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CSS `:nth-child` Selectors Cheatsheet

Here are a few of my follow recommendations: CSS: @Shefali__J Open Source: @orcDev DevRel: @FrancescoCiull4 Side Projects: @csaba_kissi Nomading: @razvanmuntian WordPress/SEO: @natmiletic Testing and Clean Code: @dmokafa Software Engineering: @mjovanovictech Thank me later!

I’ve worked on enough apps to know that search is usually one of those features that starts simple and gets complicated fast. At first, a basic keyword search feels fine. Then users start searching the way real people actually think: “Show me the most powerful car you have” “High performance Italian cars above 700hp” “A Honda or BMW with at least 200hp, rear-wheel drive, from 20K to 50K” And suddenly your search logic turns into a mix of filters, conditions, parsing rules, aliases, and edge cases. That’s why @typesense caught my attention. Typesense is an open-source search engine built for modern app and site search, and their built-in Natural Language Search support in v29 is especially interesting. Instead of forcing users to search with exact keywords, Typesense can translate plain English queries into structured search filters and sorting logic automatically using your preferred LLM provider. So instead of manually handling queries like: “A Honda or BMW with at least 200hp, rear-wheel drive, from 20K to 50K” Typesense can understand: • car brands • horsepower requirements • drivetrain preferences • price ranges • sorting intent without needing to build complex parsing logic on the application side. It’s a much cleaner way to build intelligent search experiences. Other things I like about it: • Typo tolerance out of the box • Fast search-as-you-type performance • Simple relevance tuning • Laravel Scout and Django integrations • Minimal setup for production-ready search If you’re building search into a SaaS product, ecommerce app, docs site, or internal tool, it’s worth checking out. GitHub Repo: fandf.co/4nmFHME – Thanks to @typesense for sponsoring this post!

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Most developers optimize for addition, meaning new features, extended functionality, and growing systems. Good code is easy to remove. This is such a great tip! 🧑‍💻 Shout out to @joncphillips and @denicmarko for providing such useful tips on a daily basis! If you’re into coding, you should definitely check this out! dailytips.dev/

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MongoDB just launched new AI Skill Badges designed to help developers move from AI prototypes to production-ready systems. What stands out is that these badges focus on practical capabilities teams actually need to ship AI apps, all inside the MongoDB Atlas platform developers already know. ↳ Memory for AI Applications Build persistent memory for AI agents using MongoDB, LangGraph, vector search, and Voyage AI so applications can retain context across sessions while keeping user data isolated and secure. fandf.co/4ulWIsY ↳Voyage AI with MongoDB Create semantic search and retrieval pipelines optimized for relevance, latency, and cost using vector embeddings and MongoDB Vector Search. fandf.co/4dyQaA7 ↳ Vector Search Performance Learn how to diagnose and optimize MongoDB Vector Search in production using Atlas Metrics, quantization, partial indexing, and dedicated Search Nodes. fandf.co/4ukOalW Each badge includes hands-on learning, a short skills assessment, and a verifiable credential you can share on LinkedIn. For teams already using MongoDB, this is a pretty direct path to upskilling developers on production AI workflows without introducing an entirely new stack. — Thanks to @MongoDB for sponsoring this post!

The hard part of AI coding isn’t writing code. It’s giving agents the right context and keeping work connected across tickets, docs, and PRs. Cursor Agent in Jira does exactly that. Check it out ⟶ fandf.co/43lLPvc #Ad #AtlassianPartner

It remembers. So you don't have to. • AGENTS.​md for project instructions and architecture context • Automatic checkpoints before every modification • Rewind code, conversations, or both at any time • `--resume` to continue exactly where you left off • Auto-compact keeps long sessions focused and efficient Project memory, built in. Learn more → commandcode.ai/docs

JavaScript Quiz! Do you know what will be the output of this code?