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The restrictions include a nationwide ban on Telegram until June 22 and a requirement to disable the app's message editing feature.

I'll just work from the car, I thought. But after a few minutes of staring at my screen on quick mountain switchbacks I could feel the first signs of cold, coagulated nausea bubbling up from that sweaty place in my gut. I looked to the horizon for relief, but nothing helped… until I remembered Apple's […]

AI coworker in Slack that builds reports, work tools, + more Discussion | Link

The deal is supposed to help SpaceX's struggling AI division. The company told IPO investors it sees a $26 trillion addressable market in AI.

SearchLeak exploit shows why the industry's approach to LLM security fails over and over.

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.

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

Turn any photo into a beautiful wallpaper Discussion | Link

submitted by /u/BlondieCoder [link] [留言]

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.

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

This module built one thing, from many angles: the container — the part of Spring that creates your objects, wires them together, and hands them out. Eight articles each zoomed in on a different corner of it. This recap zooms back out. The goal here is not to re-explain each topic, but to show how they are all the same idea seen from different sides, so the whole module collapses into a picture you can hold in your head at once. So before the details, here is the single sentence the entire module hangs on: the container is a factory that runs at startup, and almost every feature you met is just that factory doing a little extra work while it builds a bean. Keep that sentence close. Everything below is a way of filling it in. The factory, in one picture Picture an assembly line that runs exactly once, when your application boots. You hand it a list of what to build and how the pieces fit. It builds every object your app is made of, connects them, sets them on a shelf, and hands them out on request for the rest of the program's life. That assembly line is the container. The objects it builds and manages are beans . An object you create yourself with new is not a bean — Spring never touched it — and that distinction is the thread running through every trap in this module. The factory does four things at startup, and the order matters: it reads recipes, works out who needs whom, builds from the bottom up, and caches each result. ApplicationContext ctx = SpringApplication . run ( App . class , args ); OrderService svc = ctx . getBean ( OrderService . class ); // already built and wired By the time run returns, the work is done. Asking for a bean is instant because the building already happened. Every other topic in the module is a detail about how that one startup pass works. Why we hand the work over at all The module opened with a question of control. Left alone, a class builds its own collaborators with new — and in doing so it welds together two unrelated decisions:

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