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This week's tooling landscape is quieter on the AI-native side but dense with infrastructure moves that affect how AI-driven workloads actually run in production. Cloudflare's Workflows scaling overhaul is the clearest signal: agent-triggered execution is now an assumed pattern, not a novelty, and platforms are rearchitecting accordingly. The rest of the week rounds out with a kernel maintenance drop, a meaningful abstraction removal in tRPC, and a Biome beta that's finally making ESLint replacement feel plausible. Linux 7.1 Released with Driver and Networking Fixes 7.1 is a maintenance release. No architectural changes, no new subsystems—just patches you should care about if you're running affected hardware or kernel-adjacent tooling. The two fixes worth flagging are heap overflows in the USB serial io_ti driver ( get_manuf_info() and build_i2c_fw_hdr() ), plus memory leak corrections scattered across drivers and networking subsystems. Trace tooling also gets updates, which matters if you're doing kernel-level performance analysis on production systems. One operational note: Torvalds is traveling, so merge window latency may be irregular. If you're tracking pull request timelines for custom kernel builds, plan for slippage. Verdict: Ship — if you're on 7.0 and running USB serial hardware or affected networking paths, upgrade on your normal kernel cycle. No breaking changes, no new dependencies, nothing to validate beyond your existing regression suite. tRPC Drops Abstraction Layer for React Query This is the kind of change that looks small in a changelog and feels large in daily development. The new tRPC client exposes native TanStack Query interfaces— QueryOptions and MutationOptions —directly, rather than wrapping them in tRPC-specific hooks. The practical effect: if you're already using TanStack Query elsewhere in your app, you stop context-switching between two similar-but-different mental models. You call .queryOptions() and .mutationOptions() factories and pa

Most SaaS ideas don’t fail because of bad ideas. They fail because the execution gets stuck in an endless setup loop. You start with energy, then slowly get buried in: auth systems, billing, dashboards, SEO, analytics, and infrastructure decisions. By the time the “real product” should begin, momentum is already gone. Here are 7 practical alternatives to building SaaS in a way that never gets finished. 1. Nexora (start with a working SaaS foundation) Instead of rebuilding everything, Nexora gives you a production-ready base so you can focus on actual features. Includes: Authentication system Stripe billing User dashboards SEO pages Blog + docs structure Clean Next.js architecture 🔗 https://nexora.collabtower.com/ 👉 Best for founders who want to ship instead of setup. 2. Build-from-scratch Next.js projects The most common approach. You get: Full control Flexible architecture But you also get: Weeks of setup Repeated boilerplate work High chance of burnout before launch 3. SaaS boilerplates (minimal versions) Lightweight starter kits with: Auth Basic UI Simple Stripe setup But usually missing: Real dashboards SEO systems Production-level structure 4. Supabase-first builds Backend-focused setups. You get: Database Auth APIs But still need to build: Billing UI system Marketing pages SaaS structure 5. Low-code SaaS tools Fast visual builders. Pros: Quick UI creation No heavy coding Cons: Limited flexibility Hard to scale complex SaaS logic Platform dependency 6. AI-generated starter apps AI tools can scaffold SaaS apps instantly. Pros: Fast starting point Cons: Inconsistent structure Requires cleanup Not production-ready out of the box 7. Tutorial-based SaaS builds Many developers still learn SaaS by following tutorials step-by-step. Pros: Educational Cons: Slow Fragmented Hard to turn into real production apps Final takeaway Most SaaS workflows fail before launch because they repeat the same mistake: They start from zero every single time. That creates unnecessary setup

Bash is the easiest language for AI to write and the easiest language to get devastating output from. A 20-line script that "just cleans up old files" can recursively delete a home directory because the model assumed a variable would always be set. A "simple log shipper" can write your secrets to a remote server because the model used set -x for debugging and forgot to remove it. I have run AI-generated bash that I should not have. Most engineers I know have too. After enough close calls, there's a short checklist that catches the worst of it. This is that checklist. The five things to check before running any AI-generated bash 1. Does it start with a strict pragma? The first lines of any non-trivial bash script should be: #!/usr/bin/env bash set -euo pipefail IFS = $' \n\t ' What each does: set -e — exit on any command failure. Without this, a failure in line 5 doesn't stop the script from happily running lines 6-50. set -u — error on undefined variables. This is the one that saves you from rm -rf $UNDEFINED/ . set -o pipefail — propagate failures through pipes. Without it, failing-command | grep something succeeds because grep succeeds. IFS=$'\n\t' — sane field splitting. Defends against word-splitting bugs in filenames. If the AI-generated script doesn't have these, add them and re-read the script. You'll often discover bugs the pragma now flags. 2. Is every variable expansion quoted? # Wrong rm -rf $TARGET_DIR # Right rm -rf " $TARGET_DIR " The wrong version is what causes the "I deleted the root directory" stories. If $TARGET_DIR is empty or contains a space, the command becomes rm -rf (delete current directory) or rm -rf foo bar (delete two unintended things). Models default to the wrong version about half the time because the right version is harder to write in chat ("escape the quotes!") and the wrong version is what most blogs show. Fix: When reading AI bash, mentally check every $VAR for quotes. Add them if missing. This is the single biggest source of bas

# EnvForge BETA v1.1 ⚡ Structured .env scaffolding for modern applications. Generate, validate, and protect environment variables for 14+ services – without ever opening a docs page. Github repo [ https://github.com/Jos3456/envforge ] NPM version (https://img.shields.io/npm/v/envforge-dev) MIT License (https://img.shields.io/badge/License-MIT-yellow.svg) ## Installation bash npm install envforge-dev **Requirements:** Node.js 18 or later. --- **## Quick Start** bash # 1. Generate an .env file and choose your providers envforge init # 2. Fill in your actual credentials envforge fill # 3. Check everything is set correctly envforge validate ## All Commands ### Scaffolding Command What it does envforge init Create a new .env by selecting providers interactively envforge add <provider> Add variables from a specific provider to your existing .env envforge preset Generate a .env from a popular stack preset envforge example Create a safe‑to‑commit .env.example file envforge fill Interactively enter values (secret keys are masked) envforge list Show all built‑in and custom providers ### Guardrails Command What it does envforge validate Check that all required variables are filled in envforge scan Detect secret keys accidentally exposed in frontend code envforge hook install Install a pre‑commit hook that runs validate + scan ### Customisation Command What it does envforge provider add Create a custom provider template envforge registry update Download the latest providers from the community registry ## Built‑in Providers Category Providers Database Supabase, Neon, MongoDB Atlas Auth Clerk, Auth0, Firebase AI OpenAI, Anthropic (Claude) Payments Stripe Email Resend, SendGrid Storage Cloudinary, AWS S3 / Cloudflare R2 Other Vercel Missing a provider? Add your own with envforge provider add or contribute one to the community. ## Framework‑Aware Scanning Use --framework for smarter detection: # Next.js specific rules (app/ vs pages/, "use client") envforge scan --framework next Th

Quick update to anyone interested in upcoming changes to the PAGI project (spiritual successor to Plack/PSGI). 1) Distribution split up: when we released PAGI, we initially released everything as one distribution. PAGI ( https://metacpan.org/pod/PAGI ) currently has a) the PAGI specification; b) the reference server and c) a bunch of ease of use tools, similar to the role that the Plack distribution played for PSGI. Putting everything into one place was just to make my life easier as in the early bunch of releases there was a lot of fixes and updates, most of which cut across all three parts of PAGI. Also I wanted to make it easy for people getting into PAGI to be able to explore the ecosystem. However now that code seems to be settling down having these in independent repos and releases makes more sense. Going forward the PAGI repo will only update if the spec itself changes; PAGI::Server and PAGI::Tools (where all the utilities and helpers now go) likewise. I think this will start to bring some stability to the ecosystem, especially now that PAGI::Server is functionally complete based on the goal chart I had for it initially. So I will only update it to fix bugs and security issues. PAGI::Tools will probably continue to see evolution over the summer as I start to nail down more common use cases and identify patterns worth encapsulating. 2) Specification clarifications and updates: The PAGI specification itself will move to v0.3 in the next release and it contains mostly clarifications and fixes. Biggest change will be a more detailed mechanism for controlling streaming output, especially around handling back pressure as well as new callbacks to notice when the output buffer is getting full and when it clears. Hopefully these changes will make it easier and more reliable to do streaming in PAGI. PAGI::Server has been updated to match, and the response helper in PAGI::Tools has some updates around that as well. Currently all this sits on Github: https://github.com/j

AI agents are getting very good at writing final reports. The problem is not only that they make mistakes. The problem is that sometimes they make mistakes with excellent presentation. Proofline is a 5-skill Markdown pack that catches fake-ready output before it turns into a release, handoff, public post, or "yeah, looks done". What Proofline does It is not trying to be another giant agent. It works as a review route after the agent produces a result: Reference Gap Ready Gate Reality QA Lean Pass Repair Report Compiler Each step asks an annoying but useful question: what is missing from the references, what was not checked, where did the agent pretend everything was fine, and what actually needs to be fixed? Who it is for Builders working with Codex-style agent chats, AI coding workflows, Markdown handoffs, and any process where "done" needs to mean more than a confident paragraph. Release: https://github.com/aisflows/proofline/releases/tag/v0.2.0-rc5

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 (

We are thrilled to announce the winners of the Hermes Agent Challenge! Over the past few weeks, the...

A few months ago I started building small tools as single HTML files - no npm, no React, no backend. Just one file that opens in a browser and works offline. I built 4 real products this way: DarkenAmber IT Tools - 17+ developer tools in 194KB ZeroOffice - PDF, image, AI tools in one file PrivacyKit - Photo privacy tools, no upload required ElectroKit - Electrical calculator + cost estimates for CIS market Every single one: one .html file. Works offline. Opens instantly. No server. The problem with AI coding assistants Every time I asked Claude or Copilot to build something simple, I got: A React project with src/ folder package.json with 12 dependencies webpack config TypeScript setup ...before writing a single line of actual logic. I kept manually correcting it. "No, one file. No npm. Vanilla JS." Then I realized - I should just teach it once and reuse that knowledge. What is a Claude Skill? A skill is a Markdown file with YAML frontmatter that changes how Claude thinks for a specific context. It is not a prompt. It is not a system message. It is a reusable set of rules that shapes how Claude reasons, what it prioritizes, and what it avoids. yaml--- name: single-file-app description: "Build complete web tools as a single HTML file - vanilla JS, inline CSS, localStorage, offline-first." tags: html vanilla-js offline version: 1.2 --- The two skills I built single-file-app Teaches Claude to build complete web tools in one HTML file. What changes: No React, no npm, no build tools unless truly justified Vanilla JS first, always localStorage for data persistence Dark/light theme with system preference detection Accessibility built in (labels, aria, keyboard nav) XSS prevention for user input Export/import for user data Anti-patterns it prevents: ❌ "Let me set up a React project" ❌ Creating src/ folder for a simple tool ❌ Suggesting npm install for a calculator ✅ "Here is your complete HTML file" ship-it Teaches Claude to bias toward shipping over planning for early-stag

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

Ky 2.0 is an open-source JavaScript HTTP client built on the Fetch API, featuring significant updates such as consolidated hook handling, enhanced timeout management, and improved URL processing. The release includes response validation through schema validation libraries and addresses migration from earlier versions. It aims to provide a lightweight alternative to axios. By Daniel Curtis

We are so excited to announce the winners of the Gemma 4 Challenge! This is officially our most...

VS Code 1.123 adds a two-hour delay before auto-updating extensions to newly published versions, creating a revocation window against supply chain attacks. The delay does not apply to trusted publishers like Microsoft, GitHub, and OpenAI. Similar cooldown mechanisms have now spread across pip, RubyGems, npm, pnpm, Yarn, and Bun. By Steef-Jan Wiggers

I joined DEV at the start of January, but it's only really been in the past week or so that things clicked into place — and looking back, it's been a lot more eventful than I expected for "week one." What I Set Out to Do My original plan was simple: write a structured series covering iOS development with Swift and SwiftUI, one topic at a time, with anime examples thrown in to keep things fun. Strings, arrays, loops, functions — the building blocks. What I didn't plan for was everything else that happened alongside it. The June Solstice Game Jam Happened I saw the announcement for DEV's June Solstice Game Jam and, on a whim, decided to build something for it. A few hours later I had a fully working SwiftUI trivia game — Pride Trivia & Alan Turing Edition — with ten questions covering LGBTQIA+ history and Alan Turing's legacy, a rainbow progress bar, and a results screen with score-based messages. I'd never built and shipped something end-to-end like that before, let alone submitted it to a community challenge. Going from "let's see if this works in the simulator" to "this is live on GitHub with a demo video and a published writeup" in one sitting was honestly a bit of a blur. Then I Detoured Into Google AI Studio A few days later, I worked through the DEV Education Track for Google AI Studio and built MascotCraft Studio — an app that generates coding mascots using Gemini and Imagen. One prompt later, I had a fully deployed web app and a mascot named Octo-Byte , a cheerful deep-sea developer with eight arms and a talent for multitasking. That post sparked one of my favorite discussions so far — a few comments turned into a genuinely interesting conversation about how AI is shifting the bottleneck from "can I build this" to "what should I build, and how do I know if it's good." Not at all what I expected from a post about a cartoon octopus. The Badges Somewhere in all of this, I picked up: A 1 Week Community Wellness Streak badge, just from commenting on other people's

A while back I was building a side project that needed public data from a few social platforms. Nothing crazy — profiles, posts, some engagement numbers. I figured I'd just grab each platform's official API. Reader, I did not "just grab each platform's official API." Here's what that road actually looked like, and how I ended up consolidating everything down to one key and roughly ten lines of shared code. The five-API nightmare Instagram (Meta Graph API). Great if you own the account. Useless for pulling public data about accounts you don't. Endless app review. TikTok. The research API is academics-only with a long application. For commercial use, basically nothing. X (Twitter). Used to be wonderful. Now $100/month to start, more for anything serious. YouTube. Honestly the best of the bunch — generous and well-documented. Credit where due. LinkedIn. Partner-only. For most people, no useful public access at all. So to cover five platforms I was looking at: five sets of credentials, five auth flows, five rate-limit models, five totally different response shapes, two flat-out rejections, and a monthly bill. For a side project. What I actually wanted getProfile ( " tiktok " , " someuser " ) getProfile ( " instagram " , " someuser " ) getProfile ( " twitter " , " someuser " ) Same call shape, same auth, same error handling. That's it. I don't care that each platform structures things differently internally — I want one boundary that hides that from me. The consolidation I switched to SociaVault , which puts public data from all of these behind one API and one key. My entire client became this: const API_KEY = process . env . SOCIAVAULT_API_KEY ; const BASE = " https://api.sociavault.com " ; async function sv ( path , params = {}) { const url = new URL ( BASE + path ); Object . entries ( params ). forEach (([ k , v ]) => url . searchParams . set ( k , v )); const res = await fetch ( url , { headers : { " X-API-Key " : API_KEY } }); if ( ! res . ok ) throw new Error ( ` $

Cybersecurity firm Chainguard has announced the launch of Athena, an industry coalition to use artificial intelligence to find and fix vulnerabilities in widely-used open-source software before attackers can exploit them. The coalition focuses on libraries, containers and other components that underpin web browsers, data centres, smartphones and payment systems. By Matt Saunders

This is a submission for the June Solstice Game Jam It's interesting how the most exciting ideas always arrive when I have basically no time to work on them. A few weeks earlier, I had finished my submission for the GitHub challenge by bringing an old WinForms game back to life. That project turned out to be a lot of fun. Then Sylwia Laskowska published a great article about Google's WebMCP . The idea fascinated me, but I wasn't sure where I could actually use it. Then the June Solstice Game Jam was announced. The idea hit me like a lightning bolt: What if I made a game that both humans and AI could play? Let's do it. What I Built I created a puzzle game with a solstice theme called Tower Before Dusk . The goal is simple: reach your home tower before sundown. Every action costs time. Every step brings sunset a little closer. Rivers block your path, rocks force detours and the only way across water is to collect enough wood and build bridges. Move too much, collect unnecessary resources, or choose the wrong path, and night will arrive before you make it home. The challenge isn't just solving the puzzle. It's solving it efficiently. And apparently, that's difficult for both humans and AI. Video Demo In this demo, Gemini 3.1 Flash-Lite tries to solve the level using the exposed game tools. It fails, then I restart the level and solve it manually. That failure is part of the point: the tools worked, but reasoning through the puzzle was still hard for the lightweight model. tower-before-dusk.gramli.workers.dev Code Gramli / tower-before-dusk A TypeScript puzzle game demonstrating WebMCP, where humans and AI solve the same challenges under the same rules. Tower Before Dusk Tower Before Dusk is a tile-based puzzle game about reaching the tower before sunset. Plan each route carefully: every move spends daylight, trees provide wood, and water can only be crossed by building bridges. The game is built as a modern browser app with TypeScript, HTML canvas, and Vite. It also ex

🗓️ This Week While organizing ideas for my first iOS app , I remembered an old web app idea called ToneDrill , which I had casually built before to help practice note names on a guitar fretboard🎸. I decided to try turning it into an iOS app 🛠️. I clarified the purpose of ToneDrill , its minimum requirements , and its core features , then organized them in Notion 📝. I was curious to see how well Codex could implement an iOS app from those minimum requirements , so I gave it a try right away💡. I reviewed the SwiftUI code generated by Codex and worked through the app logic to understand how it was implemented 🔍. For now, I was able to create a working app, which felt like a meaningful step forward 🚶. I created the top page UI design for my portfolio website in Figma 🎨. I focused on keeping the structure simple and implementation-friendly, and designed the UI with reusable components for each major part. Based on what I learned from my previous failed attempt, I tried again to see how well Codex could implement a prototype from the Figma UI design (You can read about my previous attempt that didn’t go so well here😅.) Worked on the AI Threat Modelling room from the AI Security Learning Path on TryHackMe this week🤖. 📱 iOS (SwiftUI) Revisited an old web app idea called ToneDrill, which I had previously built casually as a guitar note-training app, and considered turning it into an iOS app. Organized the app idea in Notion, including its purpose, target use case, minimum requirements, and core features. Decided to aim for an MVP-level version first, instead of trying to build a fully featured app from the beginning. Wrote down simple requirements and tested how accurately Codex could implement the initial version of the app. Reviewed the iOS app implementation generated by Codex and examined the code in detail to understand how the logic worked. 🌐 Web Development Posted my weekly dev log on Dev.to📝 Completed the top page UI design for my portfolio website in Figma. Tried us

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

𝗦𝗮𝘆 𝗵𝗲𝗹𝗹𝗼 𝘁𝗼 𝗟𝘂𝗺𝗼𝗿𝗮 — 𝗧𝗵𝗲 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗕𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽 𝟱 𝗔𝗱𝗺𝗶𝗻 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗨𝗜 𝗞𝗶𝘁. 💎 🔗 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼: https://github.com/Chetankumar-Akarte/lumora 🔗 Demo: https://renukatechnologies.in/demo/lumora/ Don't forgot to 🤩 Star and 👉 Fork the Repo 𝗟𝘂𝗺𝗼𝗿𝗮 is a modern, responsive 𝗕𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽 𝟱 𝗔𝗱𝗺𝗶𝗻 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗨𝗜 𝗞𝗶𝘁 designed for teams that need a polished, enterprise-ready control center without the bloat. Whether you are building for SaaS, CRM, E-commerce, or internal analytics, Lumora provides a scalable, token-driven foundation to speed up your workflow. 𝗟𝘂𝗺𝗼𝗿𝗮 is the result: a complete admin ecosystem featuring everything from KPI blocks and ApexCharts to full E-commerce management flows and authentication screens. 𝗪𝗵𝗮𝘁’𝘀 𝗶𝗻𝘀𝗶𝗱𝗲? • Full UI Kit with basic and advanced components. • Enterprise pages (Users, Roles, Permissions, Invoices). • Interactive apps like Calendar and Contacts. • Clean, token-driven styling for consistent design. 𝗧𝗲𝗰𝗵 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: • Bootstrap 5.3 • ApexCharts & Chart.js • Vanilla JavaScript • Mobile-first design 𝗞𝗲𝘆 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: • 𝗠𝗼𝗱𝗲𝗿𝗻 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸: Built with Bootstrap 5.3, Vanilla JS, and CSS3 using a module-first architecture. • 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀: Includes layouts for Analytics, CRM, Project Management, HRM, and more. • 𝗙𝗲𝗮𝘁𝘂𝗿𝗲-𝗣𝗮𝗰𝗸𝗲𝗱 𝗔𝗽𝗽𝘀: Ready-to-use interfaces for Advanced Chat, Kanban boards, Email, and File Management. • 𝗗𝗮𝗿𝗸 & 𝗟𝗶𝗴𝗵𝘁 𝗠𝗼𝗱𝗲𝘀: Clean, professional visuals with seamless theme switching. • 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆: Modular CSS, reusable partials, and organized project structure. I built this to bridge the gap between "pretty" templates and "functional" enterprise tools. Check it out, star the repo, and let me know what you think! I'd love for you to take a look at the code and perhaps even use it for your next project. Feedback and contributions are always welcome! WebDevelopment, Bootstrap5, AdminDashboard, OpenSource, UIUX, JavaScript, GitHub, Bootstrap, CodingCommunity, OpenSourceProject, FrontendDev, LumoraUI