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A String in JavaScript is a sequence of characters used to store text. let course = " JavaScript " ; 1. String length Purpose Returns the total number of characters in a string. Syntax string . length Example let company = " OpenAI " ; console . log ( company . length ); Output 6 Real-Time Example Checking password length before registration. 2. String charAt() Purpose Returns the character at a specified index. Syntax string . charAt ( index ) Example let city = " Madurai " ; console . log ( city . charAt ( 3 )); Output u Internal Logic M a d u r a i 0 1 2 3 4 5 6 Index 3 contains "u". 3. String charCodeAt() Purpose Returns the Unicode value (UTF-16 code) of a character. Example let letter = " A " ; console . log ( letter . charCodeAt ( 0 )); Output 65 More Examples console . log ( " a " . charCodeAt ( 0 )); Output: 97 4. String codePointAt() Purpose Returns the Unicode code point of a character. Useful for emojis and special symbols. Example let emoji = " 😊 " ; console . log ( emoji . codePointAt ( 0 )); Output 128522 Difference console . log ( " 😊 " . charCodeAt ( 0 )); console . log ( " 😊 " . codePointAt ( 0 )); codePointAt() gives the actual Unicode value. 5. String concat() Purpose Combines two or more strings. Example let firstName = " Annapoorani " ; let lastName = " Kadhiravan " ; let fullName = firstName . concat ( lastName ); console . log ( fullName ); Output Annapoorani Kadhiravan Alternative console . log ( firstName + lastName ); 6. String at() Purpose Returns character at a specific position. Supports negative indexing. Example let language = " JavaScript " ; console . log ( language . at ( 0 )); console . log ( language . at ( - 1 )); Output J t 7. String [ ] Purpose Access characters using bracket notation. Example let laptop = " Dell " ; console . log ( laptop [ 0 ]); console . log ( laptop [ 2 ]); Output D l Difference console . log ( laptop . charAt ( 0 )); console . log ( laptop [ 0 ]); Both return same result. 8. String slice() Purpose Extract

A year ago, "it's just a ChatGPT wrapper" was a dismissal. You'd hear it about a startup and know what it meant: an LLM API call, a little RAG, file upload, a chat box on top. Thin. Replaceable. Probably dead the next time the base model shipped a feature. I keep coming back to that phrase, because it stopped being true in a way I didn't notice happening. The thing you'd be wrapping is no longer a model with a chat UI. It's a fast, stateful web application with its own agent loop, its own sandbox, its own artifact system. The wrapper didn't get easier to build as the models got better. It got heavier . The simple interface hides the hard part. A ChatGPT-shaped product is not just an API call with a chat box around it; it's the accumulation of many product and infrastructure decisions that make execution feel safe, stateful, and immediate. The model is the part you can buy. The surrounding runtime is the part people had to design. What gets me is the timescale. It's been roughly a year, and the question actually worth arguing about has moved out from under us — from "is this just a wrapper?" to "where does the sandbox even run?" The pace is faster than I can comfortably track. And the part I keep finding fun is that it all bends toward the practical, not away from it: every one of these shifts makes the tools more usable, more real, closer to something you'd actually ship. Surprising and, honestly, a good time to be building. This isn't a "wrappers are over" argument, and it isn't advice. It's me writing down where my thinking has drifted while trying to build these things myself — partly so I can find out where it's wrong. Read it as one person's notes. What "wrapper" used to mean The old shape was honestly small. Roughly: prompt → LLM API → (RAG retrieval) → response + file parsing on the side The whole game was prompt design, a retrieval index, and some glue. You could stand it up in a weekend. The reason "wrapper" was an insult is that the surface area was tiny —

Almost every "generate a PDF" feature starts the same way. You already have HTML. You already have CSS. So you reach for the obvious move: render the page, screenshot it to PDF, ship it. Puppeteer, Playwright, wkhtmltopdf, a hosted "HTML to PDF API" — pick your flavor. In an afternoon you have an invoice coming out the other end and it looks fine. Then it goes to production. And "fine" slowly turns into a backlog of weird, hard-to-reproduce bugs. This is not an argument that HTML-to-PDF is useless. For a one-off export or an internal report, it's great. The argument is narrower: the moment PDF generation becomes a real, automated, customer-facing part of your product, "screenshot a web page" is the wrong abstraction — and the failure modes are predictable enough to list in advance. The core problem: a PDF is not a web page A browser renders for an infinite, scrollable, single-width viewport. A PDF is a stack of fixed, finite, printable pages. Those are different physics. HTML-to-PDF works by rendering your page in a headless browser and then slicing that continuous render into page-sized pieces. Everything that's hard about it comes from that one mismatch: you designed for a stream, and now you're forcing it into pages. Most of the bugs below are just that mismatch showing up in different costumes. Failure mode 1: pagination This is the big one. A browser has no concept of "page 2." So when your content is taller than one page, the engine has to guess where to cut — and it cuts wherever the pixel ruler lands. That means: a table row sliced in half across the page break a heading stranded alone at the bottom of a page, its content on the next a total row that floats away from the table it belongs to a signature block split from the line above it CSS has break-inside: avoid , break-before , and friends — and they help. But support is uneven across engines, they interact badly with flex/grid, and you end up hand-tuning rules per document until it looks right for the da

Anthropic's critics argue it's rapidly accumulating power. The company says that's what responsible AI development looks like.

We tend to write React as functional programming because the functional component is the mainstream. In this era, one of the issues we often encounter is conditional statements. There are a variety of conditional statements, such as if, switch, and ternary operator. We confuse when to use them properly. Assign the result of the conditional statement into a variable This makes it easy to read, test, and modify codebases. The representative case is ternary operator const userName = user ? user . name : ' No user found ' ; Of course, we can write the code another way. const point = 80 ; let result ; if ( point >= 70 ) { result = ' passed ' ; } else { result = ' failed ' ; } console . log ( result ); // passed In this way, we can not ensure the immutability of let , and this section with the conditional branch is written in a procedural style. To solve this issue, we have to wrap this in a function. const judge = ( point : number ) => { if ( point >= 70 ) { return ' passed ' ; } return ' failed ' ; }; In addition to wrapping that statement, I suggest that you use early return to save the else statement. Do not write conditional statements in the return value of tsx (the UI rendering portion) ** When there is only a single conditional statement, or there is no need for any execution in the conditional statement. Let's use the ternary operation simply. import { FC } from ' react ' ; import { useQuery } from ' @tanstack/react-query ' ; import getUser from ' domains/getUser ' ; type Props = { userId : number ; }; const Profile : FC < Props > = ( props ) => { const { userId } = props ; const getSpecificUser = async () => { const specificUser = await getUser ( userId ); return specificUser ; }; const { data : user } = useQuery ([ ' user ' , userId ], getSpecificUser ); const userName = user ? user . name : ' User not found ' ; return < p > User ： { userName } < /p> ; }; export default Profile ; const userName = user ? user . name : ' User not found ' ; In this statement, you

A small word that changes the rhythm of a job For as long as I have been writing Actions workflows I have been carrying a quiet workaround in my head. Want to warm a cache while the build runs? Append & to the shell command, then squint at logs that arrive out of order and pray the job doesn't exit on you. It worked, sort of. It also meant that anything more interesting than "run one thing, then the next thing" lived as folklore, hidden inside run: blocks. GitHub closed that gap this week. On June 25 the Actions changelog announced that steps inside a job can now run concurrently, marked with a new background keyword and supported by helpers to wait for them and cancel them. Until now, the changelog notes, every step in a workflow ran in sequence, with each step starting only after the previous one completed. That single rule has shaped every workflow I have ever written. It is gone, and the replacement is the kind of feature you don't notice until the day you reach for it and it's there. What the keywords actually do There are four pieces, all of them documented in the announcement. background: true is the entry point. Set it on a step and that step starts running, and the next step starts immediately. It does not block the job. wait and wait-all are the rendezvous. wait pins on one or more named background steps and pauses until they finish. wait-all is the same idea against every background step still in flight. Either way you get back into a linear flow on your terms. cancel is the cleanup. It gracefully terminates a background step when you no longer need it, which is the missing piece if you have ever tried to kill a long-running side process from inside a job and ended up shelling out to kill . parallel is the convenience wrapper. The changelog describes it as taking a group of steps and converting them into background steps with a wait placed after. For the common "fan out, then join" shape, you write one block instead of decorating five steps by hand. Where

Have you ever been at a situation where you have been recently hired to maintain a legacy project, an important project at your company, but the previous team has long retired, and when you start, there is no documentation? When that happens, the old adage of "The code is the documentation" sounds true, but what happen when the code is also very old, hard to understand, and make use of libraries from when your parents were dating? In that case, using an AI Tool to help you understand how this project was created and maintained could be an option. Below are five prompt engineering techniques taken from a scientific article, with CLI based examples, to help you get the best out working with a legacy project. Zero-shot prompt A zero-shot prompt is when you make a prompt to request the model to execute a task without giving it any extra information or practical example in the input prompt. A classic example would be to ask a coding model to translate a "string.xml" file containing commonly used text strings in a natural language (for example, English) into another (for example, Spanish). Those are the easiest prompts to create and to feed to the model, but their results can be more unpredictable, since they usually lack the constraints of larger prompts. Prompt example: I want you to translate the contents of "string.xml" from English into Spanish and output it to me. Check this project files and directories, and provide me a list containing the current node version being used on this project and its libraries. Few-shot prompt In contrast to a zero-shot prompt, a few-shot prompt is a prompt including one or more pairs of the desired input and output, so the model can infer or mimic the desired output when you input the next value. A classic example would be asking a model for predictions based on a set of constraints. Those prompts tend to be longer and more complicated, and you must check the answer, because there is always a chance the model may infer incorrectly your

Rule of thumb : Secrets → Key Vault Configs → App Configuration Use both for secure + flexible configuration management. Azure Key Vault Purpose : Securely store sensitive information such as secrets, keys, and certificates. Use Cases : Store API keys, connection strings, tokens. Manage and rotate TLS/SSL certificates. Protect cryptographic keys used for encryption/decryption. Strengths : Built-in hardware security module (HSM) support. Access policies and RBAC for fine-grained control. Automatic secret rotation with some Azure services. Logging and monitoring via Azure Monitor. Limitations : Not designed for feature flags or configuration settings that change frequently. API calls can add latency if used excessively at runtime. Azure App Configuration Purpose : Centralized application configuration management . Use Cases : Store non-sensitive app settings (feature flags, UI options, app behavior). Versioned configurations and labels (per environment, per region). Enable dynamic configuration refresh in apps. Strengths : Feature flag management built-in. Supports key-value pairs with labels for environment separation. Integration with Azure Functions, App Service, AKS, and more. High availability and global distribution. Limitations : Not designed to store secrets or keys. Does not provide encryption key lifecycle management. When to Use Which Use Key Vault when : Handling secrets (DB passwords, API keys). Managing certificates and encryption keys. Need secure storage with strong access policies. Use App Configuration when : Handling app configs (feature flags, toggle dark mode, regional endpoints). Need dynamic refresh without redeployment. Want environment-based configuration with versioning. How They Work Together In most real-world solutions, you combine both : Use Azure App Configuration for general settings and feature management. Reference Azure Key Vault inside App Configuration for sensitive values. Example: AppConfig:DbConnectionString → points to Key Vaul

This article is part of the Comprehensive Guide to Microservices Architecture in .NET Core, Cloud and Azure series. API gateways serve as the entry point for client applications in distributed architectures, handling request routing, composition, and protocol translation. As systems grow more complex with multiple client types and backend services, choosing the right gateway pattern becomes crucial for maintaining performance, scalability, and developer productivity. This article explores two powerful API gateway patterns in .NET: the Backend for Frontend (BFF) pattern, which creates client-specific gateways, and GraphQL, which offers flexible, query-driven data fetching. Both patterns address the challenge of efficiently serving diverse clients while maintaining clean architecture and optimal performance. Backend for Frontend (BFF) Pattern Web BFF Implementation The web BFF returns detailed, enriched data suitable for desktop browsers with higher bandwidth and processing power: [ ApiController ] [ Route ( "api/web/[controller]" )] public class OrdersController : ControllerBase { private readonly IOrderServiceClient _orderClient ; private readonly ICustomerServiceClient _customerClient ; private readonly IInventoryServiceClient _inventoryClient ; [ HttpGet ( "{id}" )] public async Task < WebOrderDto > GetOrder ( Guid id ) { // Execute parallel calls to improve response time var orderTask = _orderClient . GetOrderAsync ( id ); var customerTask = _customerClient . GetCustomerAsync ( id ); var inventoryTask = _inventoryClient . GetInventoryStatusAsync ( id ); await Task . WhenAll ( orderTask , customerTask , inventoryTask ); return new WebOrderDto { Order = orderTask . Result , Customer = customerTask . Result , InventoryStatus = inventoryTask . Result , // Include additional rich data for enhanced web UI experience RecommendedProducts = await GetRecommendationsAsync ( id ) }; } } Mobile BFF Implementation The mobile BFF provides optimized, lightweight responses to min

7 daily crons, 2 starvation incidents that triggered the rewrite Health checks before work, not after, catch silent failures Queue-low alarm fires at 5 items, not at zero A cron is ignorable for 3 weeks only when failures are loud I run 7 GitHub Actions crons every day, and for two months I never looked at them. Then a content queue starved silently and I posted nothing for 4 days before noticing. Here is what I changed so a cron can stay green and be ignorable for 3 weeks straight. The Two Incidents That Forced The Rewrite The first starvation happened on a Tuesday. My image generation cron pulled prompts from a queue, made the assets, and pushed them to a publish queue. The image API returned a 429 (rate limited) and the job exited cleanly with a green checkmark. GitHub Actions reported success. The workflow logs said "0 prompts processed" in a line I never read. For 4 days the publish queue drained and nothing refilled it. I found out because a follower asked why I went quiet. The second incident was sneakier. A cron that calls an external API hit an auth token that had expired. The script caught the error, logged it, and returned exit code 0 because I had wrapped the whole thing in a try/except that swallowed everything. Green check, no work done. This one ran for 6 days before I caught it during an unrelated debug session. Both failures shared one root cause: a green checkmark in GitHub Actions means the process exited zero, not that the work happened. Those are completely different claims. A cron that catches its own errors and exits clean is lying to you in the most polite way possible. After the second incident I sat down and wrote out what I actually wanted. I wanted to never look at these workflows unless something was wrong. I wanted "wrong" to be loud. And I wanted the loudness to arrive before the damage, not after. That meant three changes. First, the exit code had to reflect real work, so swallowed exceptions had to re-raise or set a failure flag. Sec

Laravel API Development in Morocco: Architecture Guide 2026 Laravel remains the #1 PHP framework for API development in 2026 Laravel remains the #1 PHP framework for API development in 2026, and Morocco has become a hub for quality Laravel freelancers and teams. Here is the complete guide to building production-grade APIs with Laravel, based on 40+ projects shipped. Why Laravel for APIs in 2026 Eloquent ORM — most expressive DB layer in any framework Sanctum for SPA/mobile auth (simpler than Passport for most cases) Scout for Meilisearch / Algolia / Elastic full-text search Queues with Horizon for background jobs Octane for performance (Swoole / RoadRunner) Deep ecosystem : Telescope, Pulse, Forge, Vapor REST vs GraphQL — What to Choose Criteria REST GraphQL Learning curve Low Medium-high Caching Easy (HTTP) Complex Over-fetching Common Solved Mobile bandwidth Higher Optimized Best for Public APIs, simple CRUD Complex dashboards, mobile apps My default : REST with Laravel API Resources unless the client has clear GraphQL-specific needs (mobile app with variable fields, highly nested data). Standard Laravel API Architecture app/ ├── Http/ │ ├── Controllers/Api/V1/ │ ├── Requests/ (FormRequest for validation) │ └── Resources/ (API Resources for shaping output) ├── Models/ ├── Services/ (business logic) ├── Repositories/ (optional, if complex queries) ├── Jobs/ └── Events/ Key architectural decisions Versioning via URL (/api/v1/users) not headers — simpler FormRequest for validation (never validate in controller) API Resources for every response (shape, transforms, conditionals) Services layer when controllers exceed 100 lines Dedicated DTOs for complex payloads (spatie/laravel-data) Authentication — Sanctum Setup SPA on same domain : cookie-based, CSRF protected Mobile app / 3rd party : personal access tokens Revocation endpoint for logout Token abilities for granular permissions Rate Limiting & Security RateLimiter facade — per user, per IP, per endpoint CORS : use c

An innovative Moroccan platform for finding, renting, and selling real estate, offering a simple and seamless experience tailored to the local market. Challenge Launch Sarout.ma, an innovative Moroccan platform dedicated to searching, renting and selling real estate, on an ultra-competitive market dominated by a few historical players often criticized for dated ergonomics and uneven listing quality. The challenge: build an intuitive, modern real estate marketplace able to connect individual owners, agencies and tenants across all of Morocco — Casablanca, Rabat, Marrakech, Tangier, Agadir — with clear navigation and smart search. It also required enriched, geolocated listings updated in real time, and a journey differentiated by user profile (searcher, owner, professional agency). Solution Development of a site with a clean, fully responsive interface, designed mobile-first since most real estate searches in Morocco happen on smartphones. Integration of advanced dynamic filters (city, neighborhood, price, surface, number of rooms, property type, furnished/unfurnished) with instant result refresh. Listing management via a complete owner dashboard: creation, editing, view statistics, photo management with multi-upload and automatic compression, scheduling of paid promotions. Each property page has an SEO-optimized URL, rich descriptive content, precise geolocation on an interactive map, and the option to directly request a viewing. SEO architecture focused on local ranking: category pages per city and neighborhood, Schema.org RealEstateListing markup, dynamic sitemap. Email alert system for saved searches, listing moderation, and a professional agency dashboard for premium accounts. Results A high-performing, accessible real estate portal that significantly simplifies property search for individuals and strengthens listing visibility across Morocco. The interface fluidity stands out in a market where competition remains rough around the edges. Steady growth in publishe

Lab 1 – Linux Navigation & Files (20 points) Task 1 Create the following structure. /home/student/ ├── project │ ├── logs │ ├── scripts │ └── backup Requirements create all directories create app.log error.log install.sh inside correct folders. Task 2 Move app.log into backup Task 3 Delete error.log Task 4 Find install.sh using only one command. Lab 2 – Permissions (20 points) Create secret.txt Requirements Owner read/write Group read Others no access Verify permissions. Create a new user developer Switch to that user. Can the user read secret.txt Explain why. Lab 3 – Processes (20 points) Start a process Example sleep 500 Questions Find PID Kill the process Verify it stopped. Bonus Find the top five processes using the most memory. Lab 4 – Disk Space (20 points) Create a directory practice Generate a file about 100 MB. Questions How much disk space is used? How much free disk space remains? Which command shows directory size? Lab 5 – Networking (30 points) Find Your hostname private IP default gateway DNS server Ping 8.8.8.8 Questions Did it work? Ping google.com If this fails but 8.8.8.8 works, what is the problem? Display All listening ports. What service is listening on port 22? Lab 6 – Troubleshooting (40 points) Scenario 1 A user says "Internet is not working." Show the commands you would run. Expected ideas ip addr ping 8.8.8.8 ping google.com ip route cat /etc/resolv.conf Explain each step. Scenario 2 A website is down. Questions How do you verify server is running? nginx is running? port 80 is listening? firewall issue? logs? Commands should include systemctl status nginx journalctl -u nginx ss -tuln curl localhost systemctl restart nginx Scenario 3 SSH stopped working. How would you troubleshoot? Expected ping server ssh localhost systemctl status ssh ss -tulpn journalctl -xe systemctl restart ssh Lab 7 – Mixed Practical (Best Assessment) Tell students: You are the new Junior DevOps Engineer. The manager asks you to prepare a server. Complete all tasks. Ta

GitHub added an enterprise setting this week that looks like the kind of thing most developers will never read about unless it breaks their editor. Enterprise managed settings now support strictKnownMarketplaces for VS Code and GitHub Copilot CLI. In plain English: an organization can restrict which extension and plugin marketplaces are known and allowed inside the developer tools people actually use. That sounds like desktop management. I think it is more interesting than that. If coding agents can discover tools, install plugins, call commands, read repositories, modify files, and run workflows from the IDE or terminal, then plugin marketplace policy is no longer a minor preference. It is part of the runtime boundary. The agent does not only need permission to think. It needs permission to reach for tools. And the place where those tools come from is now a security surface. the tool catalog moved closer to the developer For a long time, extension marketplaces felt like productivity infrastructure. You installed a formatter, a theme, a language server, a test explorer, a Docker helper, a cloud plugin, a database browser, maybe three things you forgot existed. Some companies cared a lot. Many mostly hoped the endpoint security product would notice anything truly bad. That world was already risky, but the blast radius was usually framed around the human developer. A plugin could read files, run code, exfiltrate data, or weaken the local environment. Bad, but familiar. Agents change the framing. An AI coding assistant sitting in the IDE or CLI may use plugins as capabilities. It may call into developer tooling, use installed extensions as context, or depend on local integrations to perform work. Even when the agent itself does not directly install anything, the available tool environment shapes what it can do. So the question stops being "which extensions are developers allowed to install?" It becomes "which tool supply chains are allowed to become part of our automat

penAI reportedly plans to share its newest model, GPT 5.6, with a select group of partners instead of to the broader public. The reason: the Trump administration told it to.