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Note : This article is one of a series demonstrating building a React navigational component from scratch while considering accessibility through the process. The articles are accompanied by a GitHub repository with releases tied to one or more articles; each builds on the previous one until a fully implemented navigation component is complete. Each release and its associated tag contain fully runnable code for the article. The code discussed in this article is available in the release. and may be downloaded at release 0.9.0 . A page showcasing these base components may be run locally through this release. While code examples are written in JavaScript for brevity, all actual code is written in Typescript and targets React 19.x, all while using vanilla CSS. Examples use Next.js v16.x, which is not required to run the navigation component.] The design requirements for this release are available . -— Content Links Introduction Layout Appearance <nav / > Generic Styling Reimagining Focus and Hover Achieving Parity aria-current Refinements Summary Introduction With only one more release coming up and the horizontal layout styling complete, now is the time to focus on completing vertical layout styling. Most of the layout itself was completed in an earlier release and described in the article Laying it all out on the vertical . When styling a component, care must be taken to ensure parity with the information provided by screen readers through some aria attributes. For instance, a navigation component must set the aria-current="page" attribute on a link whose href points to the current page. In the example being shown, the link in question is the "by Era" link, under Find Your Next Story and Tales. ) While screen reader users hear which button or link is the item currently capturing focus, styling is needed to achieve the same for screen users. Visual styling is necessary to guarantee that visual and auditory/tactile users have the same information. Layout @layer system-c

Every World Cup there's a moment. Some player nobody outside their domestic league had heard of scores an absolute screamer in a knockout match, and by the time they've finished celebrating, their follower count is climbing like a rocket. I always found that fascinating, but I could never see it happening in real time. By the time the "X gained 3 million followers!" tweets show up, the surge is already over. So this tournament I built a little tracker that snapshots player follower counts on a schedule and shows me the growth curve as it happens. Here's how it works. Why the official APIs were a dead end My first instinct was to do this "properly" with official APIs. That died fast: Instagram's Graph API won't give you follower counts for accounts you don't own. TikTok's research API is academics-only and takes weeks of applications. X's API now starts at $100/month. I just wanted public follower counts — numbers anyone can see by opening the app. I didn't want a data partnership and a legal review. I ended up using SociaVault , which wraps the public profile data from each platform behind one API and one key. One request, one credit, JSON back. The shared client Everything runs through one tiny helper: // Node 18+ has fetch built in const API_KEY = process . env . SOCIAVAULT_API_KEY ; const BASE = " https://api.sociavault.com " ; async function sv ( path , params ) { const url = new URL ( BASE + path ); Object . entries ( params ). forEach (([ k , v ]) => url . searchParams . set ( k , v )); const res = await fetch ( url , { headers : { " X-API-Key " : API_KEY } }); if ( ! res . ok ) throw new Error ( ` ${ res . status } ${ await res . text ()} ` ); return res . json (); } Grabbing follower counts across platforms Each platform nests the count slightly differently, so I use fallback chains to stay defensive: async function instagramFollowers ( username ) { const data = await sv ( " /v1/scrape/instagram/profile " , { username }); const p = data . data ?. user ?? dat

My main work laptop is a Dell from 2017 with 8 GB of RAM. For weeks it had been crawling, freezing for whole seconds while I worked, and every so often it would simply switch itself off in the middle of a task. If you have ever lost unsaved work to a laptop that powers down on its own, you know exactly how frustrating that is. So I sat down and fixed it properly. The first thing I learned is worth saying up front: a slow, crashing laptop is usually two different problems wearing the same costume . Treat them as one and you will chase your tail. Separate them, and both become fixable. Everything below is free and copy-paste ready. It was tested on Ubuntu 24.04 LTS, and it applies to almost any older Linux machine. The honest disclaimer: Nobody can promise an old laptop will never lag. Software cannot add cores or memory that are not physically there. But you can absolutely stop the freezes and shutdowns completely and make everyday work feel smooth. That is the realistic, achievable goal. 0. Diagnose first, do not guess The biggest mistake is blindly applying "speed up Ubuntu" tweaks before knowing what is actually wrong. Spend five minutes measuring. Your lag has one of four common causes: heat, memory, disk, or a dying battery . Check temperature (the usual cause of random shutdowns): sudo apt install lm-sensors -y sudo sensors-detect --auto sensors Watch the Core temperatures while you work. If they spike past 90 to 100 °C right before a crash, you have a thermal problem, not a software one. Check memory (the usual cause of freezing): free -h sudo apt install htop -y htop In htop , watch the Mem and Swp bars during normal use. If memory pins near your limit and swap fills up, that thrashing is your freeze. Check disk space (a quiet killer): df -h / A root partition above 90% full makes Linux lag and turn unstable. Small SSDs fill up fast. Read the crash logs and battery health: # What went wrong during the previous (crashed) session journalctl -b -1 -p err --no-pa

Why a build log (and not another tutorial) Every AI shopping tutorial shows the same thing: install the SDK, call a tool, ship. None of them show what happens when the API returns a price that is stale , the merchant is geo-blocked , or the agent has to reconcile four different currencies for a single shopper query. This is the build log of a working cross-border shopping agent — what we shipped, what broke, and the patterns we now use on every customer integration. What we are building A conversational agent that takes a product brief ("noise-cancelling headphones under SGD 400") and returns the three cheapest matching offers across SG, US, and JP retailers in real time , with currency conversion and shipping transparency. It uses BuyWhere MCP as the price-discovery layer (5M+ products, 6M+ offers, 100+ merchants, 5 currency modes). The architecture, after three iterations v1 — naive : agent calls search_products , picks top 3, returns them. Failed because the agent had no idea which offers were in stock or shippable to the user. v2 — offer-aware : agent calls search_products (with mode=offer ), then calls get_product on each to pull current price + shipping. Failed because round-trips to get_product added 8–11s of uncached latency on the first request. v3 — multi-region, currency-normalised (the one that ships): search_products with mode=offer and a regional filter Filter offers by in-stock + ships_to=user_region in the agent prompt For cross-region results, call find_similar to get the same product in the local catalog and pick the cheaper of (local offer + shipping) vs (foreign offer + conversion + shipping) Return three results with a one-line rationale per choice The result is a 1.2–2.4s response time and a 73% click-through on the top result, measured over 1,800 shopper queries last week. Three patterns we use on every integration now 1. Always pass mode=offer for shopping tasks mode=product returns the canonical product card (good for browsing and category p

You open the "Updates" page in WordPress admin and see that Elementor Pro / ACF Pro / vk-blocks-pro have updates available. Then you run wp plugin update --all from your automation, and those exact plugins don't update. The asymmetry — "the admin sees it; WP-CLI doesn't" — traces back to how WordPress detects updates and how premium plugins layer proprietary update mechanisms on top of that. Here's the mechanism and how an automation tool can adapt. WordPress update detection is held by a transient cache WordPress doesn't decide "is there an update?" on every request. It caches the answer in the wp_options table as a transient, valid for up to 12 hours. The key entries: _site_transient_update_plugins — latest plugin versions _site_transient_update_themes — themes _site_transient_update_core — core If those transients are stale, even a version that just shipped on wordpress.org reads as "no update needed." WP-CLI consults the same transients, so stale cache = WP-CLI misses the update too . In one customer environment on ConoHa WING, we reproduced "version X is on wordpress.org, WP-CLI doesn't see it" directly. Trap 1 and fix 1 — force-refresh the transients before listing updates The first step was simple: at the start of a maintenance run, delete the three transients before querying the update list . def _flush_update_transients ( self , conn , wp_cmd , wp_path , site_name ): """ Delete update transients between DB backup and update step """ for t in ( " update_plugins " , " update_themes " , " update_core " ): conn . run ( f " { wp_cmd } transient delete { t } --path= { wp_path } " , warn = True , hide = True , ) Deleting the transients triggers a rebuild on the next wp plugin list --update=available . During the rebuild, WordPress reaches out to wordpress.org, so the returned list reflects the current release state . That handled the "we were just reading a stale cache" cases. But it didn't help with premium plugins. Trap 2 — with --skip-plugins , Pro updater filt

আমার colleague এখন প্ল্যান দেখতে পারছে। Scan types বুঝতে পারছে। Join types বুঝতে পারছে। Estimate আর actual এর gap দেখতে পারছে। BUFFERS ও দেখছে। কিন্তু সে প্রশ্ন করল। এসব দেখে কি করব? Step by step কোন পথে যাব? আমি বললাম। পাঁচটা step আছে। অর্ডার অনুযায়ী। পর্ব ২ এ আমি বলেছিলাম scan types, join types, estimate আর actual এর gap। BUFFERS কি। এবার আসি সমাধান এ। Diagnostic Workflow আপনার কাছে একটা slow query এসেছে। কিভাবে debug করবেন? এই পাঁচটা প্রশ্ন করুন অর্ডার অনুযায়ী। ৯০% slow query প্রথম বা দ্বিতীয় ধাপেই solve হয়ে যায়। ১. Deepest Seq Scan দেখুন Table বড় কি না? Filter selective কি না? Missing index থাকলে add করুন। আজই শুরু করুন যখন একটা Seq Scan দেখবেন big table এ, প্রথমে WHERE clause টা check করুন। Selective কি না? ৫% এর কম row return হওয়ার কথা? যদি তাই হয়, index missing। CREATE INDEX idx_name ON table(column) run করুন। ২. Join types দেখুন কোনো Nested Loop আছে কিন্তু দুই পাশেই বড় table? Hash Join force করুন বা ডান পাশে index add করুন। আজই শুরু করুন Nested Loop দেখলে ডান পাশের table এ index check করুন। যদি না থাকে, create করুন। Index থাকা সত্ত্বেও planner Nested Loop use করছে? SET enable_nestloop = off temporarily disable করে দেখুন। Hash Join আসবে কি না। ৩. Row estimates দেখুন Estimate vs actual ১০x এর বেশি difference? ANALYZE table দিন বা predicate rewrite করুন। আজই শুরু করুন rows=1 estimate কিন্তু rows=100000 actual দেখলে ANALYZE tablename run করুন। Statistics refresh হবে। তারপর plan আবার দেখুন। যদি তাও না আসে, WHERE clause rewrite করুন। Function call থাকলে remove করুন। Type mismatch থাকলে fix করুন। ৪. BUFFERS add করুন কোনো node এ অনেক disk reads? Caching investigate করুন। আজই শুরু করুন EXPLAIN (ANALYZE, BUFFERS) run করে দেখুন shared read high কোথায়। সেই node টাই bottleneck। Index add করলে reads কমবে। Pre-warm cache করতে পারেন। Data pre-load করতে পারেন। ৫. Sorts আর hashes দেখুন কোনো spill-to-disk আছে? work_mem raise করুন বা sort eliminate করুন। আজই শুরু করুন Plan এ external merge Disk: 421MB দেখলে spill-to-disk হয়েছে। SET work_mem = '256MB' temporarily rais

I joined a team where the controller was 800 lines long, the business rules were scattered between the controller and the DbContext , and "to run the tests, spin up a SQL Server in Docker" was a sentence I heard every week. The fix was Clean Architecture. The argument I had with the team lead was about how to actually structure it. We argued for two weeks. Then I built this template so the next person wouldn't have to. This is the Clean Architecture .NET 8 starter template I wish someone had handed me on day one. Four projects, strict dependency direction, domain entities that own their own invariants, and an Application layer you can unit test with Moq — no database required. The whole repo is on GitHub , MIT-licensed, runs with dotnet run , and ships with xUnit tests, JWT auth, Swagger, Docker, and CI. This post is the explanation of why each project exists, what goes in it, and what I learned the hard way about getting Clean Architecture right in .NET. The problem Clean Architecture solves The naive way to build a .NET Web API is one project, one folder structure, and "everything talks to everything": MyApp/ Controllers/ ProductsController.cs ← HTTP stuff OrdersController.cs ← HTTP stuff + business rules Services/ ProductService.cs ← business rules + DbContext.SaveChanges Data/ AppDbContext.cs ← EF Core, entities Models/ Product.cs ← POCO with public setters This works for the first 1,000 lines. By 5,000 lines, the controller is doing five things at once. By 10,000, "to test this, I need a database" is the answer to every test question, and your CI takes 20 minutes because every test run spins up SQL Server. Clean Architecture says: separate the business rules from the HTTP boundary, separate the database from the business rules, and enforce it with project references. A controller is allowed to call a service. A service is allowed to call a repository. A repository is allowed to know about EF Core. Nothing is allowed to know about anything "above" it in the chai

Originally published at htpbe.tech . The version on htpbe.tech stays in sync with the latest detection algorithm — refer to it for the canonical text. Accounts payable teams are receiving receipts generated by ChatGPT plugins. HR platforms are seeing payslips rendered by Python scripts. Insurance claims contain repair estimates that no shop ever issued. The documents look correct. The logos match. The numbers are plausible. The question is: what can actually be detected, and what cannot? The honest answer requires separating two things that are often confused under the phrase “AI-generated document detection.” Two distinct problems called "AI-generated document detection" When people ask how to detect an AI-generated document, they usually mean one of two distinct things: Content classification asks: was the text in this document written by an AI language model? This is what tools like GPTZero and Turnitin’s AI detector do. They analyze writing style, token probability distributions, and linguistic patterns to estimate whether a human or a model produced the text. Structural forensics asks: was this PDF file generated by a real institutional system, or did it come from a headless browser, a PDF library, or a consumer tool? This is what HTPBE does. It reads the binary structure of the file — producer metadata, xref patterns, font embedding, object numbering — and checks whether those patterns match how legitimate institutional software generates documents. These are not the same problem. A document can contain AI-written text and still come from a real corporate system. A document can contain entirely human-written text and still have been rendered by Puppeteer an hour ago. The structural check and the content check answer different questions. HTPBE does structural forensics. It does not classify text. This article explains what that distinction means in practice, what the structural approach reliably catches, and where its limits are. What structural forensics detec

1. Introduction Django's ORM is one of its greatest strengths. It abstracts away raw SQL, lets you express database operations in clean Python, and gets you productive fast. But that convenience comes with a hidden cost: if you're not deliberate about how you fetch related objects, you'll silently generate far more queries than you intend — and you won't notice until your app slows to a crawl in production. The most common culprit is the N+1 query problem : a pattern where fetching a list of N objects triggers an additional query for each one, resulting in N+1 total round-trips to the database. At ten rows it's invisible. At ten thousand rows, it's a disaster. Django provides two tools to fix this: select_related and prefetch_related . This article explains how each one works internally, when to use which, and how to combine them effectively — with before/after examples and real query counts throughout. 2. Understanding the N+1 Problem Consider a simple blog with posts and authors. You want to render a list of posts, showing each post's title and its author's name. Models: # models.py from django.db import models class Author ( models . Model ): name : str = models . CharField ( max_length = 100 ) class Post ( models . Model ): title : " str = models.CharField(max_length=200) " author : Author = models . ForeignKey ( Author , on_delete = models . CASCADE , related_name = " posts " , ) The naive approach: # views.py from django.db import connection from .models import Post def list_posts () -> None : posts = Post . objects . all () # Query 1: fetch all posts for post in posts : print ( f " { post . title } by { post . author . name } " ) # ^^^ Query 2, 3, 4, ... N+1: one per post For 100 posts, this produces 101 queries . Django lazily fetches post.author the first time you access it on each object. Each access hits the database separately. You can verify this with django.db.connection.queries (requires DEBUG = True ): from django.db import connection , reset_queries

Whether you're brand new to Power BI or just getting started with data analytics, this guide walks you through everything you need to know about data modeling — from how tables connect, to the schemas that make your reports fast and reliable. What Is Data Modeling in Power BI? Imagine you have three spreadsheets: one with your customers , one with your products , and one with your sales transactions . Individually, each table tells you something. But together, they can tell you which customer bought which product, when, and for how much . That's exactly what data modeling is: the process of organizing your data tables and defining how they relate to each other so Power BI can combine them into meaningful reports and dashboards. A data model in Power BI has three core building blocks: Tables — your data sources (Excel files, databases, CSVs, cloud services, etc.) Relationships — the links between tables that tell Power BI how data connects Measures & Calculations — formulas (in DAX) that compute totals, averages, and other insights A well-designed data model is the difference between a report that loads in seconds and one that takes forever. It's also what keeps your numbers accurate and your dashboards easy to use. Why Does Data Modeling Matter? Here's a simple analogy: a city without roads is just a collection of buildings. Data modeling is the road system that lets you travel between your tables. Without a good data model: Your visuals may show incorrect or duplicated numbers Filters in one chart won't affect another Reports will be slow and hard to maintain With a good data model: Clicking on a customer in one visual automatically filters every other visual Calculations are accurate and consistent You can easily add new data sources without rebuilding everything Types of Tables: Facts vs. Dimensions Before diving into schemas and relationships, you need to understand the two types of tables that make up most Power BI models. Fact Tables A Fact table stores the ev

Learn how to organize React projects for scalability, maintainability, and team collaboration. Introduction As React applications grow, one challenge consistently emerges: project organization . A folder structure that works perfectly for a small side project can quickly become a nightmare when multiple developers contribute to a production-scale application. Whether you're a junior React developer preparing for interviews, a mid-level frontend engineer looking to improve code maintainability, or a technical recruiter trying to understand modern React development practices, understanding scalable React folder structures is essential. In this guide, we'll explore the most popular React folder organization strategies, their pros and cons, and the structure many modern engineering teams use to build scalable applications. Why React Folder Structure Matters A well-organized React project provides several benefits: Faster onboarding for new developers Easier code maintenance Better scalability as features grow Reduced code duplication Improved team collaboration Cleaner separation of concerns Many React applications start simple: src/ ├── App.jsx ├── Home.jsx ├── Login.jsx └── Dashboard.jsx This works initially, but as the project grows to dozens or hundreds of components, finding and maintaining files becomes increasingly difficult. Common React Folder Structure Approaches 1. Type-Based Folder Structure One of the earliest and most common approaches is organizing files by their type. src/ ├── components/ ├── pages/ ├── hooks/ ├── services/ ├── utils/ ├── assets/ └── contexts/ Advantages Easy to understand Suitable for small projects Quick setup Disadvantages Components folder can become enormous Related files are spread across multiple directories Harder to maintain in large teams For example, a user profile feature might have files located in: components/UserProfile.jsx hooks/useUser.js services/userService.js pages/ProfilePage.jsx Finding all files related to one feat

A great CLAUDE.md is not the longest one. It is the one where every line changes what Claude does. The whole skill is knowing what belongs in it — and, just as importantly, what does not. The sections that earn their place Start with a one or two line project description and your stack, with version numbers. Claude infers a lot from your code, but it will not guess that you are on Next.js 15 instead of 14, or which ORM you chose. Then a directory map — not every file, just the top-level layout with a note on what each part holds. After that: the build and test commands, the conventions a formatter does not enforce, and critically, the things not to touch. # Project: Acme Dashboard Next.js 15 (App Router), TypeScript, Drizzle ORM, Vitest. ## Structure src/app/ # routes and pages src/lib/ # shared utilities, db client db/migrations/ # generated - never hand-edit ## Commands Build: npm run build Test: npm run test ## Conventions - API routes return { data, error } - never throw to client - Server components by default ## Do not touch - db/migrations/ is generated. Never edit by hand. Every line in that file would cause a mistake if removed. That is the bar. What to leave out This is where most files go wrong. Two kinds of content waste your budget: Personality instructions. "Act as a senior engineer," "think step by step," "be thorough." These feel productive but change nothing — Claude already does them. General advice that does not prevent a specific mistake is pure noise. Rules a tool already enforces. If you have a formatter or linter, do not restate what it enforces. Wire it into a hook instead, and keep CLAUDE.md for what tools cannot enforce. The one-line test For every line, ask: "If I remove this, will Claude make a mistake?" If yes, keep it. If no, delete it. This single question, applied ruthlessly, is the difference between a file Claude follows and one it ignores. A bloated file buries the rules that matter in noise, so Claude cannot tell which line is the

If you use Claude Code, there is one file that quietly shapes every session: CLAUDE.md. Most developers either do not have one or have one that works against them. Here is what it actually is, in plain terms. The file Claude reads every session CLAUDE.md is a markdown file that Claude Code reads at the start of every conversation. Think of it as your project's constitution — the source of truth for how your specific repository works. Because Claude reads it every time, you stop re-explaining your stack, your conventions, and your commands on every task. Why it exists Without a CLAUDE.md, every session starts cold. Claude can read your code, but it cannot infer the things that live outside the code: that you are on Next.js 15 and not 14, that a directory is generated and must never be edited, that your team has a particular commit style. You end up explaining these again and again, slightly differently each time, so the output drifts. CLAUDE.md captures that knowledge once, somewhere Claude always sees it. Where it lives, and how to start Put CLAUDE.md in the root of your project. You do not have to write it from a blank page — the /init command analyses your codebase and generates a starter, detecting your build tools, test framework, and existing patterns: $ claude > /init Treat the result as a foundation, not a finished product. The real value comes from refining it as you learn what Claude gets wrong without guidance. What belongs in it A good CLAUDE.md is short and specific: A one-line stack description, with versions — Claude will not guess Next.js 15 over 14 A directory map — the top-level layout and what each part holds The build and test commands The conventions a newcomer could not infer from the code A "do not touch" section — generated files, migrations, protected paths Here is a compact example: # Project: Acme Dashboard Next.js 15 (App Router), TypeScript, Drizzle ORM, Vitest. ## Structure src/app/ # routes and pages db/migrations/ # generated - never h

You wrote a detailed CLAUDE.md, and Claude Code still gets things wrong — wrong convention, touches files it should not, ignores rules you clearly wrote down. The cause is almost never that the rules are missing. It is that they are buried. The over-specified file problem CLAUDE.md loads into Claude's context every single session, and performance degrades as that context fills. When the file grows too long, something counterintuitive happens: Claude starts ignoring parts of it. The important rules get lost in the noise, and the genuinely critical instructions sit too deep to reliably influence output. A bloated file does not just waste tokens. It actively makes Claude less reliable, because it cannot tell which of your hundred lines is the one that matters. The trap of good intentions It always starts reasonably: "let me put everything relevant in here." But relevant is a low bar. The file grows until it is impossible to scan, full of duplication, and so noisy that the truly important rules carry no weight. More content felt like more control. It was the opposite. The fix: prune ruthlessly Run every line through one question: "If I remove this, will Claude make a mistake?" If the answer is no, the line is noise — delete it. And if something only matters in a specific situation rather than always, it does not belong in the always-loaded file at all. That is what skills and subdirectory CLAUDE.md files are for — they load on demand, only when relevant. Let Claude fetch what it needs Instead of embedding everything, tell Claude how to pull context when it needs it. Rather than pasting an entire API guide into the file: # Wasteful - embeds the whole file every session: @docs/api-guide.md # Better - Claude reads it only when relevant: For Stripe integration work, read docs/stripe-guide.md The second form costs almost nothing until the moment it is needed. The result A pruned CLAUDE.md is often a third of the length and many times more effective. The rules that matter are

Gelişmiş Veri İşleme (Python) Sıralama, Filtreleme ve Arama – Profesyonel Veri Manipülasyonu Rehberi Python’da veri işleme, sadece döngülerden ibaret değildir. Modern Python yaklaşımı; fonksiyonel programlama araçları , yüksek seviyeli built-in fonksiyonlar ve lambda ifadeleri ile daha kısa, daha okunabilir ve daha performanslı çözümler üretmeyi hedefler. Bu bölümde dört kritik alanı derinlemesine inceleyeceğiz: sorted() ile gelişmiş sıralama lambda ile karmaşık veri yapıları üzerinde sıralama filter() ve map() ile fonksiyonel veri dönüşümü any() ve all() ile toplu doğrulama (validation) Her bölümde gerçek dünya senaryoları ve hands-on örnekler olacak. 1. sorted() Fonksiyonu — Gelişmiş Sıralama Motoru 1.1 Temel Yapı sorted ( iterable , key = None , reverse = False ) Parametreler: iterable: Liste, tuple, set vb. key: Sıralama kriteri (fonksiyon) reverse: True → büyükten küçüğe 1.2 Basit Sıralama ```python id="s1" sayilar = [5, 1, 9, 3, 7] sonuc = sorted(sayilar) print(sonuc) --- ## 1.3 Ters Sıralama ```python id="s2" sayilar = [5, 1, 9, 3, 7] print(sorted(sayilar, reverse=True)) 1.4 Tuple Sıralama ```python id="s3" veri = (10, 5, 20, 15) print(sorted(veri)) --- ## 1.5 String Sıralama (ASCII mantığı) ```python id="s4" kelimeler = ["python", "ai", "data", "backend"] print(sorted(kelimeler)) 2. key Parametresi — Sıralamanın Beyni sorted() fonksiyonunun gerçek gücü burada başlar. 2.1 String Uzunluğuna Göre Sıralama ```python id="k1" kelimeler = ["python", "ai", "veri", "makineöğrenmesi"] sonuc = sorted(kelimeler, key=len) print(sonuc) --- ## 2.2 Sayıların Moduna Göre Sıralama ```python id="k2" sayilar = [10, 3, 7, 21, 14, 9] sonuc = sorted(sayilar, key=lambda x: x % 5) print(sonuc) 2.3 Tuple Sıralama (Gerçek Dünya) ```python id="k3" urunler = [ ("Laptop", 45000), ("Mouse", 500), ("Monitör", 12000) ] sonuc = sorted(urunler, key=lambda x: x[1]) print(sonuc) --- ## 2.4 Çok Katmanlı Sıralama Fiyat → sonra isim ```python id="k4" urunler = [ ("Laptop", 45000), ("Mouse", 500),

In the earlier posts of this series, we looked at practical query tuning tips and how to read and interpret query plans . A recurring theme in both was: "add an index here." But "add an index" is a bit like saying "use the right tool" — the interesting part is which one. PostgreSQL ships with several index types, each tuned for a different kind of data and query. Picking the wrong one means PostgreSQL quietly ignores your index and goes back to a sequential scan. In this post, we'll walk through the main index types, when each shines, and the special index variations (composite, partial, covering, expression) that often matter more than the type itself. Setting the Scene: Schema and Sample Data We'll reuse the same schema from the previous posts, with one small addition — a metadata JSONB column and a tags array on orders , so we can explore the more exotic index types. CREATE TABLE customers ( id SERIAL PRIMARY KEY , customer_name VARCHAR ( 255 ), email VARCHAR ( 255 ), created_at TIMESTAMPTZ DEFAULT NOW () ); CREATE TABLE orders ( id SERIAL PRIMARY KEY , customer_id INT REFERENCES customers ( id ), order_date TIMESTAMPTZ DEFAULT NOW (), total_amount NUMERIC ( 10 , 2 ), status VARCHAR ( 20 ), tags TEXT [], metadata JSONB ); -- Insert sample customers INSERT INTO customers ( customer_name , email ) SELECT 'Customer ' || i , 'customer' || i || '@example.com' FROM generate_series ( 1 , 1000000 ) AS s ( i ); -- Insert sample orders INSERT INTO orders ( customer_id , order_date , total_amount , status , tags , metadata ) SELECT ( RANDOM () * 1000000 ):: INT , NOW () - interval '1 day' * ( RANDOM () * 365 ):: int , ( RANDOM () * 500 + 20 ), ( ARRAY [ 'pending' , 'shipped' , 'delivered' , 'cancelled' ])[ FLOOR ( RANDOM () * 4 + 1 )], ARRAY [( ARRAY [ 'gift' , 'priority' , 'fragile' , 'bulk' ])[ FLOOR ( RANDOM () * 4 + 1 )]], jsonb_build_object ( 'channel' , ( ARRAY [ 'web' , 'mobile' , 'store' ])[ FLOOR ( RANDOM () * 3 + 1 )]) FROM generate_series ( 1 , 1000000 ) AS s ( i

Python provides socket.AF_UNIX , asyncio.open_unix_connection() , and asyncio.start_unix_server() for working with Unix Domain Sockets on Unix-like operating systems. On Windows, however, support for Unix Domain Sockets tends to depend on the Python version and runtime environment. In particular, differences become apparent when trying to use the higher-level asyncio APIs in the same way as on Unix. To address this, I created a compatibility layer that hides the differences between Unix and Windows and allows AF_UNIX sockets to be used through a largely identical API. This article covers two types of APIs: An asyncio -based AF_UNIX compatibility API A synchronous socket -based AF_UNIX compatibility API Goal The objective is straightforward. On Unix, use the standard library APIs as-is. On Windows, fill in the missing functionality so that application code can remain as unified as possible. For example, on Unix you can write: reader , writer = await asyncio . open_unix_connection ( path ) And on the server side: server = await asyncio . start_unix_server ( handle_client , path ) The goal is to preserve this style of programming on Windows as much as possible. What Was Built The compatibility layer consists of two major components. 1. Asyncio Version This is the asynchronous implementation designed to match the asyncio Unix Domain Socket APIs. The main APIs are: await open_unix_connection ( path , * , limit = ...) await start_unix_server ( callback , path , * , limit = ..., backlog = ...) await create_unix_connection ( protocol_factory , path , ...) await create_unix_server ( protocol_factory , path , ...) install () On Unix-like systems, these simply delegate to the standard asyncio implementation. On Windows, they use Winsock AF_UNIX sockets and combine WSAEventSelect with event-loop handle waiting to implement asynchronous operations. 2. Synchronous Socket Version This version provides a traditional blocking-socket-style API without using asyncio . The main APIs ar

After spending two weeks debugging issues that only showed up in production — a sitemap _redirects rule that was blocking my own sitemap-index.xml and a Bluesky image upload race against Cloudflare Pages deploy lag — I added three post-deploy checks to my workflow. They're fast and specific to the failure modes I've actually hit, not a full end-to-end test suite. Three sites (aiappdex.com, findindiegame.com, ossfind.com) on Cloudflare Pages with Astro 5 SSG. Here's what I check. Check 1: Sitemap reachability The simplest check and the one I should have had from day one. After a Cloudflare Pages deploy, I verify that sitemap-index.xml is reachable and returning 200 on all three domains: for domain in aiappdex.com findindiegame.com ossfind.com ; do status = $( curl -s -o /dev/null -w "%{http_code}" "https:// $domain /sitemap-index.xml" ) echo " $domain /sitemap-index.xml → $status " if [ " $status " != "200" ] ; then echo "FAIL: $domain sitemap unreachable" fi done I also check sitemap-0.xml — the actual URL sub-sitemap that @astrojs/sitemap generates — and assert that it contains at least a minimum expected URL count. For aiappdex.com that threshold is 1,000; if it drops below that after a deploy, the ETL data pipeline probably broke silently. The reason this check exists: I had a _redirects rule rewriting sitemap-index.xml → sitemap-0.xml as an emergency workaround that turned out to be wrong. It was live for five days before I found it. The rule was blocking the real sitemap-index.xml from reaching crawlers while appearing fine in the browser (which followed the redirect). Curl with -o /dev/null -w "%{http_code}" doesn't follow redirects by default, so it would have caught this immediately. Check 2: IndexNow batch submission After every successful sitemap check, I run node scripts/indexnow.mjs . The script reads the live sitemap XML from each domain, collects all URLs, and POSTs them to the IndexNow endpoint for Bing, Yandex, Naver, and Seznam using site-specific k

After spending two weeks debugging issues that only showed up in production — a sitemap _redirects rule that was blocking my own sitemap-index.xml and a Bluesky image upload race against Cloudflare Pages deploy lag — I added three post-deploy checks to my workflow. They're fast and specific to the failure modes I've actually hit, not a full end-to-end test suite. Three sites (aiappdex.com, findindiegame.com, ossfind.com) on Cloudflare Pages with Astro 5 SSG. Here's what I check. Check 1: Sitemap reachability The simplest check and the one I should have had from day one. After a Cloudflare Pages deploy, I verify that sitemap-index.xml is reachable and returning 200 on all three domains: for domain in aiappdex.com findindiegame.com ossfind.com ; do status = $( curl -s -o /dev/null -w "%{http_code}" "https:// $domain /sitemap-index.xml" ) echo " $domain /sitemap-index.xml → $status " if [ " $status " != "200" ] ; then echo "FAIL: $domain sitemap unreachable" fi done I also check sitemap-0.xml — the actual URL sub-sitemap that @astrojs/sitemap generates — and assert that it contains at least a minimum expected URL count. For aiappdex.com that threshold is 1,000; if it drops below that after a deploy, the ETL data pipeline probably broke silently. The reason this check exists: I had a _redirects rule rewriting sitemap-index.xml → sitemap-0.xml as an emergency workaround that turned out to be wrong. It was live for five days before I found it. The rule was blocking the real sitemap-index.xml from reaching crawlers while appearing fine in the browser (which followed the redirect). Curl with -o /dev/null -w "%{http_code}" doesn't follow redirects by default, so it would have caught this immediately. Check 2: IndexNow batch submission After every successful sitemap check, I run node scripts/indexnow.mjs . The script reads the live sitemap XML from each domain, collects all URLs, and POSTs them to the IndexNow endpoint for Bing, Yandex, Naver, and Seznam using site-specific k

Pernahkah kamu membuat sebuah proyek perangkat lunak, mengunggahnya ke GitHub, lalu menyadari bahwa kamu belum menambahkan lisensi apa pun di repositori tersebut? Banyak developer pemula yang mengira bahwa menaruh kode di GitHub otomatis membuatnya menjadi open-source . Padahal, secara default , proyek tanpa fail lisensi memiliki hak cipta yang tertutup ( exclusive copyright ). Artinya, orang lain atau developer penerus secara teknis tidak boleh menyalin, mendistribusikan, atau memodifikasi kodemu. Agar proyek tersebut aman untuk dilanjutkan dan dimodifikasi oleh pengembang selanjutnya, kita wajib menambahkan lisensi terbuka. MIT License adalah pilihan paling aman dan populer karena sifatnya yang sangat membebaskan. Berikut adalah cara kilat menyematkan MIT License pada repositori GitHub yang sudah telanjur berjalan tanpa perlu menggunakan command line : Langkah 1: Buat Fail Baru di Repositori Buka halaman utama repositori GitHub kamu. Di bagian atas daftar fail dan folder kodemu, klik tombol Add file , kemudian pilih Create new file . Langkah 2: Pancing Fitur "License Template" Pada kolom pengisian nama fail, ketikkan kata LICENSE (pastikan menggunakan huruf kapital semua). Begitu kamu selesai mengetikkan kata tersebut, GitHub akan otomatis memunculkan sebuah tombol baru di sebelah kanan bernama Choose a license template . Klik tombol tersebut. Langkah 3: Pilih MIT License Kamu akan dibawa ke halaman yang berisi daftar berbagai jenis lisensi open-source . Pilih MIT License dari menu di sebelah kiri. GitHub akan otomatis meracik draf teks lisensinya, lengkap dengan nama akun GitHub kamu dan tahun saat ini. Klik tombol hijau Review and submit di pojok kanan atas. Langkah 4: Lakukan Commit Gulir ke bagian bawah halaman. Tulis pesan commit yang singkat dan jelas (misalnya: "Add MIT License for future development" ), lalu klik tombol hijau Commit changes... . Selesai! Sekarang proyek lama kamu sudah memiliki "payung" yang jelas dan resmi berstatus open-source . Reposito