标签：#Product

Qwen’s most capable model for coding and cowork Discussion | Link

Building Three Privacy-First Mini Apps That Feel Like Standalone Products PureHub is an open-source collection of 22 free, ad-free mini apps. This release focuses on a simple product question: can a mini app inside a hub still feel dependable, focused, and complete? QR Studio The web scanner now supports a live camera and uploaded images through local decoding. Scan history stays in local storage, URL results receive basic safety checks, and supported cameras expose a torch control. Android uses CameraX and ML Kit with explicit scanner cleanup, duplicate-result protection, and copy, open, and share actions. Zen Pomodoro A one-second decrement loop drifts when a tab sleeps. The new timer stores a target time and recalculates the remaining duration, so switching tabs or waking a device no longer quietly extends a session. Weekly sessions and focused minutes remain on-device. Android uses a monotonic clock for the same reason. Zen Breath The breathing guide now includes Calm 4-6, Box 4-4-4-4, and Relax 4-7-8 patterns, controlled sessions, cycle totals, and accessible motion behavior. Nothing requires an account. Standalone safety for all 22 tools Each mini app now has a runtime contract describing its local storage namespace, offline behavior, and device capabilities. A per-tool error boundary prevents one failure from taking down the rest of PureHub. The three flagship tools also load as independent chunks and are available as PWA and Android launcher shortcuts. What happens next The Command Center will compare 14 days of anonymous aggregate opens, helpful votes, and shares. The strongest useful-use signal - not raw views - will choose the next deep-polish target. Try the release at PureHub or inspect the source on GitHub .

How one frustrated shopper turned a spreadsheet nightmare into a community-powered resource that's saving buyers hours of research. If you've ever shopped for a mini PC, you know the drill. Open a dozen retailer tabs. Copy specs into a spreadsheet. Squint at product titles that hide critical details. Try to figure out whether that $299 model has soldered RAM or upgradeable slots. Give up and buy something you're not quite sure about. This is exactly the frustration that led a developer to build Mini PC Price — a free, sortable comparison table that aggregates real marketplace listings with full specifications, all in one view. The Problem: Specs Buried in Fine Print The mini PC market has exploded in recent years. Brands like Beelink, Minisforum, Intel NUC, ASUS, Lenovo, and HP are flooding the market with devices ranging from $100 stick-class boxes to $1,000+ workstations. But the shopping experience hasn't kept pace. Most e-commerce listings show a product name, a price tag, and a few bullet points. Critical information — RAM type (soldered vs upgradeable), GPU class (integrated vs discrete), OS bundle (Windows pre-installed or barebone), storage interface (NVMe vs SATA) — is often buried in product descriptions or missing entirely. "The difference between an 8 GB listing and a 16 GB listing of the same model can be easily missed if the table doesn't make it obvious," the creator explains. "I wanted to build a tool that surfaces exactly what matters, side by side, so buyers don't have to guess." The Solution: One Table, Full Specs, Community-Maintained Mini PC Price is not another curated top-ten list or a simple deal feed. It's a full-parameter database where each row represents a real marketplace listing. Prices refresh daily, and specs are collected from official product pages. Critical attributes receive human verification where mismatches are common. The tool's default view sorts by price ascending — the fastest way to find mini PCs under $100 or $150. But th

First hour with Claude's browser extension: I pointed it at our LLC registration and watched it work through the forms, finding the right pages, filling the fields, moving on. I sat there holding a coffee, contributing nothing. I thought: I'm going to ship so many products. I shipped one. Here's what happened in between. Everything below was caught before launch. None of it was caught by being clever. It was caught by a process that got built slowly, mostly after being burned. What went wrong The idea wasn't the hard part. Once I went looking, I found several products with some of the same features. Nobody had the exact combination, but the idea was never the moat. Good implementation and distribution seem to be. You design the product while building it. Referral behaviour, what happens when a trial expires mid-session, how translations work across a page, none of it was in my head at the start. Each became a decision made under pressure, halfway through something else. Write as much of the workflow down as possible first. It says it did things it didn't do. Confidently. I deployed more than once to find the fix I'd been told about was never written. Treat every claim of completion as unverified. The rule that came out of it: make it prove the code is right before it theorises about what you did wrong. Bullet points, not paragraphs. Long replies made it hard to tell which of my five points got addressed. Numbering my instructions, and making it map answers back to the same numbers, turned "did you do item 3" into a question with an answer. It blames you first, and argues with facts. Two landing page changes; one appeared, one didn't. Its verdict: "you didn't deploy." I said one change was live, which is only possible if I had deployed. It repeated that I hadn't. It never asked which change I could see, and never reopened its own code, where the bug was. I swore at it. It stopped guessing, checked, and found the error. Many times, escalation seemed to be the only thi

Cloud canvas where your team and AI agents works together Discussion | Link

Local sandboxes for AI agents on your Mac, Linux, bare metal Discussion | Link

I remember the day I hit my breaking point. I had spent the entire morning — five hours — wrestling with a React component that an AI assistant had generated for me in about four minutes. The code looked flawless at first glance. Proper hooks, clean JSX, even decent comments. But it didn't work. And worse, I couldn't figure out why. Everyone talks about how AI speeds up coding. And it's true — when it works, it's magical. I've personally seen my feature delivery time drop by maybe 40-50% on good days. But what nobody talks about — what I certainly never saw in the breathless LinkedIn posts — is the debugging nightmare that follows when the AI gets it wrong. That day, I realised I had spent ten times longer debugging AI-written code than I would have spent writing it myself from scratch. I started tracking it. Over three months, I logged every AI-assisted task. The numbers were sobering: on average, each AI-generated snippet took me 3.2 times longer to verify and fix than to write myself. And for complex tasks — anything involving state management, async flows, or edge cases — the ratio jumped to 8-12x. The AI was giving me confidence, not correctness. And confidence, as any seasoned developer knows, is the enemy of debugging. The Hallucination That Cost Me a Sprint One incident stands out. I was building a data pipeline in Python that needed to batch-process JSON files from an S3 bucket and push transformed records into a PostgreSQL database. I gave the AI a detailed prompt: "Write a function that reads all JSON files from a given prefix, validates each record against a schema, and inserts them in batches of 500. Use threading for I/O." The AI returned a beautiful 60-line function. It used concurrent.futures.ThreadPoolExecutor , had proper error handling, even logged progress. I was impressed. I dropped it into the codebase, ran the tests — they passed. Deployed to staging. Worked like a charm. Then production hit. Three hours later, the database had 30,000 duplicat

I built a free ATS resume checker after seeing too many friends get ghosted on Easy Apply. Most people think ATS means “add more keywords.” In practice, a lot of resumes fail earlier — on parsing. The 5 blockers I see most Columns / two-column layouts Nice for humans. Bad for many parsers. Contact info or skills in a sidebar often get read in the wrong order (or skipped). Fix: one-column layout. Standard section titles: Experience, Education, Skills. Icons instead of text Phone / email / LinkedIn as icons only = empty fields for the ATS. Fix: real text next to icons (or drop the icons). Text in images / fancy graphics If you can’t Ctrl+A → copy readable text from your PDF, neither can the ATS. Fix: real text PDF/DOCX, not a designed image export. Tables for experience Tables often scramble dates and bullets. Fix: simple headings + bullet points. Keyword stuffing Repeating “Python Python Python” can look spammy and still miss role-specific terms from the job post. Fix: mirror the job’s real skills in your bullets with proof (tools + outcomes). Quick self-test Open your PDF Select all → paste into a blank doc If the order is messy or text is missing, fix the format before you apply again Free checker I use for this I shipped a free diagnosis that flags these blockers first (full score/keywords optional): https://myatscheck.com/free-ats-resume-checker If you’re job hunting through Workday / Greenhouse / Easy Apply, fix parsing before you rewrite every bullet.

This was first published on the Vibsync blog . Reposting for the DEV community. The short version: AI reliably makes each developer faster. Whether it makes the team faster is a separate question — and the gap between the two is where a lot of quiet cost hides. Below: the five coordination costs that eat the difference, a ten-question diagnostic, and five operating principles. Picture three developers, three AI coding agents, and one repository. Each developer can now produce candidate code, tests, and refactors faster than before. Yet releases move at the same pace, review queues grow, and the same facts keep getting rediscovered. That's not a paradox, and it isn't a reason to slow anyone down. It's a reminder that individual speed and team speed are different quantities , and AI coding agents scale the first far more easily than the second. Give everyone a faster typewriter and you get more pages — not necessarily a better book, written faster, by a group. Individual output is not team throughput It's worth separating two things we tend to blur: Individual output — how much finished work one developer (plus their agent) produces. Team throughput — how much shippable, coherent work the group produces together, after review, rework, waiting, and reconciling everyone's changes. AI agents lift individual output directly. Team throughput is what's left after the coordination overhead is paid, and that overhead doesn't shrink just because each person got faster. A useful way to hold it in your head — not as a formula to compute, just as a shape: team throughput ≈ the sum of local speed-ups − rework − waiting − reconciliation When you add agents, the first term grows. If nothing else changes, the last three grow too — because there's now more work in flight, produced faster, by people who can't all see what the others are doing. The interesting question for a team lead isn't "how do I make everyone faster?" It's "which of those subtraction terms is my real ceiling?" Ther

Your subconscious mind as a living 3D garden you water daily Discussion | Link

Make existing Codex and Claude Code sessions multiplayer Discussion | Link

Markdown & HTML editor, without the LaTeX pain Discussion | Link

The MCP server for marketing data Discussion | Link

A few months ago I got frustrated. I needed to compress a PDF quickly. Found a tool online — it asked me to create an account first. Found another — it had so many ads the actual button was invisible. Found a third — it uploaded my file to their servers and I had no idea what happened to it after. I thought: this shouldn't be this hard. So I built EazyStudio — a suite of 80+ browser-based tools where everything runs 100% in your browser, no signup, no intrusive ads, no files ever leaving your device. What's inside Here's a snapshot of what's available: PDF tools Compress, merge, split, rotate PDFs PDF to Word, Excel, JPG and back Add watermarks, protect with passwords Image tools Background remover (runs locally in browser) Image compressor, resizer, converter AI image upscaler Color palette extractor, color picker Developer utilities JSON formatter/validator Base64 encode/decode URL encoder, HTML entity converter Regex tester JWT decoder API tester (Postman-lite) CSS gradient generator, box shadow generator Finance & math EMI calculator, SIP calculator GST calculator, compound interest, tip splitter Unit converters (length, weight, temperature, data) And more QR code generator Password generator Text tools (word counter, case converter, lorem ipsum) Device preview tool The technical approach: browser-first The biggest design decision was: nothing gets uploaded to a server. For PDF operations I use PDF.js and pdf-lib running in the browser. For image tools it's canvas + WebAssembly (WASM) modules. For background removal I'm using a WASM-based segmentation model that loads client-side. This has three benefits: Speed — no upload round-trip, works on large files instantly Privacy — your files never touch my server Cost — zero storage, zero egress bandwidth The downside: WASM modules add initial load time. I worked around this with lazy-loading — the WASM only loads when you first use that specific tool. What I learned building this 1. People hate signups more than I ex

Wild Pokémon appear while you wait for Claude Code Discussion | Link

Study 25,000+ screens from top-earning iOS apps Discussion | Link

Hello Everyone, I'm the creator of PyBotchi, an intent-based AI Agent Orchestrator. In this post, I will discuss some key concepts why I created it. A little bit of background first. I'm a solutions architect with 10 years of experience as a software engineer. Most of my work are high throughput, high reliability, low cost and low latency services. This is while making it simple and readable to improve it's maintainabality. When I'm designing a system, I usually prioritize these concerns. You may assume this is my bias in relates to AI Agent building. I'm also Claude Certified Architect (Foundation) and I found that PyBotchi aligns almost identical to Anthropic's core agent recommendations. TL;DR: PyBotchi is an lightweight, async-first Python framework that uses nested Pydantic models and OOP inheritance to turn LLM intent detection into clean, deterministic business logic without the overhead of complex graph orchestration. Why I created PyBotchi? I really believed that traditional coding can already solved what client's need. The only limitations we have is how we read the input and how we show the output. In most cases in web services, your API use JSON, XML, etc with their respective specification/structure. Input Analogy Assume you have created a Books CRUD endpoints (FastAPI with Pydantic). Your create endpoint will have a define specifications for book creation to have a validation and avoid user errors. Most of the time you will also validates sessions and permissions which also included in the request. If you want your chat bot to support those, you just need add those endpoint as intent (tools). If your model tool selection are able to detect intents. You are more "close" to being deterministic. "Your services will have 50 endpoints or more. You will flood your tool selection call" In your frontend UI, you segregate panels/forms/inputs in their respective pages. You don't usually join multiple intent in a same page. Cluttered UI will make your UX confusin

Clone any Mac app into a fully isolated instance Discussion | Link

If you subscribe to more newsletters than you read, which tool fixes it depends entirely on which problem you actually have. Most roundups skip that step and just rank apps. Disclosure up front: we make one of the eight tools below. It's the last entry, it's new, and it has no track record — its section says so plainly. The other seven are real options and for most people one of them is the better pick. Every price and behaviour here was checked against the vendor's own site on 2 August 2026 . Where a vendor doesn't publish a price, this says that instead of guessing. The two problems people both call "too many newsletters" They aren't the same problem, and the tools split cleanly along the seam. Clutter. Newsletters are burying your real email. You'd read them, you just don't want them sitting next to your bank and your on-call alerts. The fix is routing: move them somewhere else. Volume. Twenty-five arrive a week and you have time for three. Moving them changes nothing — now you have twenty-five unread items in a nicer app. The fix is either condensing the pile or deciding what's in it. Almost every tool below solves exactly one of these. Buying a clutter tool for a volume problem is the standard way to end up paying a subscription and still having the same unread count. The plumbing, since you're the one wiring it up Four mechanics show up across all eight: Dedicated intake addresses. Readwise Reader, Meco, Readless and Digest each hand you an address on their domain (Meco's look like you@mecoinbox.com ). You subscribe with it and their infrastructure receives the mail — the cleanest integration point available: no OAuth scope on your mailbox, no IMAP polling, no shared credentials. Mailbox connection. Meco will alternatively connect Gmail or Outlook and pull your existing subscriptions across, setting the selected ones to skip your inbox (reversible at any time, per Meco's FAQ). Much faster than re-subscribing to 25 newsletters by hand. The cost is a read scope

I hit Ctrl-F more than any other key combination on this machine. It runs a shell function called fts — "find tmux session," which is not a good name but it's four years too late to change it. I press it, a fuzzy finder opens listing every project directory I have, I type a few characters, and I'm sitting in a tmux session for that project with the panes already laid out. If the session already existed, I'm back in it exactly where I left off. If what I typed doesn't exist yet, it offers to create it. Somebody watched me do this over a screen share recently and asked what was going on. So: here's the whole thing, the four tools it's built on, and a breakdown of every part that isn't obvious. What you'll end up with: One keystroke from anywhere to any project Fuzzy search across every repo you own, with a live directory tree preview Type a name that doesn't exist → it offers to scaffold and place it Never accidentally start a second tmux session for a project you already have open The same window/pane layout in every project, every time The problem it solves Before this, starting work looked like: cd ~/repo/work/some-project-i-half-remember-the-name-of tmux new-session -s some-project # split some panes, badly, slightly differently each time Three or four commands, one of which needed me to remember a path. None of it hard. All of it friction at exactly the wrong moment — the moment you've decided to start something, which is the moment you're most likely to get distracted instead. I'd also collected tmux sessions named 0 , 1 , 2 and some-project-2 , because I kept starting new ones instead of attaching to the one already running. So the goal wasn't really speed. It was making the right thing the automatic thing. Prerequisites Four tools plus zsh. All four are worth having on their own, and three of them are things you'll reach for daily once installed. Tool Version I'm on What it does here tmux 3.6a The terminal multiplexer. Holds the sessions, windows and panes. fz