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Stop Writing the Same Laravel Boilerplate: Generate a Complete Module with One Artisan Command Every Laravel developer has experienced this. You start implementing a new feature and immediately create the same files you've created dozens of times before: Model Migration Repository Service Form Request API Resource Policy Filter Status Enum Feature Tests Unit Tests Swagger/OpenAPI annotations The process is repetitive, time-consuming, and easy to get wrong. The Problem While Laravel provides excellent generators, building a production-ready API module still requires running many Artisan commands and wiring everything together manually. For large projects following Repository and Service Layer architectures, this becomes even more repetitive. The Solution I built Laravel Base , an open-source package that generates an entire production-ready module from a single command. php artisan make:module Product The generated module includes: ✅ Model ✅ Migration ✅ Repository Pattern ✅ Service Layer ✅ Form Requests ✅ API Resources ✅ Filters & Pagination ✅ Policies ✅ Status Enums ✅ Swagger/OpenAPI annotations ✅ Feature Tests ✅ Unit Tests Modern Development Experience The package is actively maintained and includes: Laravel 10–13 support PHP 8.1–8.4 compatibility GitHub Actions CI PHPStan static analysis Laravel Pint code style Automated releases Repository automation Why I Built It After working on multiple Laravel projects, I noticed I was spending too much time generating the same project structure instead of focusing on business logic. I wanted a tool that lets developers start implementing features immediately rather than setting up folders and classes. Feedback Welcome Laravel Base is open source, and I'd love to hear your thoughts. GitHub Repository: https://github.com/MuhammedMSalama/LaravelBase Packagist: https://packagist.org/packages/muhammedsalama/laravel-base The package was recently featured by Laravel News, and I'm continuing to improve it based on community feedbac

For twenty years, "ranking" meant one thing: get indexed, get crawled, get a position on a results page. Every Shopify store's SEO checklist was built around that single goal. Sitemap submitted, meta tags filled in, Core Web Vitals green, done. That checklist still matters. It's also no longer sufficient, and most stores haven't noticed yet. Two different systems, two different jobs Google's index and an LLM's answer engine are not the same kind of system, even though they both "read" your store. A search index is a retrieval system. It crawls a page, tokenizes the content, stores it, and matches it against a query at request time. Ranking is a function of relevance signals backlinks, click-through behavior, freshness, page experience. The unit of output is a list of links. The user does the synthesis. An LLM-based answer engine is a generation system. When someone asks ChatGPT, Perplexity, or Claude "what's a good Shopify store for sustainable activewear," the model isn't returning a ranked list of crawled pages. It's generating a single answer, and it decides which brands to name in that answer based on which entities it has high confidence are real, relevant, and well-attested across multiple sources. The unit of output is a sentence. The model does the synthesis, and your store either gets a mention in that sentence or it doesn't. This is the gap. A store can be fully indexed sitemap clean, every product page crawlable, ranking on page one for its category and still never get named in an AI-generated answer. Indexing is a necessary condition for citation. It is not a sufficient one. What "citable" actually requires Citation in an LLM context isn't about keyword matching. It's closer to reputation modeling. Three things tend to separate stores that get cited from stores that don't: Entity consistency across the web. The model needs to resolve "your brand" as a single, stable entity across multiple independent sources your own site, marketplaces, press mentions, r

Introduction As applications grow, traditional relational databases such as MySQL may struggle with analytical workloads involving millions of records and complex aggregations. While MySQL excels at Online Transaction Processing (OLTP), ClickHouse® is purpose-built for Online Analytical Processing (OLAP), enabling lightning-fast analytical queries on massive datasets. Migrating data from MySQL to ClickHouse® allows organizations to build high-performance reporting systems, dashboards, and real-time analytics without impacting transactional workloads. In this guide, you'll learn several approaches to migrate data from MySQL to ClickHouse®, along with their advantages, limitations, and ideal use cases. Why Migrate from MySQL to ClickHouse®? MySQL and ClickHouse® are designed for different workloads. Feature MySQL ClickHouse® Storage Model Row-based Columnar Best For Transactions (OLTP) Analytics (OLAP) Query Speed Fast for row lookups Extremely fast for large scans Aggregation Performance Moderate Extremely fast Scalability Primarily Vertical Optimized for analytical scaling Typical Use Cases Applications and transactional systems Reporting, dashboards, and analytics Migrating from MySQL to ClickHouse® makes sense when: Analytical queries are becoming slow in MySQL. You need real-time dashboards over large datasets. Reporting queries are impacting your production database. You regularly process millions or billions of rows. Migration Architecture MySQL │ ▼ Export / Synchronization │ ▼ Data Transformation │ ▼ ClickHouse® │ ▼ Dashboards / Analytics Migration Methods There are multiple ways to migrate data depending on your requirements. Method 1: CSV Export and Import (Recommended for Beginners) This is the simplest approach for performing a one-time migration of historical data. Step 1: Export Data from MySQL Run the following command inside MySQL: SELECT * INTO OUTFILE '/tmp/employees.csv' FIELDS TERMINATED BY ',' ENCLOSED BY '"' LINES TERMINATED BY ' \n ' FROM employ

A few months ago I noticed something annoying about how I worked: I was spending more time collecting information than actually thinking about it. The pattern was always the same. Open a search engine, open a dozen tabs, skim past the SEO filler and cookie banners, copy the paragraphs that actually mattered into a doc, paste the whole mess into an LLM and ask it to make sense of things. Then, a week later, do it again because whatever I was tracking had changed. At some point I stopped asking "how do I do this faster" and started asking why I was doing it by hand at all. Why the obvious answers didn't work ChatGPT and Perplexity are fine for a single question. They're worse at the part I actually needed help with, which was repetition: running the same research loop on a schedule, keeping a record of what changed, and getting a notification when it did. Neither tool is built to sit in the background and check on a topic for you. Plain scraping scripts have the opposite problem. They get you raw HTML, not understanding. You still have to strip out nav bars and footers by hand, and the moment you point one at a list-style page like Hacker News instead of a blog post, it falls apart. And bookmarking is just deferring the problem. A folder of forty saved links isn't research, it's homework you haven't done yet. I wanted something in between: automated enough to skip the tab-hoarding, but still producing something I could read and trust, not just a black-box answer. So I built Focal Harvest It's a modular CLI that runs the whole research loop, search, scrape, clean, synthesize, report, on its own, and stays lightweight enough to run on a laptop with no GPU and no database. A single run looks like this: you give it a topic and a focus area (what you specifically want answered), it searches the web, pulls and cleans the pages, synthesizes a report, and writes it to disk. There's also a loop mode, so the same query can re-run every few hours and ping you on Discord or Teleg

Most developers have experimented with ChatGPT or GitHub Copilot. But when it comes to building AI-powered applications, simply calling an LLM API isn't enough. Understanding what's happening behind the scenes helps you design systems that are scalable, reliable, and cost-effective. In this article, we'll explore four concepts every software engineer should know: tokens, embeddings, transformers, and Retrieval-Augmented Generation (RAG). 1. LLMs Think in Tokens, Not Words One of the biggest misconceptions about Large Language Models (LLMs) is that they understand words like humans do. In reality, they process tokens, which are smaller units of text. For example: Prompt: Explain dependency injection in Spring Boot. is first converted into a sequence of tokens before the model processes it. Why does this matter? API pricing is based on the number of input and output tokens. Longer prompts increase latency and cost. Every model has a maximum context window measured in tokens. When building AI applications, prompt design isn't just about getting better answers—it's also about optimizing performance and cost. 2. Transformers: The Breakthrough Behind Modern AI Before 2017, language models processed text one word at a time using architectures like RNNs and LSTMs. They struggled with long conversations because earlier context was gradually forgotten. The introduction of the Transformer architecture changed this with a mechanism called self-attention. Instead of reading text sequentially, transformers analyze the relationships between all tokens in a sentence simultaneously. Consider this sentence: "The server restarted because it ran out of memory." The model understands that "it" refers to "the server", not "memory", by assigning attention to the relevant words. This ability to capture context efficiently is what powers modern LLMs like GPT, Gemini, Claude, and Llama. 3. Embeddings Enable Semantic Search Suppose a customer searches: "How can I get my money back?" But your

The maps and images show the extent of destruction and give rescue operations a tool to find any remaining survivors.

A device that resets in the field is not always the hardest problem. The harder problem is a device that resets, comes back online, and leaves no evidence about what happened before the reboot. That is where a firmware black box becomes useful. This is the DEV.to edition of a Silicon LogiX technical article. The canonical English source is linked at the end. What a firmware black box is A firmware black box is a small diagnostic subsystem inside the firmware. Its job is to preserve enough information to support post-mortem analysis after a reset, watchdog event, HardFault, panic or unexpected reboot. It does not need to record everything. It needs to record the data that helps answer the first diagnostic questions: why did the device reset? how long had it been running? which firmware build was installed? what state was the application in? which task was active? did the watchdog fire? did memory, stack or heap margins collapse? did the network, modem, BLE, Wi-Fi or OTA flow fail just before the reboot? Without that data, every field reset deletes most of the evidence. Why sporadic resets are expensive Rare embedded bugs are often more expensive than obvious failures. A crash that happens every time in the same function can usually be analyzed with a debugger, logs and a repeatable test. A reset that appears once every ten days on a customer device is different. The cause may depend on a combination of: temperature unstable power brown-out cable length enclosure heating network drops modem state memory fragmentation stack exhaustion long uptime race conditions a peripheral that stops responding an OTA edge case In the lab, the product may look clean. In the field, the environment changes. The customer report often becomes: "it rebooted", "it stopped communicating", or "we had to power-cycle it". That is not enough for firmware diagnosis. What to capture A good first version does not need to be large. Start with a compact structure that survives the next boot: reset r

React useIntersectionObserver Hook: Lazy Load & Detect Visibility (2026) You want to load an image only when it scrolls near the viewport. Or fire an analytics event the first time a card is actually seen . Or trigger "load more" when the user reaches the bottom of a list. Every one of these is the same question — is this element on screen yet? — and for years the answer was a scroll listener that fired hundreds of times a second, re-read getBoundingClientRect() on each tick, and still managed to miss the edge cases. IntersectionObserver is the browser API that answers that question correctly, asynchronously, and off the main thread. useIntersectionObserver is the hook that wires it into React without the useEffect / useRef /cleanup boilerplate — and without the leak-on-unmount and stale-closure bugs the hand-rolled version always ships. This post covers the real @reactuses/core API, the three patterns you'll actually reach for, and how to tune threshold , rootMargin , and root . SSR-safe and typed. Why Not Just Use a Scroll Listener? The old way to know whether an element was visible looked like this: listen to scroll , and on every event measure the element against the viewport. useEffect (() => { function onScroll () { const rect = el . getBoundingClientRect (); if ( rect . top < window . innerHeight ) { setVisible ( true ); } } window . addEventListener ( ' scroll ' , onScroll ); return () => window . removeEventListener ( ' scroll ' , onScroll ); }, []); This has two problems baked in. First, scroll fires on the main thread, dozens of times per second, and getBoundingClientRect() forces a synchronous layout each time — that's exactly the recipe for janky scrolling. Second, it only catches elements crossing the viewport ; the moment your scroll happens inside a container, you're re-deriving geometry by hand. IntersectionObserver flips the model. You hand the browser a target and a threshold, and it tells you — asynchronously, batched, off the scroll path — when

AWS launched Lambda MicroVMs, a new serverless compute primitive that runs each user session or AI agent in its own Firecracker virtual machine with hardware-level isolation, snapshot-based rapid launch, and state preservation for up to eight hours. Reddit community analysis found the minimum setup costs $3.03/day, roughly 9x Fargate spot pricing. By Steef-Jan Wiggers

New OpenAI Signals data shows how ChatGPT adoption is growing globally, with users increasing usage, exploring more capabilities, and driving growth across regions and languages.

Event-driven architecture promises scalability, but in Java-based real-time systems the tradeoffs only surface in production. Drawing on a Java/Kafka contact center platform handling 80k BHCC across 10k agents, this article details where the design breaks down—state management, partition limits, deduplication, JVM tuning, cascading consumer failures—and the Redis-backed patterns that fixed each. By Sagar Deepak Joshi

OKX is bringing together payments, identity and reputation into a marketplace for AI agents.

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A few months ago I was applying for jobs and stumbled across Jobscan. It looked exactly what I needed — paste your resume, paste the job description, see how well you match. Then I saw the price. $49.95/month. As a student, that's a week of groceries. I closed the tab. But the problem didn't go away. I kept wondering — why is my resume getting rejected before a human even reads it? ATS systems are filtering people out and nobody tells you why. So I built ClearScan. What it does: Scans your resume against a job description. Shows exactly which keywords you're missing. Checks ATS compatibility across 5 platforms (Workday, Taleo, Greenhouse, Lever, iCIMS). Scores your bullet points using STAR format analysis. Gives you a transparent breakdown — you can see why you got the score you did. That last part matters to me a lot. Most tools just give you a number. ClearScan shows you the math. Where it stands: Launched today. First paying customers already. Free tier gives you 2 scans/month — enough to feel the product before deciding. Pricing starts at €3.99/month. Built for students, priced for students. Live at clearscan.fyi — would genuinely love your feedback, especially from developers who've dealt with ATS hell themselves.

This week in COSS: The acquisition trend continued as Qualcomm agreed to acquire Modular for nearly $4 billion, Cursor quietly acquired open-source coding assistant Continue, and Elastic acquired AI SRE startup Deductive AI for up to $85 million. In funding news, DeepSeek closed over $7 billion in funding, Timefold raised a $13M Series A for its scheduling optimization platform, and Moonshot AI has reportedly sought a $30 billion valuation in new funding talks. In other announcements, Sentient Foundation committed $42 million to advance open-source AGI, Daytona announced it is going closed source, Vercel launched eve (an open-source agentic framework), Zilliz launched Vector Lakebase, Upbound open-sourced Modelplane (a control plane for AI inference), Bluesky COO Rose Wang discussed AI and the company's open-source approach, and ClickHouse announced Silk, a new fiber runtime. We also feature the following companies in Cossmology: ArcadeDB, Proton, HitKeep, Passbolt, Nirmata, Paper Compute Co., Plastic Labs, DuckLabs, Blacksky Algorithms, and Earendil Works. COSS Headlines Cursor quietly acquires Continue, an open-source alternative to GitHub Copilot Companies mentioned: Continue Announcement · The New Stack Introducing Modelplane: the control plane for AI inference Companies mentioned: Upbound Announcement · Modelplane Blog 'AI is taking away what makes us human' says social media boss Companies mentioned: Bluesky Media Mention · Metro DeepSeek closes $7bn-plus round with an unusual structure Companies mentioned: DeepSeek Funding · The Next Web Elastic reportedly acquires site reliability engineering startup Deductive AI Companies mentioned: Elastic Announcement · SiliconANGLE Vercel launches eve, an open-source framework that treats agents as directories Companies mentioned: Vercel Announcement · The New Stack Zilliz Launches Vector Lakebase, Extending the World's Most Adopted Vector Database into a Unified Data Platform for AI Companies mentioned: Zilliz Announcem