your ideas: If you were building a community-driven launch board from scratch, how would you design the ranking algorithm? What parameters or rate-limits would you use to ensure it stays fair for solo devs and bulletproof against paid bot agencies? Drop your logic in the comments. I’m treating this as an open whiteboard and want to build the solution based on how actual founders think. 👇

I've been having the same conversation with my colleagues for months. What does it mean to create...

Most guides about Google Forms and your website answer a question you did not ask. Search for how to replace a Google Form with your own HTML and you get three kinds of answer. Embed the iframe but style the container. Use a service that hides Google's branding. Or the clever one: build your own HTML form and point it at Google's endpoint, so responses still land in your existing spreadsheet. All three keep Google Forms in the loop. If that is what you want, they work, and I will show you the third one because it is genuinely useful when you need it. But if you actually want the Google Form gone, replaced by markup you own, here is how that works and what it costs you. One-line summary: Google Forms does one thing your static site can't, accept a POST; swap that for a form endpoint and you get your markup back, at the cost of owning spam and losing free-unlimited. Why the iframe is the problem The embed is an iframe. That means: You cannot restyle it. Your fonts and colours stop at the border. It does not resize with its content, so a long form becomes a scroll area inside your page. It looks like Google on your site, because it is. You inherit its accessibility behaviour and can do nothing about it. None of that matters for an internal survey or a sports club sign-up sheet. It matters a lot on a business site, where a Google-branded iframe reads as a stopgap someone never got round to replacing. The clever workaround, and where it breaks You can POST your own HTML form straight at a Google Form's response endpoint. Open your form, inspect the page, dig the field IDs out of the markup, and build a form whose input names match: <form action= "https://docs.google.com/forms/d/e/YOUR_FORM_ID/formResponse" method= "POST" > <input name= "entry.1234567890" type= "email" required > <textarea name= "entry.9876543210" required ></textarea> <button type= "submit" > Send </button> </form> Responses land in the same spreadsheet. No new service. For a throwaway internal page, thi

I had a simple-sounding question: does ChatGPT recommend this business? You'd think you just ask it. Ask ChatGPT "best personal injury law firm in NYC", see if the business is named, record yes or no. That works exactly once. Ask again an hour later and you might get a different answer. Not slightly different — potentially a completely different set of firms and a completely different set of cited sources. Which means the naive version of this measurement is worthless. You're not measuring visibility, you're sampling a distribution once and calling it a fact. This is the same problem anyone gets when they try to test an LLM-backed feature. Your normal testing instinct — same input, assert on output — just doesn't apply. So here's how I ended up designing around it, and the numbers that came out, which surprised me. The setup I wanted to compare four assistants (GPT-4o, Claude Haiku 4.5, Gemini 2.5 Flash, Perplexity Sonar, all with web search on) across 10 buyer-intent questions in one vertical. Something like: "Best personal injury law firm in New York City?" "Top immigration lawyers in Mumbai?" For each response I recorded two things: which businesses got named, and which URLs got cited. The cited sources come from each API's own citation metadata, so that part is structured — no scraping the prose. First pass, the results looked dramatic. The four assistants barely agreed on anything. Different firms, different sources, almost no overlap. Great finding. Except I couldn't publish it, because there was an obvious objection I couldn't answer: Maybe they weren't disagreeing with each other. Maybe each one was just disagreeing with itself. If a single assistant returns wildly different sources run to run, then "these four models cite different things" is a meaningless statement. You'd be measuring noise and calling it signal. The control The fix is the same idea as a control group. Measure the thing you're worried about, separately, and see if it explains your result.

Every few months I hit the same wall. A client sends over a .mov file that needs to end up as an .mp4 for a web page, or someone drops a .heic photo in Slack and asks why it "won't open" on their Windows machine. My first instinct used to be brew install ffmpeg and then spend twenty minutes remembering the flags. These days I don't bother unless the job actually needs scripting or batch automation. Here's what's actually in my rotation, and when I reach for each one. When it's a one-off file and I just need it done If I'm not going to touch this format again for another six months, I'm not installing anything. Browser-based converters have gotten good enough that for a single file, they're just faster. CloudConvert is usually my first stop for anything document or spreadsheet related — it handles a wide range of formats and the interface doesn't get in the way. AhaConvert is what I use when it's image or audio work specifically; it's fully browser-based, no account needed, and it deletes uploaded files automatically after 24 hours, which matters if the file has anything client-confidential in it. Neither one requires me to think about dependencies or version conflicts, which honestly is 90% of why I use them. For quick audio grabs — pulling an MP3 out of a video file someone sent, or converting an old .wma voice memo — I've had good results with Online-Convert too. It's not pretty, but it's reliable and doesn't nag you to create an account. When I need to batch-process a folder This is where the browser tools stop being useful and FFmpeg earns its keep. If I'm converting 200 images or normalizing audio levels across a podcast archive, nothing beats a script I can rerun. for f in * .wav ; do ffmpeg -i " $f " -acodec libmp3lame " ${ f %.wav } .mp3" done I know this loop by heart at this point. If you're doing this regularly, it's worth the setup pain once and never thinking about it again. When it's part of a pipeline If file conversion is happening inside an app — sa

Have you ever been in a situation where you needed immediate help from someone nearby? Maybe you needed a blood donor, a local electrician, emergency transportation, pet care, or just someone in your neighborhood who could help quickly. Finding the right person at the right time isn't always easy. That's exactly why I built NeighborHelp — a modern community platform that helps people connect with nearby neighbors and provide or receive help in real time. 🌐 Live Demo: https://neighborhelp99.vercel.app 💡 What Makes NeighborHelp Special? 📍 Smart Location-Based Help NeighborHelp uses real-time location to show nearby help requests with distance filters like: Within 5 km Within 10 km Within 25 km Anywhere This makes finding nearby help simple and fast. 💬 Real-Time Chat Users can instantly communicate using a built-in chat system powered by Socket.io. Features include: Online status Live typing indicators Instant messaging Everything updates in real time without refreshing the page. 🔔 Instant Notifications Urgent requests shouldn't wait. NeighborHelp instantly sends: Web Push Notifications Automated Email Alerts so people can respond as quickly as possible. 🏆 Community Reputation System Helping others deserves recognition. The platform includes: 10-level badge system Reputation points Community success stories to encourage active participation and build trust. 🤖 NeighborBot AI Assistant An integrated AI assistant helps users by: Answering common questions Guiding new users Suggesting helpful actions Making the platform easier to use 🛡️ Secure Authentication Security was one of my top priorities while building this project. Features include: JWT Authentication Express Rate Limiting OTP-based Password Recovery Protected APIs 🛠️ Tech Stack Frontend Next.js 16 React Tailwind CSS Vanilla CSS Backend Node.js Express.js Socket.io Web Push API Database & Deployment Supabase PostgreSQL Vercel 💻 What I Learned Building NeighborHelp from scratch helped me gain hands-on experience wi

How a Missing Database Index Turned an 8ms API into an 8-Second Nightmare Every developer...

Hi everyone, I built a simple, fast, and lightweight online CRC calculator tool for embedded systems and developers. URL: https://crc-calc.com Features: Supports standard CRC polynomials (CRC-8, CRC-16, CRC-32, etc.) Custom polynomial & bit reflection settings No signup required I'd love to hear your feedback or suggestions!

I spend a lot of time in the AI space -- reading papers, building things, talking to engineers who are actually shipping. And there is a gap between what the demos show and what production systems actually look like that nobody is being fully honest about. So here is my honest take on where things actually are. The Problem With How We Talk About AI Agents Everyone is calling everything an "agent" right now. A function that calls a tool? Agent. A chatbot with memory? Agent. A script with a loop? Agent. This dilution is not just semantic. It is causing real engineering mistakes. When you do not have a precise definition for what you are building, you end up over-engineering simple pipelines and under-engineering genuinely complex ones. I have seen teams spend weeks adding "agentic" orchestration to workflows that would have been fine as a single well-structured prompt. Here is the definition I keep coming back to: an agent is a system that has an objective, not just an instruction. It decides what to do next. It handles failure. It knows when it is done. Everything else is just a fancy function call. 🟢 If your system needs a human to tell it each step, it is not an agent. It is a chat interface. 🔵 If your system can recover from a failed tool call and try a different approach, you are getting somewhere. ✅ If your system can decompose a goal into subtasks and delegate them, that is the real thing. What Is Actually Happening in Production Right Now The honest picture from teams I follow and talk to: Most real agent deployments are narrow. They do one thing well. Customer support triage. Document extraction. Code review on a specific codebase. They are not general-purpose reasoning engines. They are purpose-built pipelines with some intelligence in the decision layer. The teams getting good results are not chasing the latest model release. They are obsessing over: ☑️ Tool design -- what can the agent actually call, and how clean is the interface ☑️ Failure handling -- wh

I spend a lot of time in the AI space -- reading papers, building things, talking to engineers who are actually shipping. And there is a gap between what the demos show and what production systems actually look like that nobody is being fully honest about. So here is my honest take on where things actually are. The Problem With How We Talk About AI Agents Everyone is calling everything an "agent" right now. A function that calls a tool? Agent. A chatbot with memory? Agent. A script with a loop? Agent. This dilution is not just semantic. It is causing real engineering mistakes. When you do not have a precise definition for what you are building, you end up over-engineering simple pipelines and under-engineering genuinely complex ones. I have seen teams spend weeks adding "agentic" orchestration to workflows that would have been fine as a single well-structured prompt. Here is the definition I keep coming back to: an agent is a system that has an objective, not just an instruction. It decides what to do next. It handles failure. It knows when it is done. Everything else is just a fancy function call. 🟢 If your system needs a human to tell it each step, it is not an agent. It is a chat interface. 🔵 If your system can recover from a failed tool call and try a different approach, you are getting somewhere. ✅ If your system can decompose a goal into subtasks and delegate them, that is the real thing. What Is Actually Happening in Production Right Now The honest picture from teams I follow and talk to: Most real agent deployments are narrow. They do one thing well. Customer support triage. Document extraction. Code review on a specific codebase. They are not general-purpose reasoning engines. They are purpose-built pipelines with some intelligence in the decision layer. The teams getting good results are not chasing the latest model release. They are obsessing over: ☑️ Tool design -- what can the agent actually call, and how clean is the interface ☑️ Failure handling -- wh

Hoi hoi! I'm @nyaomaru, a frontend engineer who was recently knocked off my feet by how good the...

cal.com, Calendly, zcal... booking SaaS isn't short on options, and most of them are genuinely decent. Free tiers cover the basics for a lot of freelancers. The catch: you're the product (nothing's really free), and your customer data lives somewhere you don't fully control and can't fully audit. A dysfunction I ran into on another SaaS tool was the trigger. Trusting a third-party service by default, just because it's widely used and billed monthly, doesn't always hold up. That episode was enough to make me reconsider every external service this site was relying on for functionality that's actually simple to self-host — and the booking widget, running on Calendly, was one of them. Nothing wrong with Calendly specifically. It worked fine. But structural friction had been building regardless: a recurring subscription for something as simple as displaying open slots and recording a choice, a hard dependency on a third party for a component with nothing exceptional about it technically, and customization capped by whatever the vendor exposes in settings — no way to go further if a need falls outside that box. On top of that, an integration constraint that mattered more than any of the above: the site runs on Astro, generating lightweight static pages by design, specifically to avoid the weight of third-party scripts and dependencies — the exact opposite of what embedding a SaaS widget implies. So: could a self-hosted alternative match the experience, without the monthly bill and without handing a core commercial function (people booking a call with me) to an external vendor? This is the write-up of that search, the codebase audit that came out of it, and the production rollout. The landscape Four self-hosted candidates stood out as genuinely comparable — not just UI skins sitting on top of someone else's API, not just internal-scheduling tools with the public-facing UX as an afterthought. CloudMeet — Svelte + TypeScript, deployed on Cloudflare Pages/Workers/D1, free-tie