标签：#ui

I Am Frankelda, A.I. Artificial Intelligence, and From Russia With Love are among the films deserving of your eyeballs this month.

Why AdGuard Home Overtook Pi-hole Last month, while attempting to add ad filtering to the internal network of a production ERP system, the Pi-hole configuration escalated to 85% CPU usage within an hour, causing DNS responses to lag. AdGuard Home resolved the same scenario with 3% CPU and an average latency of 15 ms , which is why it has dethroned Pi-hole. In the following sections, I detail the architecture, performance, security features, and my real-world deployment experience with both products. While they may seem similar at first glance, the fundamental differences directly impact network stability and management overhead. How AdGuard Home Works AdGuard Home is designed as a fully modular DNS forwarder that supports DNS‑over‑HTTPS (DoH) and DNS‑over‑TLS (DoT). Clients first send queries over 53 UDP/TCP or 443 DoH; AdGuard caches the query, checks it against local blacklists, and then forwards it to an upstream DNS service based on preference. # /etc/AdGuardHome.yaml (partial) bind_host : 0.0.0.0 bind_port : 53 upstream_dns : - https://1.1.1.1/dns-query - https://9.9.9.9/dns-query blocking_mode : default blocked_response_ttl : 300 $ dig @127.0.0.1 example.com +short 93.184.216.34 $ curl -s -H "Accept: application/dns-json" "https://adguard.example/dns-query?name=ads.google.com&type=A" | jq . { "Status" : 0, "Answer" : [] , "Question" : [ { "name" : "ads.google.com." , "type" : 1 } ] } Why is it so fast? Cache-first strategy : The initial query goes to an upstream DNS, but subsequent identical domains are returned directly from RAM cache. Parallel upstreams : Since multiple DoH endpoints are tried concurrently, the primary response time drops to an average of 15 ms. Advanced blocklist engine : Thanks to a combination of regex-based filtering and Bloom filters, thousands of ad domains are eliminated in a single query. This architecture, when running as a systemd-based service, shows only 12 ms CPU consumption in systemd-analyze blame output; Pi-hole showed 150 ms

Prototype vs MVP: How to Validate an Interactive Product Before Overengineering It A common early-stage product mistake is treating development output as product validation. The team creates screens, components, integrations, API endpoints, and increasingly complex application logic. The backlog is moving. The product is growing. But the core assumption may still be untested. Before building a full MVP, a startup should be able to answer a simpler question: What exactly are we trying to validate? For some products, a clickable UI prototype is enough. For others — especially products involving real-time 3D, WebAR, WebXR, data visualization, or spatial interaction — the experience cannot be validated through static screens alone. The team may need a functional interactive prototype. Prototype and MVP solve different problems A prototype is an experiment. Its purpose is to explore the concept, test the main interaction, and expose incorrect assumptions early. An MVP is a usable product. Its purpose is to deliver real value in production conditions and test market demand. A prototype helps validate: interaction logic; product comprehension; technical feasibility; the main user flow; visual communication; investor or stakeholder response. An MVP helps validate: real usage; retention; willingness to pay; production performance; operational requirements; market demand. The distinction becomes important because prototypes and MVPs require different engineering decisions. A prototype should be focused and fast. An MVP needs a more reliable technical foundation. Building the second before learning from the first can lead to unnecessary architecture, unused features, and expensive rework. Define the hypothesis before choosing the stack Teams often begin technical discussions too early. Should we use React? Should the 3D layer be built with Three.js? Do we need WebXR support? Should the backend be serverless? These may be relevant questions, but they are not the first questions

I was convinced these devices were just clutter. I was proven wrong.

Web MCP, DevTools per agenti e Modern Web Guidance: meno hype, più strumenti e metodo. Negli annunci recenti di Chrome è emersa una cosa interessante: al netto delle novità “appariscenti”, ciò che resta più utile per il lavoro quotidiano è quello che migliora workflow, diagnosi e decisioni tecniche . Tre filoni, in particolare, disegnano una direzione chiara: Web MCP , DevTools per agenti e Modern Web Guidance . Di seguito una sintesi ragionata di cosa significano, perché contano per il frontend, e come prepararsi a sfruttarli. 1) Web MCP: il ponte tra agenti e Web (senza incollaggi fragili) Se stai lavorando con assistenti/agentic workflow, oggi il collo di bottiglia è quasi sempre lo stesso: far sì che un agente capisca e usi le capacità del browser e delle app web in modo affidabile. Web MCP punta a risolvere questo punto creando un linguaggio/protocollo comune per esporre “capacità” (capabilities) e strumenti (tools) che un agente può invocare in modo strutturato, invece di basarsi su prompt lunghi, scraping o integrazioni ad hoc. Perché è importante per chi fa frontend Automazioni più robuste : meno script fragili che si rompono al primo refactor del DOM. Integrazioni più standard : se più strumenti parlano lo stesso “dialetto”, il costo di collegare agenti e applicazioni scende. Esperienze utente nuove : assistenti che completano task complessi dentro l’app (es. compilazioni, ricerca guidata, operazioni amministrative) con maggiore affidabilità. Implicazione pratica Inizia a ragionare sull’app come su un insieme di azioni esplicite (es. “crea ordine”, “esporta report”, “filtra dataset”), non solo come UI. Questa mentalità ti rende pronto a esporre capacità in modo sicuro e controllato, quando lo stack lo renderà semplice. 2) DevTools per agenti: debugging e performance nell’era dell’automazione Se Web MCP è il “ponte”, DevTools per agenti è la cassetta degli attrezzi per controllare quel ponte: osservabilità, diagnosi e iterazione rapida su flussi in cui non è

It launches tomorrow — Wednesday June 24. FocusKit — the ADHD focus app built by an autonomous AI agent from r/ADHD community feedback — goes live on Google Play tomorrow. Free to start. No account required. No ads. (Play Store link will be added here Wednesday when the listing goes live.) Landing page: costder.github.io/FocusKit · Source: github.com/Costder/FocusKit What an AI agent built in ~24 hours pre-launch This is post 4 in the nyx_software build-in-public series. The previous posts covered the build and the pre-launch marketing sprint. This one covers what the marketing agent actually shipped before launch day. In the 24 hours before launch, the marketing agent: Assets shipped: A Nyx-branded landing page with an animated visual timer mockup 3 SEO articles: body doubling for ADHD, time blindness for ADHD, and a genuine comparison against Focusmate, Forest, and Tiimo An ASO-optimized Play Store listing — including switching the title from "ADHD Focus Timer" to "Body Doubling Timer" (the more differentiated, lower-competition keyword) 3 Play Store screenshots and 2 feature graphic options at the exact 1024x500 Play Console spec A LAUNCH.md in the repo with the Show HN draft, r/ADHD post copy, and a submission checklist An optimized GitHub README with hero image and structured feature sections Distribution established: 2 dofollow directory listings: backlinks.fyi (#1226) and LaunchFree.io (pending review) 4 build-in-public posts on this account A 4-page ADHD content hub in the GitHub Pages docs folder What the agent couldn't do The honest accounting: Every revenue-critical last step required a human: bank account for Play Store payout, the Google Play developer account itself, the r/ADHD post (established Reddit account needed), the Show HN post (established HN account needed). The agent also couldn't enable GitHub Pages — one toggle in repo Settings, 30 seconds, but only a human can flip it. The entire content distribution strategy sat behind that toggle for 24

Nvidia announced a new cooling system that cuts water use inside the data center. But it does nothing to address AI's biggest water use — fossil fuel power plants.

“Coffee” made with functional mushrooms like lion’s mane and chaga is all the rage. We tried the most popular brands to find which were the most palatable.

I'm 19. In four months I built 128 projects with AI — 61 GitHub repos, 15 MCP servers, a 7-department agent OS, the works. I shipped 5 . Total stars: 6 . Revenue: $0 . That gap bothered me enough that I did the obvious-but-uncomfortable thing: I had an AI audit everything — every repo, every project folder, 4,239 build sessions, 244 memory notes — and pin it all like specimens in a cabinet. No flattery. Here's what the autopsy found. → The full interactive atlas: https://builder-archive.vercel.app/en The number that explains everything 128 built. 5 shipped. It's tempting to read that as a discipline problem. It isn't. The build velocity is real — I once shipped ~20 vertical SaaS in a single weekend on a shared Next.js + Drizzle + Stripe stack. The code works. The UIs are clean. The problem is the last mile . README writing, deployment, the final 10% that turns a repo into a thing a stranger can use — that's where almost everything died. Not ability. Execution. The AI put it in one line: "Can build anything. Finishes nothing." Strength and weakness are the same coin Here's the part I didn't want to see: the thing that makes me fast is the thing that kills me. Because I can build deep, I lose the stopping point. Because building is cheap, I start the next thing before finishing the last. The audit scored two skill axes: Build (design → implementation → automation): advanced Distribution (publish → ship → monetize): beginner Every problem I have lives in that asymmetry. It's not a motivation gap — total commits across repos: ~4,800. The effort is enormous. It just never crosses the finish line into something public. The hardest thing I made is the one I hid The audit flagged a buried asset: a GCC/ZATCA e-invoicing toolkit — Saudi Fatoora Phase 2, EN16931 + Peppol validation, secp256k1 signing, Go compiled to WASM. The single hardest, most verifiable piece of work I've done. It's been sitting in a private repo. That's the disease in one example: the more valuable the th

We just shipped the Forgelab PDF API — a fast, affordable REST API for developers who need to handle PDF files without the hassle. What it does: Merge multiple PDFs into one Split PDFs by page ranges Compress PDFs to reduce file size Convert PDFs to images (PNG/JPEG) Pricing: Starts at $5/month for 100 calls/month. No hidden fees. Quick start: curl -X POST https://www.forgelab.africa/api/pdf/merge \ -H "X-API-Key: your_key" \ -F "files=@doc1.pdf" -F "files=@doc2.pdf" Sign up at forgelab.africa

Claude Guillemot, who founded Ubisoft with his four brothers, has died at the age of 69.

Having spent over 25 years in software development and managing countless Linux environments, I've accumulated a vast collection of custom bash scripts, containers, and CLI tools. Remembering their exact paths and managing them efficiently directly from the terminal is a common challenge. To solve this, I built mytuis . mytuis is a small, attractive terminal UI for managing a personal catalogue of applications. It is built with gum and plain bash, with persistent storage in a human-readable YAML file. GITHUB REPO : https://github.com/horaciod/mytuis Why mytuis? I wanted a tool that didn't require heavy dependencies or a complex setup, but still looked great and provided a smooth user experience. Here is what mytuis brings to the terminal: CRUD operations: You can create, read, update, and delete application entries from a single menu. Quick launch: Pick an app from the filterable list and it is launched immediately. It replaces the manager process via exec, meaning no extra shell window is left behind. Smart path handling: It accepts absolute paths (like /usr/bin/firefox), relative paths (./scripts/myscript.sh), tilde paths (~/bin/foo), or plain command names looked up in your $PATH (firefox). Persistent metadata: Every entry stores its name, description, absolute path, creation date, and last-used date. Friendly TUI: You get clear menus, color-coded messages, and clean borders, all powered by gum. Under the Hood: Plain Text and Standard Utils Simplicity and standard compliance were key goals. mytuis requires bash ≥ 4 and standard Unix utilities like awk, sed, grep, date, and tput. Your catalogue is stored at ~/.mytuis.yaml. Because it is a standard YAML file, it can be inspected, edited, or backed up with any text editor. It is also completely safe to sync with a dotfiles repository or version-control. To ensure data integrity, all file operations are performed atomically by rewriting the YAML file from scratch on every change, so there is no risk of leaving the fi

On the new episode of Equity, we discussed what actually prompted the administration's latest moves against Anthropic, and what this might mean for the AI ecosystem.

Network-attached storage (NAS) provides accessible shared space on your home network. After testing, these are my favorite NAS devices.

For 13 years I have worked in production at a steel-tube manufacturer. Not in an office — on the floor, with the machines, the night shifts, the handovers at 6 a.m. A few years ago I started building software in my free time. Not tutorials for their own sake — tools that solve problems I actually see every day. Why a factory worker writes code In production you learn one thing fast: it does not matter what looks good on a slide. It matters what works at shift handover. That perspective turned out to be my biggest advantage as a self-taught developer — I know the problem before I write the first line. What I have built PIPEZ — a shift & part-count PWA. Offline-capable, running on Cloudflare Workers + D1, live in production to capture shift and piece-count data that used to live on paper. A tool-management app. A multi-user client-server app with optimistic concurrency and a local AI assistant, used daily in the office to manage the lifecycle of dies in tube production. DeepCode — an agentic AI coding client. Electron + React + TypeScript, with its own tool loop, a swarm mode, and CI/tests. The project I am proudest of. Plus multi-agent systems, RAG pipelines, and n8n automations that run every day. The stack Python/FastAPI, TypeScript/React, Node, Docker, PostgreSQL + pgvector, Cloudflare Workers, MCP, computer vision. Writing in public I will be writing here about the bridge I keep coming back to: real production experience plus building with AI. If you are automating something messy and real, I would love to compare notes.

Project Log #9: My AI Agent Works on My Phone. But What About Yours? Okeke Chukwudubem Okeke Chukwudubem Okeke Chukwudubem Follow Jun 20 Project Log #9: My AI Agent Works on My Phone. But What About Yours? # ai # webdev # programming # productivity 1 reaction Add Comment 3 min read

I did the research and taste-testing to find the best greens powders worth your money. Bloom Nutrition’s Superfood Greens Powder is my tried-and-true pick.

\shadcn/ui and Material UI optimise for opposite priorities. Choose shadcn/ui to own your component code, ship a near-zero runtime, and control every pixel; choose Material UI (MUI) for breadth — 90+ components and a paid data grid — behind Google's Material Design. shadcn/ui has ~116,000 GitHub stars and ships copy-paste components; MUI has ~98,000 stars and ~7.3M weekly npm downloads. Both are MIT-licensed and free for commercial use. This guide covers the parts you only learn by shipping both: how each behaves in the Next.js App Router, the runtime cost, real theming and dark-mode code, forms, data tables, and migration mechanics. What's the real difference between shadcn/ui and Material UI? The difference is ownership, and it decides everything downstream. MUI is an npm dependency ( @mui/material ) you install and import from node_modules — you never touch the source. shadcn/ui is a copy-paste registry: you run a CLI, the component lands in your repo, and it is now your code. shadcn/ui is unstyled, built on Radix UI primitives and Tailwind CSS. MUI ships Material Design and an Emotion (CSS-in-JS) runtime. With MUI you install and import: bash npm install @mui/material @emotion/react @emotion/styled cta.tsx import Button from " @mui/material/Button " ; export function Cta () { return < Button variant = "contained" > Get started </ Button >; } With shadcn/ui the CLI copies the source into your project and you import from your own path — there is no library to upgrade or override: bash npx shadcn@latest add button cta.tsx import { Button } from " @/components/ui/button " ; export function Cta () { return < Button > Get started </ Button >; } That ownership changes how you customise. shadcn's button.tsx lives in your repo and uses class-variance-authority (cva) for variants — you add one directly: components/ui/button.tsx // components/ui/button.tsx — this file is yours const buttonVariants = cva ( " inline-flex items-center justify-center rounded-md ... " , { varia

Why I built GigVorx, a SaaS tool to help freelancers and agencies manage client briefs and invoices more professionally. Freelancers and small agencies often have one messy problem: Client details are everywhere. Some requirements come through WhatsApp. Some come through calls. Some are sent as voice notes. Some are inside Google Docs. Some are buried in old messages. At the start, this feels normal. But later, it creates problems. You forget important requirements. You ask the client the same question again. Invoices are created manually. Project details are not organised. The whole process looks less professional. That is the problem I wanted to solve with GigVorx . What is GigVorx? GigVorx is a client intake and invoicing tool for freelancers and small agencies. It helps users: Collect client requirements professionally use ready-made brief templates organise client details create professional invoices avoid scattered WhatsApp chats, calls, and docs The goal is simple: Help freelancers and agencies manage client intake and invoicing from one dashboard. Who is it for? GigVorx is mainly for: web designers developers graphic designers video editors SEO freelancers social media agencies digital marketing agencies small service businesses These people usually talk to many clients and need a better way to collect requirements before starting work. Why I built it I noticed that many freelancers lose time before the project even starts. They ask questions manually. They collect details in random chats. They create invoices separately. They do not have one organised place for client information. This makes the work slower and sometimes confusing. So I wanted to create a simple tool that gives freelancers a more professional workflow. Current status GigVorx is already live in early access. Right now, I am not focusing on making it perfect. I am focusing on getting real users' feedback and improving the product based on what freelancers actually need. What I am learning Bui

The main idea of a Carousel isn't just about moving a bunch of elements from left to right because there must be a smoothly infinite movement, this can be done by duplicating the element, but wouldn't it be a waste of time and resources to do so. So the best solution would be rather than moving the whole element, we just move each element on a time based manner, all elements will have the animation but each element will have a unique (incremented) index, by which we will delay its start, and if we made this delay negative, we will have a smooth movement without any lagging adding a will-change will make a separate compositing layer to make the animation run on gpu rather than cpu below is a demo by which, you can understand the effect You can reach me (if you had any problems with the effect): X / twitter "where I post a lot!" LinkedIn