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Disclosure up front: I'm affiliated with diagram.now — I'm connected to the product. I'm posting this to get developer feedback on diagram import/export interoperability, not to pitch an install. Most teams I've worked with don't have one source of truth for their diagrams. They have: a few Mermaid blocks living in READMEs and Markdown docs, an old Visio ( .vsdx ) or Lucidchart file someone made two reorgs ago, a SQL schema that is secretly the "real" ERD, and a pile of screenshots pasted into docs and tickets. The diagram is rarely the hard part. The hard part is that the same diagram lives in five formats and none of them stay in sync with the docs they're supposed to explain. I've been working on diagram.now , a browser-based editor for technical diagrams — flowcharts, UML, ERD, BPMN, cloud/network architecture, mind maps, wireframes. It's a free browser editor with no signup to start. There's an optional Confluence app for teams that want diagrams editable inside Confluence pages, but that's intentionally not what I want to talk about here. I want feedback on the editor itself, and specifically on the interoperability story. What it does today Import/insert from Mermaid and SQL — paste a Mermaid graph or a CREATE TABLE block to start an editable diagram instead of a static render. Import Lucidchart and Visio .vsdx files — this is migration-oriented, and honestly the part I most want real-world files to stress-test. Export to PNG, SVG, PDF, or a URL. Templates/shapes for the diagram categories above. I'm deliberately keeping the Confluence side secondary. The thing I actually want to learn is whether the browser editor plus import/export is useful on its own. Where I'd love feedback Imports: Which format matters most to you — Mermaid, SQL→ERD, .vsdx , Lucidchart, or something else (PlantUML, draw.io XML, Graphviz)? If you've ever tried to migrate diagrams between tools, where did it break? URL export: Is a shareable diagram URL genuinely useful in your workflow (

Most AI coding assistants do not really remember your project . They remember just enough to be dangerous . They see the latest prompt, skim a few files, improvise, and then forget the reasoning that made the answer useful five minutes ago. That is fine for toy demos. It breaks down fast inside a real software codebase. In Knotic I take an harder line . Instead of treating memory like a chat log with extra lipstick, I treat memory as infrastructure . Project knowledge is separated from session knowledge. Source material is separated from condensed understanding . Old context is compressed instead of blindly dragged forward. The result is a system that feels less like autocomplete with a caffeine habit and more like an AI engineering partner that can stay oriented over time. If you care about AI coding assistant memory , context engineering , persistent project memory , or long-term memory for software development , this is the part worth paying attention to. The Real Problem With AI Memory in Coding Tools The average AI IDE has the same failure mode . It looks smart on the first turn and shaky on the fifth . Why? Because software work is not just about answering the latest question. It is about carrying forward constraints, architecture, naming conventions, decisions, tradeoffs, dead ends, file relationships , and the exact context of the change in progress. When an assistant does not separate those layers, everything gets mixed together . Stable project facts sit next to temporary tool output. Important decisions compete with random noise. The model burns tokens re-reading the same files, or worse, works from partial memory and starts making up the missing pieces . Knotic solves this by splitting memory into distinct layers , each with a clear job. That design choice sounds simple. In practice, it changes everything . Knotic Does Not Use One Memory. It Uses Three. Knotic's memory model is built around three different kinds of context . The first is long-term projec

The Prizes for the SecDim AppSec Village Wargame CtF at DEF CON 34 has been announced! Build and submit a challenge and get a chance to Win a ROG Xbox Ally if your challenge submission wins 1st Place! Challenge submissions close on 31 July. https://sessionize.com/appsec-village-wargame

Most AI tools give Australian small businesses American advice. An Aussie tradie running Xero does not need to hear about QuickBooks. A cafe owner with three casual staff has Fair Work Act obligations that no generic "automate your business" tool will surface. I built AppZ AU Business Advisor to fix this -- a free tool powered by Gemini 2.0 Flash that generates personalised automation blueprints with real Australian business context. This post covers the technical decisions, the prompt engineering approach, and why the AU-specific scaffold makes all the difference. The Problem with Generic AI Business Advice When you ask a general AI "how should I automate my business?", the training data skews heavily American. You get advice about QuickBooks, not Xero. About W-9 forms, not BAS lodgement. About 401k, not superannuation. For an Australian sole trader approaching the $75k GST registration threshold, this is not just unhelpful -- it is actively misleading. The compliance obligations are different. The software ecosystem is different. The pain points are different. The Prompt Scaffold Approach Instead of injecting "you are talking to an Australian business" as a keyword, I built a reasoning scaffold -- a structured context block the model uses as a knowledge foundation: AUSTRALIAN BUSINESS CONTEXT: - GST: 10%, mandatory registration at $75k annual turnover - BAS: lodged quarterly (or monthly for large businesses) to the ATO - Superannuation: 11.5% employer contribution, paid per payroll from July 2026 - ATO tools: STP Phase 2 mandatory for all employers - Dominant accounting platforms: Xero, MYOB, Reckon (not QuickBooks) - Fair Work Act: award rates, leave entitlements, payslip requirements - Key software by vertical: ServiceM8 (trades), Deputy (hospitality), Cliniko (health) This is not a keyword list -- it is a reasoning foundation. When a tradesperson mentions "invoicing problems", the model now reasons about Xero integrations, GST-inclusive invoicing, and BAS categ

En una gestoría del automotor, consultar las multas de un auto era entrar a 33 sistemas distintos (Provincia, CABA, municipios), cada uno con su captcha y sus caídas. Lo automatizamos con una sola API, la de Multita , y comparto cómo quedó porque sirve a cualquiera que arme herramientas para el rubro automotor o fintech en Argentina. El problema Las infracciones de tránsito en Argentina no viven en un solo lugar. Hay sistemas provinciales (Buenos Aires, Santa Fe, Entre Ríos, Misiones, Chaco, Salta, Mendoza) y municipales (decenas). Ninguno habla con el otro. Consultar a mano son 15 a 20 minutos por vehículo. La solución: una request, todas las jurisdicciones La API de Multita recibe una patente, un DNI o un CUIT y devuelve, en JSON, las actas de cada jurisdicción con su monto y su estado. curl -X POST https://multita.com.ar/api \ -H "X-Api-Key: TU_KEY" \ -H "Content-Type: application/json" \ -d '{"tipo":"patente","valor":"AB123CD","jurisdicciones":"todas"}' { "resultados" : [ { "jurisdiccion" : "pba" , "nombre" : "Provincia de Buenos Aires" , "cantidad_actas" : 2 , "total_oficial" : 418500 }, { "jurisdiccion" : "caba" , "nombre" : "CABA" , "cantidad_actas" : 1 , "total_oficial" : 95000 } ], "resumen" : { "cantidad_actas" : 3 , "total_oficial" : 513500 } } Lo que nos ahorró Pasamos de 15-20 minutos por auto a segundos, y de cuatro ventanas abiertas a una sola llamada. Para una gestoría que cotiza decenas de carteras por día, es la diferencia entre atender 10 clientes o 30. Datos clave Cubre 33 jurisdicciones argentinas (provinciales y municipales), por patente (dominio), DNI o CUIT. Respuesta en JSON al instante; opcional, el total ya cotizado con tu pricing. Hay también una consulta web gratis para probar sin integrar nada. Si tenés una gestoría o estudio y querés esto andando sin programar, escribinos a BA Gestoría y te lo dejamos listo (y un descuento si venís de este post). Docs de la API: https://multita.com.ar/api

You change one value in a JSON config, run git diff , and get a wall of red and green — because a formatter reflowed the file, or the serializer reordered the keys, or the indentation shifted by two spaces. The one thing you actually changed is buried in there somewhere. Good luck finding it in review. The problem is that git diff and diff work on lines . JSON isn't lines — it's a tree. So I built jdelta : it compares the data , ignores key order and whitespace entirely, and tells you exactly which values changed, addressed by path. Zero dependencies, no network. What it looks like $ jdelta config.before.json config.after.json Added (1) + features.darkMode true Changed (2) ~ auth.required true → false ~ server.port 8080 → 3000 +1 -0 ~2 auth.required flipped to false and you can see it instantly — no scrolling past 200 lines of reindented braces. Why not git diff / diff ? Because they diff text. Run a formatter, sort your keys, change two spaces of indent, and a line differ lights up the whole file while reporting zero semantic change. jdelta parses both sides and walks the trees: Object keys show as user.profile.age ; array elements as items[2].price . Odd keys fall back to quoted brackets ( ["order-id"] ). A key on only one side is added / removed ; a key on both with a different value is changed . A type change ( number → string , object → array ) is one changed entry tagged with the kinds — not a confusing add + remove. Reordered keys and whitespace produce nothing , because they aren't data changes. Great for reviewing config changes, API-response snapshots, tsconfig / settings.json , lockfile-adjacent files, and test fixtures. In scripts and CI jdelta a.json b.json --json # machine-readable: {added, removed, changed, summary} jdelta a.json b.json --quiet # just the +a -r ~c line jdelta a.json b.json --exit-code # exit 1 if they differ — gate a pipeline on it Install npx jdelta a.json b.json # Node build (npm) pip install jdelta # Python build — same behavior Tw

A pip-installable CLI tool that auto-centers off-center page number annotations created by macOS Preview, or batch-adds new ones — with smart content avoidance and landscape support. The Problem If you've ever used macOS Preview to add page numbers to a PDF (via the text annotation tool), you know the pain: numbers land wherever you drop them, never centered, and manually positioning dozens or hundreds of them is soul-crushing. Especially when the PDF has mixed portrait and landscape pages. I ran into this preparing a thesis — 200+ pages of final manuscript, page numbers visibly off-center on every single page. Editing each one by hand wasn't an option. The Solution pdf-pagenum is a single CLI command that reads a folder of PDFs and centers every page number annotation to the bottom of its page. It works by: Detecting FreeText annotations that look like page numbers Measuring body content boundaries on each page Repositioning the annotation to a clean, centered position below the content — with proper margins Preserving the original page dimensions (no resizing, ever) If your PDF has pages with no annotations at all, it can generate new page numbers from scratch in the correct position. Install pip install pdf-pagenum That's it. PyMuPDF and natsort come along as dependencies. Usage Fix Mode (default) Reposition existing page number annotations so they're centered at the bottom: pdf-pagenum ./scans/ ./output/ This is the mode you'll use 90% of the time — it takes whatever rough page numbers Preview gave you and snaps them to the mathematically correct center. Add Mode Generate brand-new page numbers on pages that lack them: # Number all pages starting from 1 pdf-pagenum ./scans/ ./output/ --add all # Number pages 3 through 7 only pdf-pagenum ./scans/ ./output/ --add 3-7 # Number specific pages, starting count from 10 pdf-pagenum ./scans/ ./output/ --add 1,3,5-7 --start 10 Ranges and comma-separated lists can be mixed freely. Start Offset The --start N flag works in b

Hey pessoal! 👋 Queria compartilhar um projeto que venho desenvolvendo para resolver um problema comum: encontrar uma biblioteca de ícones limpa, consistente e fácil de integrar, sem peso desnecessário no projeto. Apresento o coreIcons — uma coleção organizada de 352 ícones de desenvolvimento feita para workflows modernos. 📦 Por que o coreIcons? Leve e Rápido: Impacto mínimo no carregamento das suas aplicações. Organizado: Desenvolvido com uma grade (grid) e estilo totalmente consistentes. Focado no Dev: Feito para se encaixar perfeitamente nos seus projetos de frontend ou full-stack. 🚀 Como Usar É super simples! Basta acessar a nossa demonstração ao vivo, navegar pela coleção e clicar em qualquer ícone. O sistema vai fornecer instantaneamente a URL correta ou o snippet do ícone escolhido para você copiar e usar na hora. 🌟 Apoie o Projeto & Conecte-se! Se você achar essa biblioteca útil, por favor, considere deixar uma estrela ⭐️ no nosso repositório do GitHub ! Seu apoio ajuda o projeto a crescer e a alcançar mais desenvolvedores na comunidade. Também quero deixar um agradecimento enorme a todos que apoiam projetos open-source, contribuem com feedbacks e ajudam a construir um ecossistema melhor para todos nós. Vamos construir juntos! 🔗 Acesse o projeto Repositório no GitHub: https://github.com/mauriciospark/coreIcons Demonstração / Site: https://mauriciospark.github.io/coreIcons/ O que você achou? Deixe suas ideias, feedbacks ou sugestões nos comentários abaixo! 👇 Hey everyone! 👋 I wanted to share a project I've been working on to solve a common problem: finding a clean, consistent, and easy-to-integrate icon library without overhead. Meet coreIcons — an organized collection of 352 development icons built for modern workflows. 📦 Why coreIcons? Lightweight & Fast: Minimal footprint for your applications. Organized: Designed with a consistent grid and style. Developer-Centric: Built to fit smoothly into your frontend or full-stack projects. 🚀 How to Use It's extremely

I run a hosting company. I'm also a BCA student. These two things coexist somehow. GigaNodes started in 2022 as a game server hosting brand — Minecraft, FiveM, ARK, the usual. Over time it grew into a proper VPS and dedicated server operation under GigaNode Technologies Private Limited, with AMD EPYC 7C13 hardware co-located at Yotta DC Noida. Earlier this year we did something I haven't seen any other Indian hosting provider do: we deployed Cloudflare Magic Transit across our entire network. What that actually means Magic Transit is not Cloudflare CDN. It is not a proxy. It is Cloudflare's enterprise network product where your IP prefixes get announced via BGP into Cloudflare's global backbone. All traffic destined for your servers enters Cloudflare's network first, gets scrubbed for attack traffic, and clean packets get forwarded to your data center via GRE tunnel. To deploy it, your infrastructure partner needs to have BGP-level integration with Cloudflare. Individual companies can't just sign up for it. We made it work through our partnership with Advika Datacenters Private Limited (AS135682) at Yotta DC Noida. The result: DDoS traffic never reaches our hardware. Our servers don't see the attack at all. Why no other Indian provider had done this Most Indian hosting providers use blackholing. When an attack comes in, they null-route your IP. Server goes offline. Attack stops eventually. Server comes back. That is the standard. That is what "DDoS protection included" usually means in India. The difference with Magic Transit is that legitimate traffic keeps flowing while attack traffic gets dropped. Your server stays online. Players stay connected. Trades don't get interrupted. We found out pretty quickly this actually works. We took a 1.7 Tbps attack after deployment. The servers didn't notice. A 1.7 Tbps volumetric attack hit our network in May 2026. Cloudflare absorbed it at the edge. No downtime. No support tickets from customers. We found out from the Cloudfla

Originally published on the Prufa blog . Five days ago we audited 49 Show HN launches and found that 78% had a critical bug on day one. This week we pointed the same free audit at a different cohort: 14 products freshly posted to r/SideProject. We expected more of the same. We got the opposite — and it turned out to be more interesting. Not one of the 14 had a critical finding. No broken signup flow, no canonical pointing at the wrong domain, no analytics tag silently swallowing every event. By the measure that matters most on launch day — does the core thing work — these builders shipped clean. And yet every single site had findings. They just all live one tier down, in a layer so consistent it reads like a shared checklist nobody handed out: 11 of 14 sent no analytics events at all. 11 of 14 shipped with no Content-Security-Policy and could be framed by any site (no X-Frame-Options ). 11 of 14 had serious accessibility violations . 12 of 14 had tap targets smaller than 24px on mobile. 9 of 14 took over four seconds to paint their largest element on mobile. 8 of 14 had no canonical link on the entry page. No site is named in this post. The point isn't to embarrass anyone — these are good builders who got a real product live. The point is that the same common side-project launch mistakes show up again and again, and if 11 of 14 strangers have them, you probably have a few too. Methodology, briefly We pulled 20 URLs from recent r/SideProject posts and ran each through the same audit a free Prufa run does: a real browser loads the public pages and captures network traffic, console output, response codes, headers, and the rendered DOM, then a fixed suite of deterministic checks grades the evidence. Same input, same verdict. Of the 20: 14 completed cleanly , 4 were blocked by bot protection before our runner could load them, and 2 didn't finish inside our polling window. The numbers below are from the 14 that completed. Two honest caveats. First, 14 is a small sample —

I open-sourced MarketEye today. For anyone who missed the first post: MarketEye is a self-hosted competitor price monitor I built because I didn't want to pay $99/month for Prisync. The code is now up on GitHub under MIT license. GitHub: github.com/dachengzi065-gif/marketeye Why open source? Three reasons: 1. People actually asked for it. After my first post here, a few people DM'd me asking to see the code. They're developers too — they want to modify it, extend it, make it their own. That's fair. Selling source code to devs is like selling ice to eskimos. 2. Trust. A closed-source price tracker that "runs on your machine" — you either trust the author or you don't. Open source removes that doubt. You can read every line, check what data leaves your machine (nothing), and build it yourself if you want. 3. Longevity. Self-hosted tools have a dirty secret: if the developer disappears, you're stuck with a broken tool. Open source changes that. Even if I get hit by a bus tomorrow, you can fork the repo and keep going. What this means for the $49 version The Gumroad package still exists. It includes: The same code, pre-packaged Email support (I'll help you set it up) A clear conscience subscription (you're paying for convenience, not software) But honestly? If you can run pip install , just clone the repo. It's free. What's next I'm actively working on: Docker image (one-command deploy) More scrapers (plugins for different sites) Discord/Telegram bot alerts (requested by several people) PRs welcome. Issues welcome. Feedback welcome. 👉 github.com/dachengzi065-gif/marketeye

Building a Game with Zero Assets in Godot This is the first game I've ever made. I'm not a developer by trade, I'd never touched Godot before, and I leaned on AI to help me get over the learning curve. But I gave myself one hard rule that ended up shaping the entire project: Zero external assets. No textures. No sprite sheets. No audio files. No music files. The whole repository contains none of them. Everything you see and hear in Reactor Panic - a small arcade game where you sort plasma cores before the reactor melts down - is generated at runtime in code. Here's how I did it, including the parts that went badly wrong. Why do this to myself? Two reasons. First, I can't draw or compose, so "make it all procedural" was weirdly easier than sourcing, creating, and licensing art assets. Second, and this is the part I didn't expect, when everything is code, everything can react to the game state for free. More on that later. Drawing the Reactor All of the 2D art is rendered using Godot's _draw() function. The most involved piece is the containment dome. It isn't a sprite at all - it's shaded per cell like a tiny software renderer. For each cell, I compute a hemisphere surface normal, perform Lambertian diffuse lighting with a specular hotspot, add Fresnel-style rim darkening, and then quantise the result into a handful of discrete steel bands so it reads as pixel art rather than a smooth gradient. # Hemisphere surface normal var sx : = ( mid_x - center_x ) * inv_half_w var sz : = sqrt ( maxf ( 0.0 , 1.0 - sx * sx - sy_sq )) var norm : = Vector3 ( - sx , sy , sz ) . normalized () # Lambertian diffuse var ndotl : = maxf ( 0.0 , norm . dot ( light3 )) var light_val : = 0.1 + ndotl * 0.9 # Fresnel rim darkening (surface curving away from viewer goes dark) light_val *= lerpf ( 0.4 , 1.0 , clampf ( sz * 1.8 , 0.0 , 1.0 )) # Quantise into discrete shade bands -> reads as pixel art var band : = clampi ( int ( round ( light_val * max_band_f )), 0 , num_bands - 1 ) var col : Colo

We Built ARK Because Our Customer Support Was Spread Across 4 Apps The Problem A few months ago, our small team was drowning. Not in customers (well, a little) — but in tabs. WhatsApp open in one window. Instagram DMs in another. A live chat widget buried in a third. Email in a fourth. Every time a customer reached out, someone had to figure out: which channel did this come from, has anyone replied already, and what was the context of the last conversation? The result was predictable: slower replies, repeated questions to customers, and a support workflow that didn't scale past a handful of conversations a day. Why Existing Tools Didn't Fit We looked at the usual suspects — Intercom, Zendesk, Front. They're solid products, but they're built for large support teams with big budgets and dedicated admins. We needed something simpler: a single inbox, AI doing the repetitive work, and a setup that doesn't take weeks to configure. What We Built ARK pulls every customer conversation — WhatsApp, Instagram, Messenger, email, live chat — into one inbox. On top of that, AI handles three things: Drafting replies based on conversation history and context Summarizing long threads so anyone on the team can jump in without reading 40 messages Routing conversations to the right person automatically based on topic or channel The goal wasn't to replace human support — it was to remove the busywork so the team can focus on actually helping people. Where We Are Now ARK is live with a 7-day free trial (auto-renews after that). We're still early, and we're shaping the roadmap based on real feedback from teams managing support across multiple channels. If you're dealing with the same multichannel chaos we were, I'd love to hear how you're handling it — and what's still missing from the tools you've tried. 🔗 https://byark.ai/

Have you ever felt that browsing the web has become "tiring"? We open a browser, search, close a page, then move to another... a dizzying cycle of distracted navigation between sites, while we are essentially looking for "knowledge," not "links." I asked myself: What if browsing was as fluid as scrolling through Facebook, but with the power and accuracy of search engines like Google? I finally decided to turn this idea into reality through my new project, Arabinum. What does Arabinum do? Turning websites into posts: The browser reformats the web so that content appears as fluid feeds, eliminating visual distraction. Smart categorization: No more getting lost; I have divided content into specialized sections like "Videos" and "Research Papers," so you can find what you need in one place. Browsing as a social activity: I added interactive features (Like, Comment, Repost) to make content consumption a collaborative experience rather than a rigid, individual process. I believe the web needs an interface that restores the user's focus, and this project is my attempt to merge the best of the worlds of "Search" and "Social Media." Notes: This is a beta version I launched just to see your thoughts on the idea. This version might not be compatible with small screens yet. This version includes Google Search, YouTube, and scientific papers from arXiv. I look forward to hearing your opinions. The site is free and ad-free, but I need your support to continue due to API and domain costs. I am sixteen years old and a high school student. Finally, I present to you my browser, Arabinum: https://arabinum.amrzlabs.com

When I shipped Trooper , a privacy-aware LLM proxy written in Go, I didn't have a marketing plan. I had GitHub traffic analytics and a habit of checking them obsessively. Seven weeks later, I have something more useful than a viral moment: a ranked table of every traffic spike, what caused each one, and proof that the exact same playbook that worked at launch still works when you have something new to say. What is Trooper? Trooper sits between your app and your LLM provider. When your cloud quota runs out, it automatically falls back to a local Ollama instance with zero code changes on your end. It also tracks session context, so your agents don't go blind between calls. It's not a chatbot wrapper. It's plumbing. Which makes the distribution story more interesting, because plumbing doesn't go viral the way demos do. The Data GitHub gives you 14-day rolling windows for clones and views. I screenshotted them obsessively and tracked every spike. Here's the full ranked table: Rank Date Clones Unique Cloners Views Unique Visitors Driver 🥇 1 May 13 375 173 1,113 ~140 Reddit wave peak 🥈 2 May 10-12 312 137 974 133 Reddit launch spike 🥉 3 Jun 10 289 124 749 101 "Escalate the model" r/ollama post 4 Jun 11 268 112 840 95 Decaying from Jun 10 spike 5 Jun 12 240 99 739 74 Decaying from Jun 10 spike 6 Jun 9 175 102 802 100 Organic 7 Apr 25 174 71 664 113 Early Reddit posts 8 Jun 7 171 110 876 110 Organic recovery 9 Jun 6 163 104 755 102 Organic recovery 10 May 29-30 122 73 610 83 LinkedIn post 11 May 25 76 48 495 53 Claude Code integration chat What I learned 1. Reddit is the only thing that moved the needle, and community fit matters more than size The #1 and #2 peaks were both Reddit-driven. On May 10-11, I posted across r/ollama, r/LocalLLM, r/ClaudeCode, and r/Gemini simultaneously. Total views across those posts: ~7,000. r/ollama alone drove nearly 4,000 of those views. Not r/LocalLLM. Not r/ClaudeCode. r/ollama , the smallest of the four communities. The reason: Trooper so

We open sourced Centaur last month—a Slack agent we built for our own investing and engineering work. Over the past few months it's grown to 100-150 daily power users across a few organizations, handling both judgment-heavy tasks like investment research and raw horsepower work like searching massive codebases. The interesting part isn't just our internal use. We've been running a small Slack Connect with external orgs using it, and the feedback has been consistent: most SaaS tools don't cut it because companies need too much customization and their critical integrations aren't supported out of the box. Our roadmap is getting clearer as we tackle the tricky parts of multi-org collaboration. We're working on scoping Slackbot access by channel, which would finally let different organizations' agents coexist safely in the same space—almost like an Enterprise Matrixbook. But the real challenge isn't the vision, it's execution. Keeping costs low while staying self-hostable for smaller teams has forced us to rethink everything. The hard problems only become obvious once you're deep in the implementation. That said, I do think Slack has won in one sense: it's the best place for a coworker agent to emerge, rather than a standalone application. Curious whether others are seeing this pattern too.

Ever started a new project and found yourself rebuilding the same modal, dropdown, toast notification, tabs, and switches for the 20th time? I got tired of that. So I built UltraHTML , a lightweight UI library that gives you modern components with simple HTML and JavaScript, no framework required. Getting Started Include the CSS and JS files: <link rel= "stylesheet" href= "dist/ultra.css" > <script src= "dist/ultra.js" ></script> Initialize UltraHTML: Ultra . init (); Done. Buttons UltraHTML includes two button styles out of the box: ultra-button — a clean, modern button. ultra-button-wave — adds a wave/ripple-style interaction effect. Basic button: <button class= "ultra-button" > Simple Button </button> Wave button: <button class= "ultra-button ultra-button-wave" > Wave Button </button> Buttons use UltraHTML's default green theme, but because they're standard HTML elements, you can easily customize them with CSS. For example, here's a red button that displays a popup message: <button onclick= "Ultra.popupmsg('Hello from UltraHTML!')" class= "ultra-button ultra-button-wave" style= "background-color: red" > Show Popup </button> You can create buttons that match your site's branding without learning a separate theming system: <button class= "ultra-button" style= "background-color: #3b82f6;" > Blue Button </button> <button class= "ultra-button ultra-button-wave" style= "background-color: #f59e0b;" > Orange Button </button> <button class= "ultra-button ultra-button-wave" style= "background-color: #ef4444;" > Red Button </button> UltraHTML handles the styling and interactions while still giving you full control over how your buttons look. Modal Example Need to display important information? <script> window . addEventListener ( " load " , () => { Ultra . init (); Ultra . modal ({ head : " Important " , text : " You need to reset your password " , buttonText : " Reset " , buttonAction : ( modal ) => { console . log ( " Going to reset page... " ); modal . remove (); } }); }

I've shipped 4 Chrome extensions and 2 VS Code extensions. The advice that always sounds smart — "find a popular extension the dev abandoned, rebuild it better" — is miserable in practice. You open the Web Store, see 100k users and a 4.4 rating, think you found gold, then burn a weekend reading reviews only to realize half the complaints are unfixable traps (sync died, login broke, backend gone). So I built a small pipeline to do the boring part automatically. The method Scrape public Chrome Web Store metadata — users, rating, last-updated date. Filter: 20k–300k users, 18+ months without an update, rating 3.3–4.4 (good enough to prove demand, bad enough to prove pain). Pull up to 50 recent reviews per candidate via public CWS data. Score each one: score = log10(users)10 + months_stale0.5 + feature_request_count2 - trap_count1.5 The key part is trap_count — I subtract points for complaints about sync/login/server issues, because those are unfixable without inheriting someone else's dead backend. High "demand" with high trap count is a mirage. One example Extension Manager — 100k users, 4.4★, last updated ~25 months ago. Looks healthy until you read the 1–2★ reviews: "The site-specific rules feature simply does not work… the core feature advertised is broken." "It won't save any changes made… extensions are re-enabled automatically." A user even posted an RCE report: the dev parses JSON with a Function(str)() fallback — executing arbitrary code from untrusted input. That's not "build a clone." That's "fix the rules engine, kill the eval, add local backup, ship something 100k people already want." The counterintuitive part The highest-scoring extension in my list (200k users, abandoned ~4 years) is actually the worst business opportunity — it's a simple toggle utility whose users will never pay, and the original asks for camera/mic permissions (adware-grade). Raw download counts would put it at the top of your build list. Revenue potential buries it. That gap between "

Ever wanted to turn a simple PHP host into a gateway for your local network? I built host2gateway to do exactly that. ProfiDE / host2gateway Uses a PHP host or web server to create a gateway that securely allows access to clients through it. host2gateway host2gateway is a tool designed to provide access from a web server (Gateway) to a client without requiring static IP addresses, port forwarding, changing firewall rules, or other complex configurations . It is written in PHP and can be deployed on most hosting provider environments. Features No need for static IP or port forwarding: There is no requirement to modify your firewall or router settings. Platform-independent: Works anywhere PHP 8.2 or higher is supported, making it suitable for most shared hosting services. Lightweight and simple: Minimal dependencies and easy deployment. Strong encryption built-in: Uses a powerful encryption mechanism that secures all communication, even if SSL/TLS is not available on the hosting provider. Your data is protected at all times, regardless of your environment. How It Works The client establishes an outbound connection to a Gateway server that is accessible from the internet (a PHP-enabled web host). Both sides communicate… View on GitHub 🔥 What is host2gateway? It's a lightweight tool that transforms any server running PHP into a gateway that can route traffic, manage requests, and act as a bridge between your local network and external services. No heavy dependencies. No complex configs. Just PHP, Cron and a network interface. 🧠 Why I built this Most gateway solutions are bulky, written in Go or Rust, and require root access and system-level changes. But what if you only have: A shared hosting account A basic VPS with PHP enabled A Raspberry Pi running a PHP server host2gateway fills that gap. It gives you gateway-like capabilities using the tools you already have. 🛡️ Use cases Use Case Description Local network bridge Connect isolated parts of your network Traffic inspe

For a long time I found myself checking different European stores to see where products were cheapest. It was slow and annoying. I had to open lots of tabs, change countries, check delivery costs and compare prices myself. I used other comparison websites, but I wanted something simpler. So I decided to build Haggl.eu. Haggl lets you compare prices across Europe from one search, helping you find better deals and save money. This is my first public project and I am learning a lot while building it. The site is still a work in progress, but I have plenty of ideas for the future. I want to add more stores, more countries, price history tracking and better delivery comparisons. My goal is to make it as easy as possible to find the best deal without spending ages clicking through different pages. I would love to hear any feedback or suggestions. Thank you everyone :) https://haggl.eu