标签：#Product

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

From smart apps and planters to unique tools, these gifts will turn even a black thumb into a next-level plant parent.

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Script-first subtitle timing for voiceover videos Discussion | Link

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For months, the most interesting model at Anthropic was one we could not use. Mythos was the internal system the company said was too capable to release, the one that found software vulnerabilities at a level that tripped its own safety thresholds. On June 9, 2026, that tier went public for the first time, as Claude Fable 5. Opus 4.8, the model anchoring production coding agents, suddenly had a successor that's a full capability class above it. This raises two questions for anyone running coding agents. The practical one is whether you should move your fleet from Opus 4.8 to Fable 5. The bigger one is whether a Mythos-class model, the tier Anthropic held back as too capable to ship, lives up to what the name promised. This article answers both, and the numbers tell a more interesting story than the announcement did. We ran both models through the same evaluation, close to 1000 shared scenarios scored twice each, once with no skill supplied and once with the relevant skill in context. The short answer, as of mid-2026, is that Opus 4.8 is still the better value for most agent fleets, and the gap between the Mythos hype and the measured reality is the real story in the data. A Mythos-class model is a tier of Claude that sits above the Opus class in capability . It reaches a threshold Anthropic considers high-risk, particularly at discovering and exploiting software vulnerabilities. Fable 5 and Mythos 5 are the same underlying model with the same capabilities. What separates them is the safeguards: Fable 5 is the public version that ships with safety classifiers, while Mythos 5, restricted to approved partners, runs without them. What the industry expected from a Mythos-class model Before launch, the speculation was not subtle. Across Reddit, X, and a run of explainer posts, Mythos was framed as the model that would change how agents work, not just how well they answer. The recurring predictions clustered around four capabilities: Restructuring a large codebase in one c

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The hype cycle for AI adoption in businesses often follows a familiar, and often frustrating, trajectory. It begins with the undeniable allure of a powerful new tool—seeing ChatGPT effortlessly summarize documents or Copilot intelligently autocomplete code is undeniably impressive. The immediate reaction is almost universally: "We need to get AI." So, tools are procured, workshops are run, and initial enthusiasm soars. Yet, more often than not, six months down the line, that excitement has waned, and the needle on actual business transformation hasn't moved. The fundamental operational rhythm of the organization remains unchanged. As a Senior IT Consultant and Digital Solutions Architect with over a decade of experience, I've observed this pattern repeatedly across various client engagements. This isn't a failure of the technology itself, but rather a profound strategy failure . It’s the single most common and costly mistake I encounter in AI adoption today. The Critical Distinction: Tool Acquisition vs. Strategic Integration Think of buying an AI tool like purchasing a state-of-the-art machine for a workshop. Its inherent value is immense, but if it sits in a corner, unused or without a defined process around it, that value remains entirely theoretical. It's an expense, not an asset generating return. Real AI integration , on the other hand, is a disciplined, multi-faceted endeavor. It's about deeply understanding your existing business processes, meticulously identifying precisely where intelligent automation can create genuine, measurable leverage. It involves designing a holistic system that delivers this leverage, and critically, establishing robust mechanisms to measure the actual outcomes against predefined business objectives. This holistic approach is strategy. The tools—be it an LLM, a specific AI platform, or an automation suite—are merely components that strategy carefully selects and orchestrates to achieve a greater aim. Where AI Integration Truly Crea

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I was working on a project and needed to convert some Markdown to HTML. Searched for it online, found a site, done. Next day I needed HTML back to Markdown. Searched again, different site. Then JSON to CSV. Then something else. Different site every time, half of them slow. At some point I just thought — why not build one site that handles all of this? So I did. That's QuickConvert . What It Is Just a collection of the conversions I kept searching for: JSON → CSV and back Markdown → HTML and back JSON → YAML XML → JSON CSV → JSON HTML → PDF Nothing fancy. No account needed. Everything runs directly in your browser — no data is sent anywhere, nothing is saved on a server. Why Astro I also wanted to try Astro for a while. I kept hearing it was great for content-heavy sites because of how little JavaScript it ships by default. A converter site felt like the perfect use case — mostly static pages with one interactive tool on each. Since Astro works with React components, it wasn't a big adjustment once I got the basics down. You write your page layout in .astro files and drop in React components where you need interactivity. Clicked pretty quickly. The result — 100 on Lighthouse across the board. The pages load instantly because there's barely anything to load. Hosting Deployed on Cloudflare Pages (now cloudflare workers). Free tier. The only thing this site costs me is the domain name. Try It quickconvert.dev Runs in your browser, no account, no data saved anywhere. I'm planning to keep adding more conversions — the everyday ones that developers reach for and end up Googling every single time. Maybe we can make something that becomes a tab that just stays open. Feedback welcome — especially if a conversion you need isn't there yet.

Programming is all about solving problems efficiently. Two concepts that play a major role in writing reusable and efficient programs are loops and functions . Loops help us perform repetitive tasks without writing the same code again and again, whereas functions help us organize code into reusable blocks. Let's understand these concepts in detail. Why Do We Need Loops? Suppose we want to print "Hello" five times. Without loops, we would write: console . log ( " Hello " ); console . log ( " Hello " ); console . log ( " Hello " ); console . log ( " Hello " ); console . log ( " Hello " ); Although this works, it violates one of the fundamental principles of programming: Don't Repeat Yourself (DRY) Repeating code: Increases the number of lines. Makes maintenance difficult. Introduces more chances for errors. Loops solve this problem by allowing us to execute the same block of code multiple times. Types of Loops in JavaScript JavaScript provides three looping statements: Loop Type Category while Entry-Check Loop for Entry-Check Loop do...while Exit-Check Loop Entry-Check Loop / Entry-Controlled Loop In entry-Check loops, the condition is checked before executing the loop body. If the condition is false initially, the loop body never executes. Examples: while loop for loop Exit-Check Loop / Exit-Controlled Loop In an exit-Check loop, the loop body executes first and then checks the condition. Therefore, the body executes at least once. Example: do...while loop Components of Every Loop Every loop generally consists of three parts: 1. Initialization Determines where the loop starts. let i = 1 ; 2. Condition Determines whether the loop should continue executing. i <= 5 3. Increment or Decrement Updates the loop variable after each iteration. i ++ ; or i -- ; 1. while Loop The while loop repeatedly executes a block of code as long as the condition remains true. Syntax while ( condition ) { // statements } Example: Print Numbers from 1 to 5 let i = 1 ; while ( i <= 5 ) { cons

If you use Claude daily, you've probably hit a wall mid-task. The reason it's so easy: Claude doesn't have one limit. It has several. A rolling 5-hour session window A weekly pool shared by all models Separate weekly caps per model (Opus, Sonnet, Claude Design) And the weekly pool is sneaky: Fable has no cap of its own and draws that shared pool down ~2× faster than Opus . So you can burn the week without realizing it. I wanted that state visible at a glance instead of digging through settings, so I built Claude Usage Monitor — a small browser extension for Chrome and Firefox. What it does It puts a color-coded % badge in your toolbar , and one click shows the full breakdown: Current 5-hour session + live countdown to reset Weekly all-models pool (with the Fable draw-down flagged on the card) Per-model weekly caps: Opus, Sonnet, Claude Design Daily Claude Code routine runs Extra-usage credits + prepaid balance for pay-as-you-go Auto-detects your plan (Pro / Max 5x / Max 20x / Team), so every number matches your subscription Six themes The privacy part (the part I cared about most) It's open source (MIT) and runs 100% locally — no backend, no analytics, no servers. A design decision I'm happy with: the host permissions are scoped to exactly four claude.ai endpoints (org list, usage stats, routine-run budget, prepaid balance). It physically can't read your chats, projects or files. Narrow permissions = less to trust, and less to exploit. One honest caveat: it reads claude.ai's own usage endpoints, so it can break if Anthropic changes them. Not affiliated with Anthropic. Try it It's free, no account needed (desktop Chrome & Firefox): 🔗 claude-monitor.com Chrome Web Store Firefox Add-ons Happy to hear what else you'd want tracked — drop a comment.

Six months ago, I could tell you which model to use for almost any job, and I would have said it with confidence. Today I hedge, and so does almost everyone I talk to who builds with these tools. The reason is simple. The ground keeps moving under us. Models get smarter on a schedule no one can forecast, and they get cheaper to run on a second schedule that is just as hard to predict. Both curves are bending at once, and they point in directions that change how I build and how I think you should build too. I spend my days crafting development, content and productivity workflows that lean on these models. I wire up agents, route tasks, and watch the bills. So this is not a far-off observation for me. It is the thing I am living with week to week, and it has forced me to rethink habits I held for years. This is not the usual story about a single breakthrough. It is four shifts happening together. Frontier performance is climbing past what most of us guessed was possible this year. Small models are getting good enough to run on a phone or a thirty-five dollar computer. The smart move has stopped being "pick the best model" and started being "build a system that picks for you." And a coding startup with a rocket company behind it is showing what happens when product, data, and compute sit under one roof. Let me take these one at a time. Then I want to show you what they add up to, because the sum is bigger than the parts. Performance Is Outrunning the Forecasts Start at the top of the market, where the most capable models live. Anthropic now ships a tier above its Opus line. The Fable and Mythos family is a class of model built for problems that smaller systems still fumble: long chains of reasoning, deep code work, research that needs to hold many threads at once. Claude Fable 5 carries extra safety work so it can go out to the public. A more powerful sibling, used inside a small set of trusted partners, stays behind tighter controls. The names are not the point. The p