标签：#product

Notify your AI agent when the web changes Discussion | Link

Safety-first walking navigation to walk the safest routes Discussion | Link

Browser music and piano studio with visual harmony tools Discussion | Link

Give customers AI analytics inside your product. Discussion | Link

TRIESTE, Italy – For developers, system administrators, and digital hoarders alike, the daily struggle of locating a specific snippet of text buried deep inside hundreds of nested project files is a universal headache. While heavy-handed IDEs and clunky terminal commands exist, they often feel like using a sledgehammer to crack a nut. Enter FiXiY, a lightweight, blazing-fast utility designed to do exactly one thing flawlessly: scan a folder and find precisely what you’re looking for inside the files. Created by software engineer Lorenzo Battilocchi (known online as XeroHero), FiXiY has officially launched as a free, open-source project on GitHub. Simplicity Meets Speed Unlike built-in operating system searches that are notorious for missing code snippets or taking ages to index, FiXiY bypasses the bloat. It provides a localized, no-nonsense approach to file-content searching. Users simply point the tool to a folder, type in the phrase, string, or code block they need, and FiXiY maps out every instance across all supported file types within seconds. "As developers and creators, we waste an incredible amount of cumulative time just navigating our own file structures looking for a variable, a configuration line, or a specific piece of text," says creator Lorenzo Battilocchi. "FiXiY was built out of necessity. It’s a nimble, friction-free alternative for anyone who wants instant answers without waiting for a massive IDE to load or fighting with complex regex syntax in a terminal." Key Features of FiXiY: Deep Folder Scanning: Recursively searches through complex directory trees and nested folders seamlessly. Intelligent Text Matching: Pinpoints exact strings of text, code, or data buried within plain text, source code, scripts, and logs. Lightweight Footprint: Operates with zero background bloat, making it perfect for rapid-fire asset hunting on any machine. 100% Open Source: Built transparently for the community, ensuring full privacy with no data leaving your local mac

Hey DEV community! 👋 A few days ago, I built a VS Code extension called Marksmith to fix the most annoying parts of writing Markdown (like pasting Excel tables and syncing preview scrolls). But recently, I noticed a huge shift in my own workflow: Half the Markdown I write isn't for humans anymore. It’s being fed directly into Claude, ChatGPT, or Gemini as prompts and context. When you're constantly stuffing docs into context windows, two things happen: You worry about hitting context limits (or racking up API costs). You waste time dealing with AI "hallucinations" when you ask it to generate docs back for you. So, for the v0.3.0 release , I decided to pivot Marksmith into something new: An Agent AI-Ready Markdown Toolkit. 🚀 Here is what I added to survive the AI era: 📊 1. Real-time LLM Token Estimator Instead of just counting words, Marksmith’s Document X-Ray sidebar now includes a Heuristic Token Estimator for GPT, Claude, and Gemini. Before you copy-paste that massive README into your AI assistant, you can see exactly how "heavy" it is in terms of tokens right inside your editor. No more guessing if you're about to blow past your context limit! ✂️ 2. Copy Optimized for AI (1-Click Minify) Formatting is great for humans, but LLMs don't need all those extra spaces, perfectly aligned markdown tables, or empty lines. I added a CodeLens button at the top of your files. Click it, and Marksmith instantly minifies your Markdown (compresses tables, strips blanks) and copies it to your clipboard. Result: You save significant tokens and API costs without ruining your beautiful local .md file. 🕵️ 3. Hallucination Quick Fix Ever ask an AI to write documentation, and it leaves behind a bunch of [TODO: Insert link here] or makes up a fake local image path? Marksmith now automatically scans your document and puts a red squiggly line under AI placeholders and broken local links . Click the 💡 icon, and you can instantly strip them out or fix them. It acts as a safety net before you

Every Linux PHP developer knows the dance. You need to switch from PHP 8.1 to 8.3. You run your sudo commands, update your global symlinks, and then realize your local development server in the other window just crashed because it was running on the old version. Why should managing PHP versions be a system-wide struggle? The Solution: Per-Shell Version Isolation phpvm brings the seamless developer experience of tools like pyenv , rbenv , or nvm to the PHP ecosystem on Linux. Instead of changing /usr/bin/php globally, it uses a lightweight shim directory prepended to your PATH . When you call php , the shim inspects your environment variables and forwards the execution to the correct binary. It supports three layers of resolution, falling back gracefully: Shell pin : Pinned manually via phpvm shell <version> Project default : Resolved from .php-version or composer.json requirements when you cd into a directory Global default : The system fallback managed by update-alternatives Effortless Provisioning No need to look up repository installation guides. The built-in installer automatically detects your distribution (Ubuntu or Debian) and configures the appropriate upstream repositories (Ond?ej Sur�'s PPA or deb.sury.org ) to fetch the exact CLI and FPM packages you need. Polish in v2.5.1 Our latest release focuses on making the environment rock-solid: Tray App Auto-Start : Spawns the GTK desktop tray app immediately after installation by resolving the graphical session environment from active processes. PATH Priority : Actively prevents IDEs, login shells, or snap profiles from overriding the shim's position in PATH . Clean Cleanup : Ensures all background processes are terminated during uninstallation. Getting Started You can install or upgrade using the interactive script: curl -fsSL https://raw.githubusercontent.com/rijverse/phpvm/main/install.sh | sudo bash If you are already running v2.5.0, simply run: phpvm --self-update Check out the project website, or find the

For a long time, I thought productivity was about effort. Work harder. Focus more. Stay disciplined. Manage time better. Most productivity advice is built around some version of this idea. Then I noticed something strange. Some days I could spend ten hours at a desk and accomplish almost nothing. Other days I could spend three hours working and make more progress than I had all week. The difference wasn't effort. The difference was context. The Most Expensive Thing Is Not Time Ask people what their most limited resource is and most will answer: Time. But for knowledge workers, engineers, researchers, writers, and designers, I think the scarcer resource is often something else. Mental state. The ability to hold a problem in your head. The ability to remember why a decision was made. The ability to see connections between ideas. The ability to continue a train of thought without interruption. That's the state where meaningful work happens. And it's surprisingly fragile. Every Context Switch Has a Cost Imagine you're debugging a difficult issue. You've already: read the logs inspected the code traced the requests formed a hypothesis You're finally starting to see the shape of the problem. Then: a Slack notification arrives someone schedules a meeting an email requires attention a different task becomes urgent The interruption itself might only take two minutes. The real cost is what disappears. The mental model. The momentum. The partially constructed map inside your head. The next time you return to the task, you don't continue where you left off. You rebuild. Software Often Creates The Problem It Tries To Solve One thing that surprised me after building products for years is how much software exists primarily because other software creates friction. A note-taking application exists because memory is limited. A task manager exists because priorities change. A research assistant exists because information is fragmented. Many tools are not solving fundamental problems.

A new way to review PRs, directly inside Linear Discussion | Link

OpenSparrow v2.6 is out. This one's a big step forward — RAG (Retrieval-Augmented Generation) integration, bulk grid operations, and a whole new UX layer. RAG & AI integration You can now upload documents and let users ask questions against them. The system retrieves relevant sections and generates answers using an LLM (supports Ollama locally or any OpenAI-compatible API). What's new: RAG Statistics tab in admin — tracks query tokens, response times, document matches, and recent queries Multilingual auto-response — user questions are answered in their own UI language, no schema changes needed 20-language support — "Ask AI" panel fully translated, plus language dropdown in the test interface Good for things like: knowledge base Q&A, customer support automation, or letting internal teams ask questions about their own data. Grid & bulk operations Mass Edit module — select rows via checkboxes, bulk edit fields, change owners, duplicate, or delete with one click Keyboard shortcuts — arrow keys to navigate, Tab, Ctrl+C to copy, Ctrl+F to search, Ctrl-hold for help modal — works across all 20 languages Quick Data Cleanup toolbar — find & replace with live preview, case sensitivity, accent-ignore. Editor-gated with audit trail. Admin improvements Renamed "RAG Knowledge Base" to "Centrum AI" (heading is translatable) Migration Manager — tracks pending cleanup tasks after version upgrades, with automatic backups and audit trail FK columns render as dropdowns in forms by default Security & Quality All bulk operations (mass edit, cleanup, delete) are editor-role gated CSRF protection on every operation Full audit trail — every change is logged 20 languages fully supported across all new modules Fixed regressions in RAG API and CSV import Following this series? OpenSparrow v2.3 – visual admin panel, zero dependencies, now with ERD and M2M support OpenSparrow – open-source admin panel builder, zero dependencies, v2.1 just dropped Websites opensparrow.org github.com/wrobeltomasz/

Analytics is often treated as a reporting feature: a dashboard added after the product already exists. That is usually too late. For software operators, analytics is closer to infrastructure. It is the layer that makes the state of the product visible. Without it, a team cannot evaluate the situation, understand whether the product creates value, or know whether a workflow is improving. That is the reason WebmasterID is built around privacy-first analytics. The goal is not to collect more data than necessary. The goal is to preserve enough signal to make practical decisions without turning measurement into surveillance. Analytics answers operational questions Good analytics starts with plain questions. What happened? Which workflow changed? Which part of the product is used? Where do people leave? Which system events matter? What evidence supports the conclusion? Those questions sound simple, but they are the foundation of product judgment. If the data model cannot answer them, the team is forced to reason from anecdotes, support messages, and internal opinion. Those inputs still matter, but they are not enough on their own. Analytics gives operators a way to compare the current state with the previous state. It makes change visible. It also makes uncertainty visible when the evidence is incomplete. Product value needs evidence A product can look polished and still fail to create value. It can also look unfinished while solving a real operational problem. The difference is usually visible in behavior. Do users return? Do they complete the workflow? Do they avoid a manual step? Does the product reduce confusion? Does it make a business process easier to operate? Privacy-first analytics should help answer those questions without building a profile of every person. In many cases, first-party events, coarse context, workflow state, and careful retention rules are enough. The system does not need to know everything about a user to show whether a product path is working.

Every week another "AI agent for X" launches. Email triage. Calendar coordination. Sales follow-up. PR reviewer. Slack monitor. Meeting summarizer. I've installed enough of them to see the pattern. Here's the dirty secret nobody mentions in the launch posts: These tools don't reduce your work. They multiply your notifications. Each AI tool is configured to be helpful by default. "Helpful" means: "I noticed this thing — here's a notification." Stack a dozen of those, and instead of one inbox to ignore you have twelve. The signal-to-noise ratio gets worse every time you add an AI to your workflow. The mainstream answer is "just configure each one." Sure. Spend four hours tuning notification settings every time you add a tool, and another four hours when one of them ships a "smarter notifications" update. That's not productivity. That's notification janitorial work disguised as setup. This is a structural problem. Not a configuration problem. The wrong question Every AI tool asks the same thing: "Is this important?" Wrong question. There is no objective "important." Importance depends on you, right now. A Stripe webhook is important when you're debugging a checkout flow. The same webhook is pure noise during a deep work block. A Slack message from your cofounder is critical at 11am Tuesday and irrelevant at 11pm Friday. The right question is: Is this urgent enough to interrupt me, right now, given what I'm doing? That's not a question any individual AI agent can answer. It's a layer above all your AI agents. None of them have the context. None of them know what the others are doing. None of them know how you're spending the next hour. So they all default to "I'll just send you a notification, you decide." Which is exactly the experience you have right now: drowning. What an AI firewall actually looks like I'm building that layer. It's called Klorn . Here's how it works in practice. Every signal — email, calendar invite, agent action, webhook, push from another tool — g

Last Friday at 4:30 PM, my product manager walked over: "Hey, can you grab the titles from the Hacker News homepage and send me an Excel file?" I thought: That's it? Five minutes tops. Two hours later, I was still debugging CSS selectors. How Things Spiraled Out of Control Step 1: Initialize the Project mkdir hacker-news-scraper && cd hacker-news-scraper npm init -y npm install puppeteer Hit enter, waited three minutes. Puppeteer needs to download a full Chromium browser — over 200 MB. I stared at the progress bar and started questioning my life choices. Step 2: Write the Code "It's just a document.querySelectorAll , right?" That's what I thought. Then I opened my editor: const puppeteer = require ( ' puppeteer ' ); ( async () => { const browser = await puppeteer . launch ({ headless : true , args : [ ' --no-sandbox ' , ' --disable-setuid-sandbox ' ] }); const page = await browser . newPage (); try { await page . goto ( ' https://news.ycombinator.com ' , { waitUntil : ' networkidle2 ' , timeout : 30000 }); await page . waitForSelector ( ' .titleline > a ' , { timeout : 10000 }); const titles = await page . evaluate (() => { const items = document . querySelectorAll ( ' .titleline > a ' ); return Array . from ( items ). map ( el => ({ title : el . textContent , url : el . href })); }); console . log ( JSON . stringify ( titles , null , 2 )); } catch ( err ) { console . error ( ' Scraping failed: ' , err . message ); } finally { await browser . close (); } })(); I counted: 27 lines. And this is the minimal version — no User-Agent spoofing, no retry logic, no proxy support, no concurrency control. Add all of that and you're well past 50 lines. Step 3: Run It node index.js Error: Navigation timeout of 30000 ms exceeded . Switched to domcontentloaded , got past that. But then waitForSelector timed out — because .titleline was a relatively new class name. Hacker News had silently changed it from .storylink at some point, and nobody sent me the memo. Step 4: Debug Set head

I got tired of explaining to people why their "simple weather widget" needed an API key, a signup form, and a credit card. So I built a weather API that doesn't need any of that. Here's how you can add live weather to your personal website, portfolio, or side project in about 60 seconds. The old way (painful) Most weather APIs make you: Create an account Verify your email Generate an API key Paste that key into your code Worry about rate limits Get an email when your "free tier" expires For a weather widget on a personal site? That's overkill. The Nimbus way (one fetch, done) I made a public API that doesn't need keys. Just call it. fetch ( ' https://nimbus-api-gxuc.onrender.com/api/v1/weather?city=Berlin ' ) . then ( response => response . json ()) . then ( data => { console . log ( data ); }); That's it. No headers. No tokens. No signup. Real example: Put it on your site Here's a working snippet you can drop into any HTML page right now: <div id= "weather-widget" > <p> Loading weather... </p> </div> <script> fetch ( ' https://nimbus-api-gxuc.onrender.com/api/v1/weather?city=Berlin ' ) . then ( res => res . json ()) . then ( data => { const weatherHtml = data . temp_celsius + ' °C • ' + data . description + ' <br>Wind: ' + data . wind_speed_kmh + ' km/h • Humidity: ' + data . humidity_percent + ' % ' ; document . getElementById ( ' weather-widget ' ). innerHTML = ' <strong> ' + data . location + ' </strong><br> ' + weatherHtml ; }) . catch ( function () { document . getElementById ( ' weather-widget ' ). innerHTML = ' <p>Weather not available right now</p> ' ; }); </script> Change Berlin to any city. It works. Why did I build this? Because I got tired of closed source APIs that change their pricing every 6 months. Nimbus is open source. The code is on GitHub. You can read it, fork it, or run your own version if you don't trust me. The API is free because hosting a weather endpoint costs me almost nothing, and I'd rather lose money than put up a paywall. Try it righ

Track rising Hacker News posts before they explode Discussion | Link

Memory layer for LLMs that stores repo rules + past lessons Discussion | Link

The AI Marketing Agent Powered by Ahrefs Data Discussion | Link

LLM usage tracking that lives in your Mac's notch Discussion | Link

AI Video Cutter for viral Shorts, Reels, TikTok in Telegram Discussion | Link

Generate fast, reliable APIs for any platform. No browsers Discussion | Link