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

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:

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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

Every day, startups rent expensive GPUs to power AI applications. The problem is that most of those GPUs spend a surprising amount of time doing nothing. Imagine renting an apartment and only using one room while paying for the entire building. That's effectively what many AI teams do with GPU infrastructure. The Hidden Cost of GPU Rentals When you rent a GPU, you're usually paying for uptime. Whether your application is processing requests or sitting idle at 3 AM, the bill keeps running. For many early-stage products: Traffic is inconsistent Usage spikes are unpredictable Most requests arrive in short bursts As a result, GPU utilization can be far lower than expected. The Utilization Problem A startup might rent a GPU for an entire month. But how much of that compute is actually being used? During development: Developers test occasionally Demos happen a few times a day Customer requests arrive sporadically The GPU remains available 24/7, but actual inference workloads often occupy only a small fraction of that time. Yet the infrastructure bill reflects full-time usage. Why This Matters For startups, infrastructure costs directly affect runway. Every dollar spent on idle compute is a dollar that cannot be spent on: Product development Customer acquisition Hiring Experiments Reducing wasted infrastructure spend can significantly improve efficiency. A Different Model Instead of paying for GPU uptime, what if developers only paid when inference actually occurred? For example: Pay per token generated Pay per image generated Pay per second of video generated This approach aligns cost with actual usage rather than reserved capacity. The Future of AI Infrastructure As AI adoption grows, efficiency becomes increasingly important. The next generation of AI infrastructure may look less like traditional server rentals and more like utilities: Use what you need. Pay for what you use. Nothing more. What has your experience been with GPU utilization and AI infrastructure costs? I

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

How many times have you seen a picture of a plane with red dots posted on the internet without context? There's a famous story about a statistician named Abraham Wald and a bunch of WWII bombers. The military looked at the planes coming back from combat, mapped where they were riddled with bullet holes, and decided to add armor there. Wald, being the kind of person who ruins meetings by being right, pointed out the obvious thing nobody wanted to hear: The planes they were looking at came back . The ones hit in the spots with no bullet holes, the engine, the cockpit, were at the bottom of the English Channel, not available for the survey. Reinforce the parts that aren't shot up. That's where the dead planes got hit. I think about this story a lot, mostly while reading those blog posts titled "X Habits That Made Me a 10x Engineer." The entire industry is a returning-plane survey Here is the uncomfortable thing about software engineering wisdom: almost all of it is collected from the planes that came back. Successful companies write blog posts. Successful founders do podcast tours. Successful engineers give conference talks with titles like "Scaling to 100 Million Users with Three People and a Dream." The companies that did the exact same things and died do not have a booth at the conference. They are not on the panel. They are in the channel, with the engines. And yet we keep doing the survey. We stare at the bullet holes on the survivors and go, "Ah, this is where we add armor." "Netflix uses microservices, so we should too" You have eleven users. Three of them are your co-founders, and one is your mom. Netflix runs a globe-spanning streaming empire on hundreds of microservices because they have hundreds of teams, billions in revenue, and problems you will be lucky to have in a decade. You have a Postgres database that is doing just fine, thank you, and a monolith that boots in four seconds. So naturally, you spend the next eight months splitting your perfectly funct

Hello Dev Community! 👋 It is Day 32 of my continuous web development run, and today I jumped into a project that pushed my array manipulation and conditional logic to a whole new level: A complete Snake and Ladder Board Game using HTML5, CSS3, and Vanilla JavaScript! After building Rock Paper Scissors yesterday, I wanted to tackle a game that requires tracking persistent coordinate states across a 100-cell mathematical grid. 🛠️ The Game Architecture & Logic Breakdown Building this wasn't just about random numbers; it was about managing spatial transitions on a dynamic interface. Here is how I structured the core backend mechanics: 1. The 100-Cell Grid Layout Instead of manually hardcoding 100 divs inside my index file, I engineered the grid programmatically. I mapped out a loop running from 100 down to 1, building individual cell elements and using CSS Grid properties to wrap them perfectly into a standard 10x10 layout matrix. 2. Mapping Snakes & Ladders (The Jump Engine) To build the shortcuts and traps, I didn't write massive, messy if-else trees. Instead, I utilized a clean JavaScript Object Map tracking key-value pairs where the key is the trigger tile and the value is the destination tile: javascript const gameModifications = { // Ladders (Climbing up) 4: 14, 9: 31, 21: 42, 28: 84, 51: 67, 72: 91, 80: 99, // Snakes (Sliding down) 17: 7, 54: 34, 62: 19, 64: 60, 87: 36, 93: 73, 95: 75, 98: 79 };

A Silicon Valley software maker and an ecommerce company reveal to WIRED how they are navigating the emerging challenge of “tokenomics.”

Every engineering team hits this conversation at some point. Someone proposes GraphQL. Someone else says REST is fine. A third person mentions gRPC and half the room goes quiet. The debate usually ends with the most senior person in the room picking what they're most familiar with. That's not a strategy — that's habit. Here's an objective breakdown of all three, when each one wins, and how to actually make the decision for your specific use case. The Core Mental Model Before comparing them, understand what each one is optimizing for: REST optimizes for simplicity and broad compatibility GraphQL optimizes for flexibility and precise data fetching gRPC optimizes for performance and strongly-typed contracts None of them is universally better. Each one is a tradeoff. The right answer depends entirely on who is consuming your API and what they need from it. REST — The Default That Still Wins Most of the Time REST (Representational State Transfer) is not a protocol. It's an architectural style built on HTTP — verbs, URLs, and status codes most developers already understand. Where REST genuinely wins: Public APIs. If external developers are consuming your API, REST is the only reasonable default. The tooling, documentation patterns, and developer familiarity are unmatched. Stripe, Twilio, GitHub — all REST. Simple CRUD services. If your resource model is straightforward, REST maps cleanly to it. No overhead, no learning curve, no ceremony. Browser-native requests. REST over HTTP works directly in the browser without any special client. Fetch it, done. Where REST struggles: Over-fetching and under-fetching. A single REST endpoint returns a fixed shape. Mobile clients that need 3 fields get 40. Separate data needs often require multiple round trips. Versioning overhead. As covered in our previous post — every breaking change forces a versioning decision. This compounds quickly on complex APIs. GraphQL — Powerful, But You Need to Earn It GraphQL is a query language for your A

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Tools like Claude Code and Codex have completely reshaped how software engineering is done. This new tooling allows for much faster development and iteration, but it's important to keep the code maintainable and scalable to make sure the project can continue evolving over the long term. A template project with an initial structure using all of the technologies described here is available on GitHub: https://github.com/MartinXPN/nextjs-firebase-mui-starter When working on a startup, the speed of iteration is key. The requirements change quickly, features are added daily, and code gets modified rapidly. In those conditions, picking technologies that enable fast iteration, while ensuring your users get the best experience possible, is crucial. During the last four years or so, we have experimented with many modern technologies while building Profound Academy . So, in this blog post, I'd like to present the whole tech stack that enables building quickly, while having a highly maintainable codebase, scalable infrastructure, and a great user experience. We'll cover everything from Authentication to UI, we'll talk about the backend, hosting, testing, and much more! AI Agents, Skills, and MCP servers AI Agents enable quick iteration and rapid improvement, including bug fixes, the addition of new features, and performance improvements. Yet, it's important to keep the code maintainable for the long run. AI tools make it really easy to overengineer things and add thousands of lines of code to a project. It's important to resist the urge to solve problems that don't exist yet, and keep things simple (both in terms of the code, the infrastructure, and the user experience). Even in the Agentic Software Development Era, having a small and simple setup helps. Agents coordinate better, features are added faster, bugs are fixed more easily, and the code is maintainable by humans, too. So, we have chosen to take a balanced/nuanced approach to how we use AI Agents when it comes to worki