Practical
We track 17 posts about Practical from 17 engineering blogs. Most active: Amit Merchant, Ariya Hidayat, Bartlomiej Filipek. Latest post: Oct 7, 2026.
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Recent posts
A Practical Way to Check Your AI Search Visibility (opens on the source site)
Last month, I built an AI agent that could search the web before answering a question. While working on the article around that project, I got another project idea and that was around the visibility of a website in the world of AI-powered search. The idea essentially revolves around the fact that whether the AI-powered search mentions your website in its answer or not or whether it cites your website as a source or not, and lastly, compare these resulst with the regular Google search results. Does your website show up when AI-powered search answers questions related to the topics it covers?…
Node 26 Debounce and Throttle: Practical Guide and Lodash Comparison (opens on the source site)
Use Node.js 26.10's built-in util.debounce and util.throttle: options, promise behavior, tested examples, the cancel() gotcha and how they differ from Lodash. Continue reading Node 26 Debounce and Throttle: Practical Guide and Lodash Comparison on SitePoint.
Data modernization: A practical guide for getting it right (opens on the source site)
Warehouse, lake or lakehouse: How to pick Data warehouse: structured, schema-on-write, built for business intelligence (BI) and reporting with mature SQL tooling. A good fit when your workloads are well-defined, mostly structured and governance/consistency matter more than raw flexibility. Data lake: cheap storage, schema-on-read, holds structured, semi-structured and unstructured data side by side. Great for data science and exploratory work, but without discipline, it turns into a data swamp nobody trusts. Lakehouse: the industry's answer to not wanting to run and reconcile two separate…
Run AI in the Browser: A Practical Guide to Transformers.js (opens on the source site)
Transformers.js lets you run AI models directly in the browser without a backend, API keys, or an internet connection after the model is cached. The article explores how it works, which models are available, and the trade-offs of client-side AI compared to traditional AI providers. Read more
Reducing C++ template bloat by factoring out the type-dependent portions of the function, practical exam (opens on the source site)
Applying our principles. The post Reducing C++ template bloat by factoring out the type-dependent portions of the function, practical exam appeared first on The Old New Thing.
Terraform and Kubernetes: A Practical Guide for 2026 (opens on the source site)
Pulumi ·
Yes, Terraform can manage Kubernetes: the official hashicorp/kubernetes provider lets you declare Deployments, Services, and other objects as HCL resources, and community providers like kubectl fill in the gaps. It works well for many teams. The friction shows up around two well-documented limits — provider ordering and plan-time API access — and around testing, where a general-purpose language changes what’s possible. That friction matters more in 2026 than it did a few years ago. Kubernetes infrastructure now sits next to AI-driven engineering workflows: agents that propose changes, run…
Using LLMs to Analyze Spark SQL Plans: A Practical Approach to Debugging Long-Running Jobs (opens on the source site)
Expedia ·
Expedia Group Technology — InnovationUsing large language models to reveal bottlenecks in Spark SQL execution plansPhoto by Luis del RíoIf you’ve ever stared at a 300-plus-node physical plan at 2 a.m. trying to spot a missing broadcast or one cursed skewed partition, this is for you.Spark makes it deceptively easy to write complex SQL that looks correct but quietly turns into a performance and cost problem at scale. A query that runs fine on day one can slow to a crawl as data grows, joins get wider, and aggregations become more nested. Suddenly, jobs take hours instead of minutes, clusters…
Next.js error handling: a practical guide (opens on the source site)
Next.js gives developers a structured way to handle errors at every level of an application — from form validation to root-level crashes. Learn how to manage expected errors with return values, catch uncaught exceptions with error boundaries, and set up automatic error reporting in production.
Declarative vs. Imperative Programming: A Practical Guide for Choosing the Right Paradigm (opens on the source site)
Toptal ·
Deep understanding of declarative versus imperative programming shapes how developers write, run, and maintain code. This guide explains the differences, challenges, and when to choose each approach.
Stop Defaulting to "use client": A Practical Mental Model for React Server Component Architecture (opens on the source site)
How thinking in server-first component boundaries can simplify data flow and reduce client JavaScript in Next.js applications.When I first started working with the Next.js App Router, I kept running into the same situation.Components would suddenly break — usually after adding a hook or a click handler. The fix felt obvious: add "use client".But after doing this a few times, I realised something: I was slowly turning my application back into a traditional client-side React app.Which defeats the whole point of React Server Components.The real challenge isn’t learning how to use Client and…
Making AI Write Android Code Our Way: A Practical Guide to Agent Skills (opens on the source site)
Medium ·
Generated by DALL-ETurning knowledge into reusable AI agent instructions for a small, fast-moving team.We're a small Android team at Medium, just a handful of engineers maintaining and evolving the Medium Android app. Our codebase follows Clean Architecture with Kotlin, Jetpack Compose, Hilt, Apollo GraphQL, and a growing number of feature modules. Like most Android teams, we have strong opinions about how code should be structured: where ViewModels get their data, how analytics events flow, how feature flags are checked, what a "new screen" looks like from Fragment to preview function.The…
7 Practical std::chrono Calendar Examples (C++20) (opens on the source site)
This article collects small, self-contained, and practical examples for working with std::chrono calendar types. The previous blog post - see Exploring C++20 std::chrono - Calendar Types - C++ Stories - focused on the building blocks: calendar types, operators, and arithmetic rules. In this post, we’ll focus on practical examples like: What’s the last business day of the month? When is the third Friday in June? How many days since the start of the year? What happens when I add a month to January 31? And a few more Let’s start. 1. Which day of the year is that? Let’s begin with a simple…
From Crash to Resolution: A Practical Guide for Xbox & Windows Developers (opens on the source site)
If you’ve ever had an app crash at the worst possible moment, you’ll know how frustrating it can be - both for developers and players. Recently, I wrote an article for the Microsoft Developer blog that dives deep into this exact challenge: how to diagnose and resolve
Making Documentation Simpler and Practical: Our Docs-as-Code Journey (opens on the source site)
In the fast-paced world of software development, documentation often gets a bad rap. It's perceived as a chore, a necessary evil, and sometimes, unfortunately, an afterthought. But what if writing documentation could be as dynamic and collaborative as writing the code itself? What if it could be simpler to write and more practical to use?
Practical Considerations for Advancing AI Collaboration in Software Development (opens on the source site)
TL;DR Human-in-the-loop is essential; AI offers probability, not certainty. AI excels at word-smithing, so spend more time on documentation and context. Leverage diverse AI models for varied research, improvements, and analysis. Be wary of deskilling: if AI makes a task trivial, agents may soon replace it. You should feel like you are testing the boundaries of what AI is capable of for at least some tasks. The Problem AI’s proficiency in handling routine coding allows human engineers to dedicate more time to strategic activities such as system design, architectural planning, intricate…
Practical Considerations for Advancing AI Collaboration in Software Development (opens on the source site)
TL;DR Human-in-the-loop is essential; AI offers probability, not certainty. AI excels at word-smithing, so spend more time on documentation and context. Leverage diverse AI models for varied research, improvements, and analysis. Be wary of deskilling: if AI makes a task trivial, agents may soon replace it. You should feel like you are testing the boundaries of what AI is capable of for at least some tasks. The Problem AI’s proficiency in handling routine coding allows human engineers to dedicate more time to strategic activities such as system design, architectural planning, intricate…
Practical Testing of Firebase Projects (opens on the source site)
Your little Firebase project is getting bigger every day? Never underestimate the need to establish a solid and firm integration tests from the get go.
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