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There are over a dozen cases around the country where police officers are using the Flock surveillance camera system to obsessively and illegally stalk people . Alternate link .

Sitting at a desk for hours? Upgrade your WFH setup and work in style with these comfy WIRED-tested seats.

Sitting at a desk for hours? Upgrade your WFH setup and work in style with these comfy WIRED-tested seats.

Sitting at a desk for hours? Upgrade your WFH setup and work in style with these comfy WIRED-tested seats.

Lenovo announced a new version of its chonky speaker-filled Tab Plus tablet that once again puts a strong focus on audio. While the original Tab Plus launched two years ago with eight built-in speakers requiring a 13.58mm rear bulge to squeeze them all in, the new Tab Plus Gen 2 bumps that to nine speakers […]

The Meta-owned social platform announced a series of new features launching today, including a "Your Algo" tool that lets users control what they see in their feeds

Sellers on TikTok used pricey items to lure bidders to “Surprise Sets” livestreams, but most of the auctions yielded only cheap prizes. Gambling experts are concerned about the potential for harm.

Despite being an objectively terrible digital camera, the Kodak Charmera has been incredibly popular thanks to a cheap price tag and several fun retro designs inspired by the iconic 1987 single-use Kodak Fling. Instead of entirely rethinking that formula, Reto, the company licensing the Kodak brand, is following up on the original Charmera with a […]

Threads has surpassed 500 million monthly active users, Meta announced on Tuesday, hitting the milestone just shy of the platform's third birthday. Threads got off to a hot start in 2023, reaching 100 million users even faster than ChatGPT, and CEO Mark Zuckerberg has said that he thinks Threads could hit 1 billion users. Meta […]

Turn any photo into a beautiful wallpaper Discussion | Link

Amazfit’s trail running smartwatch delivers solid off-road performance at a bargain price compared to its competitors.

The chatbot still remains the most popular AI assistant worldwide with over 1.1 billion monthly users, followed by Gemini with 662 million and Claude with 245 million.

At 21, Speed has pushed the limits of streaming by transforming a distinctly solo format into a global group chat. His song for this year’s World Cup is becoming the tournament’s unofficial anthem.

My AWS Bedrock Experiment Cost Me $8.43 in Just One Day What I learned about AWS Bedrock pricing the hard way, and why budget alerts saved me Why I Even Tried Claude Code on Bedrock I have been using Claude Code for a while now, connected to Anthropic directly. It works well. But two things were bothering me. First, the usage limits. Claude Code on Anthropic's native setup has 5hours session limit and a weekly usage cap. Once you hit it, you have to wait. If you are in the middle of something or just want to experiment freely, that gets frustrating fast. Second, billing. I already manage everything on AWS. I'm very familiar with it, the invoices go to one place, and I understand how to track and control costs there. Adding a separate Anthropic subscription meant one more billing account, one more credit card charge, one more thing to track. I just wanted everything under one roof. So I thought, why not try Claude Code connected to Amazon Bedrock? Same tool, runs on AWS, billed through AWS. Seemed like a clean solution to both problems. What happened next is why I am writing this post. The Two Ways to Run Claude Code Most people do not realise Claude Code can be configured to run in two different ways. Option 1: Claude Code via Anthropic directly You connect Claude Code to Anthropic's API or use it under your Claude subscription. Billing goes through Anthropic. If you are on a subscription plan, you pay a flat monthly fee and the usage limits apply to how much you can do within that. Option 2: Claude Code via Amazon Bedrock You connect Claude Code to AWS Bedrock as the backend. Same Claude models, but now AWS is your provider. Billing goes through your AWS account. No Anthropic subscription needed. From the outside, it looks and feels the same. But the billing model underneath is completely different, and that is where things get interesting. What Happened When I Tried It I set up Claude Code to use Bedrock and gave it a prompt. A fairly detailed one, nothing unusual

We Built Korexbase: A Lead Generation Platform for Finding Business Leads by City and Niche Building software is exciting. Building software that solves a real problem is even better. Over the past few months, we've been working on Korexbase , a lead generation platform designed to help businesses discover targeted leads faster. The Problem Many agencies, sales teams, freelancers, and startups spend hours manually searching for potential customers. The process usually looks something like this: Search for businesses online Collect contact information Copy everything into spreadsheets Repeat the process every day It's slow, repetitive, and difficult to scale. We wanted to simplify that workflow. The Idea Korexbase allows users to search for business leads by: City Industry Business category Instead of manually collecting data, users can generate leads and manage them through a clean dashboard. The goal isn't to replace sales. The goal is to help businesses spend less time searching and more time closing deals. Building the Platform A major focus during development was creating a dashboard that feels simple and easy to navigate. Some areas we focused heavily on included: Responsive layouts User-friendly navigation Clear data presentation Fast loading interfaces Consistent design patterns Challenges Like most projects, we faced a number of challenges: Designing for Simplicity One of the biggest lessons was that adding more features doesn't automatically create a better product. We spent a lot of time simplifying interfaces and removing unnecessary complexity. Creating a Better Dashboard Experience Presenting lead generation data in a way that is useful without overwhelming users required multiple design iterations. We focused on: Better spacing Better visual hierarchy Cleaner cards and tables Improved responsiveness Product Positioning An interesting challenge was refining the product's positioning. As development progressed, we learned more about what users actually w

Originally published on rikuq.com . Republished here for Dev.to's readers. I dropped my $100/month Claude Max subscription and migrated entirely back to Antigravity. If you want the verdict upfront: Claude Desktop is still the best tool for beginners who need the AI to guess their intent from clumsy prompts. But if you have solid documentation discipline and cost efficiency is a serious factor for your SaaS, Antigravity is now the clear winner. I'm a Chartered Accountant by trade with zero formal coding experience. I’ve shipped three production AI SaaS— Prism , Citare , and BatchWise —relying entirely on AI tools. I started with VSCode, moved to Antigravity (when it was just an IDE), and eventually landed on the Claude Desktop App. Claude was incredible; it operated in the background, handled my stack, and I didn't need to know what was happening under the hood. But the bills started stacking up. When my Claude usage consistently hit $100 a month, efficiency became a priority. I fired up the new version of Antigravity and found the recent updates had completely transformed it. It is no longer just an IDE—it is a full agentic desktop experience that mirrors what made Claude so good. TL;DR — The 2026 Reality Feature Claude Desktop App Antigravity (New Update) Best for Beginners, unlimited budgets, "pure performance" Experienced AI directors, cost-conscious solo founders Pricing $100+/mo (Claude Max) $20/mo (Gemini Advanced) Agentic Workflow Exceptional. The benchmark. Identical. Background execution, zero friction. Context Handling Better at anticipating intent from messy prompts Huge total memory, but requires tighter prompting MCP Support Native Native (handles them just as well) Verdict Keep it if cost doesn't matter Switch to it if efficiency is the goal The Catalyst for Switching My path to Antigravity wasn't a calculated feature comparison. It was pure economics combined with a pleasant surprise. I had previously dropped Antigravity when it was just an IDE. When

Ship AI agents like web apps, in minutes. Discussion | Link

So here's what happened: i Wish I Knew AI Recommendation Sooner — Here's the Full Breakdown Last quarter I burned through about three billable hours debugging a recommendation pipeline for a Shopify client. The thing was — it shouldn't have taken that long. I had the data. I had the API keys. What I didn't have was a clear-eyed picture of what AI recommendation systems actually cost in 2026 when you're paying the bills yourself. If you freelance like I do, every line item matters. My "office" is a kitchen table, my "PM" is a Slack ping at 11pm, and my CFO is whatever's left in my checking account after software subscriptions. So when I say I've been digging into the numbers on AI recommendation systems for the last six weeks, I mean I've been doing it the way I do everything: with a calculator open in one tab and a client invoice in the other. This post is the writeup I wish I'd had before I started. Consider it the field guide for anyone building recommendation features on a budget, on a deadline, or just for fun. Why I Even Cared About Recommendation Systems I took on a small retainer back in February for an indie e-commerce shop that sells specialty coffee beans. They wanted "AI-powered product recommendations" on their storefront — you know, the classic "customers who bought this also bought..." thing, but smarter. The owner had been quoted $15,000 by a "full-service AI agency" to build it. He doesn't have $15,000. He has $15,000 in revenue per month and a wife who is deeply skeptical of his side-hustle energy. So he came to me. And I said yes, because I'm a sucker and also because I knew it should cost a tiny fraction of that quote. The math was never going to support five figures for a recommendation widget. Not when the underlying API calls are fractions of a cent. That's when I started really paying attention to the pricing landscape. The 184-Model Elephant in the Room Here's the thing nobody tells you when you start shopping for LLMs: there are a lot of the

I'm not here to trash Sololearn. Sololearn taught millions of people how to code. It was one of the first apps to make programming education feel mobile-native. That's a real achievement. I respect it. But I'm building Codino — a Python learning app — and I'd be lying if I said I didn't study Sololearn carefully before writing a single line of code. I looked at what they got right. I looked at where users complained. And I made decisions based on both. This is that honest breakdown. What Sololearn Got Right 1. The Community Feel Sololearn built a genuine community. The code playground where users share their projects, comment on each other's code, and get likes — that was smart. Learning feels less lonely when other people are doing it alongside you. It created a social loop that kept people coming back even when they weren't actively doing lessons. I haven't built this yet in Codino. The leaderboard is a start, but a full community layer is something I'm thinking about for a future update. 2. Multi-Language Support Sololearn didn't bet on just one language. Python, JavaScript, C++, SQL, HTML — they covered everything. That gave them a massive addressable audience. Codino is Python-only right now. That's intentional — going deep on one language is better than going shallow on ten. But I understand why multi-language eventually matters for scale. 3. The Code Playground The ability to write and run real code inside the app — without going to a browser — was ahead of its time when Sololearn launched it. That feature alone brought back users who had finished all the lessons. Codino has a full offline IDE powered by Sora Editor. I'd argue ours is actually more capable — real syntax highlighting, autocompletion, offline Python execution — but Sololearn deserves credit for proving this feature matters. 4. Bite-Sized Lessons That Actually Work Sololearn understood that people learn on the bus, in bed, waiting in line. Their lessons are short, digestible, and don't demand 45

AI coding assistants have fundamentally changed the way we write software. Today it's perfectly normal to ask ChatGPT, Claude, Cursor, or Copilot to explain an API, generate a React component, review a pull request, or help debug a problem. For many developers, these tools have become part of the daily workflow. Yet there's one area where they still struggle more than we'd like to admit: understanding the current state of the web platform. Mozilla recently demonstrated this problem in a surprisingly direct way. While evaluating Claude Code on recently released Firefox features, the team discovered that the model confidently claimed Firefox didn't support the Web Serial API and that Mozilla had no plans to implement it. The answer sounded plausible, detailed, and authoritative. There was just one issue. Firefox had already shipped support for the API. That experiment became one of the motivations behind Mozilla's new MDN MCP Server , a tool designed to give AI assistants direct access to MDN documentation and browser compatibility data. More importantly, Mozilla didn't just launch the service—they tested whether it actually improves the quality of AI-generated answers. The results are worth paying attention to. The Real Problem Isn't Hallucination When discussions about AI reliability come up, the conversation usually focuses on hallucinations. But browser compatibility is a slightly different problem. The web platform evolves continuously. Browsers ship new APIs, CSS features, HTML capabilities, and compatibility updates every few weeks. Specifications change, Baseline statuses evolve, and features that were experimental yesterday can become production-ready tomorrow. Large language models, on the other hand, are trained on snapshots of information. Even highly capable models can only know what was available when they were trained. When they're asked about something that appeared later—or something that wasn't widely represented in their training data—they often hav