How to speed up EF Core queries with BulkRead and WhereBulkContains

🔍 𝐓𝐡𝐞 𝐔𝐧𝐝𝐞𝐫𝐫𝐚𝐭𝐞𝐝 𝐋𝐈𝐍𝐐 𝐒𝐮𝐩𝐞𝐫𝐩𝐨𝐰𝐞𝐫: 𝐂𝐮𝐬𝐭𝐨𝐦 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐞𝐫𝐚𝐭𝐨𝐫𝐬 : 𝐖𝐫𝐢𝐭𝐞 𝐘𝐨𝐮𝐫 𝐎𝐰𝐧 𝐖𝐡𝐞𝐫𝐞(), 𝐒𝐞𝐥𝐞𝐜𝐭(), 𝐚𝐧𝐝 𝐁𝐞𝐲𝐨𝐧𝐝! Most developers use LINQ’s built-in operators like 𝗪𝗵𝗲𝗿𝗲, 𝗦𝗲𝗹𝗲𝗰𝘁, 𝗢𝗿𝗱𝗲𝗿𝗕𝘆, and 𝗚𝗿𝗼𝘂𝗽𝗕𝘆. But did you know you can actually 𝗰𝗿𝗲𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗟𝗜𝗡𝗤 𝗼𝗽𝗲𝗿𝗮𝘁𝗼𝗿𝘀, that integrate seamlessly with the LINQ syntax? 😎 This is called 𝗖𝘂𝘀𝘁𝗼𝗺 𝗟𝗜𝗡𝗤 𝗘𝘅𝘁𝗲𝗻𝘀𝗶𝗼𝗻 𝗠𝗲𝘁𝗵𝗼𝗱𝘀, and it’s one of the most powerful, yet least-known LINQ features. ⚙️ 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 ✅ Encourages 𝗱𝗼𝗺𝗮𝗶𝗻-𝗱𝗿𝗶𝘃𝗲𝗻 𝗱𝗲𝘀𝗶𝗴𝗻, code reads like natural language. ✅ Reduces duplication across large projects. ✅ Works across 𝗜𝗘𝗻𝘂𝗺𝗲𝗿𝗮𝗯𝗹𝗲 𝗮𝗻𝗱 𝗜𝗤𝘂𝗲𝗿𝘆𝗮𝗯𝗹𝗲 (EF Core queries too!). ✅ Keeps business logic cleanly encapsulated in reusable extensions. 🚀 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀  • Filtering only “in-stock” products: .AvailableProducts()  • Fetching “premium customers”: .PremiumMembers()  • Building reusable audit queries: .ModifiedAfter(DateTime.UtcNow.AddDays(-7)) 💬 𝗜𝗻 𝘀𝗵𝗼𝗿𝘁: You don’t have to stick with LINQ’s defaults. Create your own operators, and you’ll transform LINQ into a DSL (Domain-Specific Language) for your business logic. 💡 #DotNet #CSharp #Linq #DotNetCore #SoftwareEngineering #CleanCode #DotNetDeveloper #CodeOptimization #AspNetCore #BackendDevelopment #CodingTips #MicrosoftDeveloper #DotNetCommunity #ReusableCode

💻 Day 19 of #100DaysLearningChallenge by Saurabh Shukla Sir ✅ 📚 Learning Topic: Custom Set Data Structure in JavaScript (Using Map) 🧠 What I Learned: Today, I explored how to implement a Custom Set in JavaScript using the Map object. Unlike the built-in Set, this version provides more control and customization — allowing operations like union, intersection, and deletion through manual implementation. 🚀 💡 Concepts Covered: 👉 Understanding the difference between Set and Map 👉 Implementing core Set operations using Map 👉 Union and Intersection of Sets 👉 Checking membership (has()) and deletion (remove()) 👉 Exploring how keys and values can be used to ensure uniqueness ⚙️ Key Takeaways: ✅ Learned how Map can act as a base for custom data structures ✅ Reinforced concepts of data uniqueness and key-value storage ✅ Understood real-world use cases of Sets in data filtering and collection management ✅ Practiced implementing add(), remove(), has(), union(), and intersection() methods 💡 Concept Insight: Building data structures from scratch helps understand how JavaScript’s internals work — it’s not just coding, it’s crafting logic! 💪 🌐 GitHub: https://lnkd.in/gvRrrQ49 #100DaysLearningChallenge #JavaScript #DSA #CustomSet #Map #ProblemSolving #CodingJourney #Developers #Programming #LearningEveryday #CodeEveryday

This JavaScript library solves a big pain for devs 🕰 It lets you create animated dashboard gauges with ease. Comes with tons of options and customizations to fit your data visualization needs perfectly. In case you don’t know, gauges are those speedometer-style indicators you often see in dashboards showing values like speed, performance, or progress. A simple yet powerful way to make your UIs more interactive and engaging :) Source 🔗: github . com/toorshia/justgage Hope this helps ✅️ Drop a Like if you found this post helpful! 👍 Follow Ram Maheshwari ♾️ for more 💎 #html #css #javascript #100daysofcode #webdevelopment #programming

𝐌𝐂𝐏 𝐏𝐨𝐢𝐧𝐭𝐞𝐫: 𝐕𝐢𝐬𝐮𝐚𝐥𝐥𝐲 𝐒𝐞𝐥𝐞𝐜𝐭 𝐃𝐎𝐌 𝐄𝐥𝐞𝐦𝐞𝐧𝐭𝐬 𝐚𝐧𝐝 𝐒𝐞𝐧𝐝 𝐓𝐡𝐞𝐦 𝐭𝐨 𝐂𝐥𝐚𝐮𝐝𝐞 𝐂𝐨𝐝𝐞 Ever wished you could just point at a webpage element and instantly get AI help with it? MCP Pointer makes this a reality by bridging your browser with AI coding assistants through the Model Context Protocol. This innovative tool combines an MCP Server (Node.js) with a Chrome Extension, letting you visually select DOM elements and make them available to tools like Claude Code, Cursor, and Windsurf. 🔥 Key Features: ✨ Visual element selection with Option+Click in any browser ✨ Complete element data including text, CSS classes, attributes, and positioning ✨ React component detection with source file mapping (experimental) ✨ Real-time WebSocket connection between browser and AI tools ✨ Full MCP compatibility for seamless integration How simple is it? Hold Option (Alt on Windows) and click any element on a webpage. The extension captures all relevant data and sends it to your AI coding tool via the MCP server. The best part? It's 100% Open Source. This is the kind of workflow improvement that saves hours every week. Instead of manually describing elements or copying HTML, you can now just point and get instant AI assistance. 💭 How do you currently handle DOM element analysis in your development workflow? 🔗 Link to the GitHub repo in comments! #WebDevelopment #AI #MachineLearning #OpenSource #DeveloperTools #MCP #ClaudeCode #Programming #JavaScript #React #DOMManipulation

A while ago, I kept seeing [Owned] in Entity Framework Core and honestly… I had no idea what it actually did. 😅 After digging in, I realized it’s one of those small EF Core features that can make your domain models so much cleaner — especially if you care about design and maintainability. I wrote a short Medium article explaining [Owned] in plain English, with examples you can relate to. If you’ve ever worked with EF Core or value objects, you’ll enjoy this one 👇

📈 Day 169 of #200DaysOfCode Today, I worked on filtering numbers based on a condition — extracting values greater than 50 from a list in JavaScript. Instead of using .filter(), I implemented the logic manually to stay grounded in the fundamentals of: • Looping through arrays • Conditional checking • Selective data extraction • Handling scenarios where no valid values exist 🌍 This simple concept has strong real-world relevance: ✅ Data analysis ✅ Score/metric filtering ✅ Performance thresholds ✅ Analytics dashboards 💡 The more I revisit basics, the more I realize — logic is the real power behind every feature we build. Fundamentals are never outdated. They always scale with you. #JavaScript #169DaysOfCode #ProblemSolving #LearnInPublic #BackToBasics #WebDevelopment #LogicBuilding #CodingChallenge #DeveloperMindset

Rust Learning Series #9 – Markdown → HTML Converter I built a Markdown to HTML converter from scratch using Rust, including a trait-based rendering system for extensibility. What it does: - Converts markdown files to HTML - Supports headers (# through ######) - Handles inline formatting (bold, italic, code, links) - Groups list items into proper <ul> structures - Configurable with builder pattern - Extensible through trait system for multiple output formats Key Rust concepts I practiced: - Enums for representing document structure (AST) - Trait system for creating extensible rendering architecture - Module organization across multiple files (lib, parser, html, types) - Regular expressions with the regex crate - LazyLock for compile-time regex initialization - Builder pattern for ergonomic configuration - Integration testing with tempfile - Writing comprehensive doc comments What I learned: - Traits make code extensible without modifying existing implementations - LazyLock is perfect for expensive one-time initializations like regex compilation - Module organization matters for library APIs—re-exporting from lib creates clean interfaces - Regex has limitations in Rust (no lookahead/lookbehind), forcing simpler solutions - Builder pattern makes configuration intuitive and flexible - Integration tests with tempfile let you test complete workflows safely The big lesson: This was my first proper Rust library (not just a CLI tool). Designing for extensibility with traits while keeping the API simple taught me to think like a library author, not just an application developer. The trait system clicked for me here, it makes the code extensible, I can add new renderers (PDF, LaTeX) without modifying existing code. 👉 Full code: https://lnkd.in/dcVX5V_U #Rust #RustLang #LearningSeries

Day 5/365 — Why your task manager doesn't crash when you have 10,000 todos The secret? Heaps. 𝗧𝗵𝗲 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘆 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 Arrays force you to search through everything to find max priority: O(n). Heaps give you the highest priority item instantly: O(1). 𝗛𝗼𝘄 𝗜𝘁 𝗪𝗼𝗿𝗸𝘀 Heaps are complete binary trees with one rule: parent ≥ children (max-heap). Two operations maintain this: → Bubble up when adding (swap with parent if larger) → Bubble down when removing root (replace with last, swap with larger child) Both run in O(log n) time because the tree stays balanced. 𝗧𝗵𝗲 𝗖𝗹𝗲𝘃𝗲𝗿 𝗣𝗮𝗿𝘁 Heaps use arrays, not linked nodes. Parent at index i has children at 2i+1 and 2i+2. No pointers needed. 𝗪𝗵𝗮𝘁 𝗜 𝗕𝘂𝗶𝗹𝘁 JavaScript Heap class with insert, extractMax, and helper methods for bubbling operations. 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Heaps power priority queues in operating systems, Dijkstra's algorithm, and task schedulers. O(1) access to max + O(log n) updates = efficient priority management at scale. Ever wondered how your OS decides which process runs next? #datastructures #heap #priorityqueue #algorithms #javascript #dsa #webdevelopment #performance #learninginpublic #softwareengineering

🚀 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐖𝐞𝐛 𝐒𝐞𝐫𝐯𝐞𝐫𝐬 𝐢𝐧 𝐆𝐨 𝐰𝐢𝐭𝐡 𝐉𝐮𝐬𝐭 𝐭𝐡𝐞 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐋𝐢𝐛𝐫𝐚𝐫𝐲 One of the things I enjoy about Go is how much you can achieve without pulling in external frameworks. With just the native net/http package, you can spin up a fully functional web server in just a few lines of code: 𝗳𝘂𝗻𝗰 𝗵𝗮𝗻𝗱𝗹𝗲𝗿(𝘄 𝗵𝘁𝘁𝗽.𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲𝗪𝗿𝗶𝘁𝗲𝗿, 𝗿 *𝗵𝘁𝘁𝗽.𝗥𝗲𝗾𝘂𝗲𝘀𝘁) {   𝗳𝗺𝘁.𝗙𝗽𝗿𝗶𝗻𝘁𝗳(𝘄, "𝗛𝗲𝗹𝗹𝗼, 𝗚𝗼 𝗛𝗧𝗧𝗣!") } 𝗳𝘂𝗻𝗰 𝗺𝗮𝗶𝗻() {   𝗵𝘁𝘁𝗽.𝗛𝗮𝗻𝗱𝗹𝗲𝗙𝘂𝗻𝗰("/", 𝗵𝗮𝗻𝗱𝗹𝗲𝗿)   𝗵𝘁𝘁𝗽.𝗟𝗶𝘀𝘁𝗲𝗻𝗔𝗻𝗱𝗦𝗲𝗿𝘃𝗲(":𝟴𝟬𝟴𝟬", 𝗻𝗶𝗹) } That’s it: no heavy frameworks, no boilerplate, just clean and efficient code. Over the years, Go’s standard library has evolved to make this even better: - Strong support for concurrency with goroutines. - TLS, HTTP/2, and context handling built-in. - Robust ecosystem of middlewares you can add only if you need them. While frameworks can still provide useful abstractions, Go gives you the power to start simple, stay lightweight, and scale without hidden complexity. If you’re coming from languages where a framework is almost mandatory, it’s refreshing to see how Go empowers you to do more with less.

Generates Webs from Type Hints The Problem I Was Solving I run a laser cutting business. Every day I need to: Process images for engraving Generate reports from production data Share tools with my team \(who don't code\) I wanted something insane: Just type hints → complete web UI So I built it. After a month, I shipped FuncToWeb - a framework that auto-generates production-ready web interfaces from Python type hints. Zero HTML. Zero CSS. Zero JavaScript. from typing import Annotated from func\_to\_web import run from func\_to\_web.types import ImageFile from pydantic import Field def process\_image\( image: ImageFile, brightness: Annotated\[int, Field\(ge=0, le=100\)\] = 50, mode: Literal\["grayscale", "sepia", "blur"\] = "grayscale" \): # Your image processing logic here processed = apply\_filter\(image, brightness, mode\) return processed # PIL Image auto-displayed run\(process\_image\) What you get: ✅ Professional file upload with drag-and-drop ✅ Slider for brightness \(0-100, validated client + server\) https://lnkd.in/gTZejntw