A high‑traffic e‑commerce monolith was split into 13 microservices, causing order latency to jump from 300 ms to 1.2 s, P99 over 8 s, and throughput to halve, and the article analyzes why this happens and presents ten fatal anti‑patterns with concrete remediation steps.
This article reviews ten frequent misuses of Domain‑Driven Design—ranging from over‑engineering simple CRUD apps to misusing layers, aggregates, services, repositories, and events—provides concrete counter‑examples, explains why they happen, and offers practical corrective guidelines plus a self‑check checklist.
This guide explains how to use the Skill Creator tool to generate automated trigger tests, compare skill‑enabled versus baseline performance, continuously evaluate results, apply checklists, debug with a six‑step process, avoid six common anti‑patterns, and manage skill versioning and reuse so that AI skills become progressively smarter.
The article satirically outlines four common ways CMDB projects become unusable—over‑recording assets, buying tools without process changes, reckless auto‑discovery, and isolating the database from business—then offers concrete anti‑pattern fixes and governance tips to turn a failing CMDB into a reliable digital foundation.
The article lists twelve common anti‑patterns—such as over‑splitting microservices, excessive refactoring without rollout, ultra‑long methods, deep nesting, random naming, misleading comments, copy‑paste code, ignored specs, missing logs, and reinventing wheels—that dramatically reduce code readability and increase maintenance difficulty.
This article examines common log‑management anti‑patterns—such as copy‑truncate rotation, NAS storage, multi‑process writes, file‑hole creation, frequent overwrites, and Vim edits—explains why they cause data loss or duplicate collection, and offers practical best‑practice recommendations for reliable log handling in cloud‑native environments.
This article reveals how common bad coding practices such as over‑engineering, hidden business logic in utility classes, deep inheritance trees, excessive DTO layers, unnecessary interfaces, and contract‑less microservices degrade team efficiency and project quality, and offers concrete, principle‑driven remedies.
This article examines ten frequent Python anti‑patterns—such as mutable default arguments, catching broad exceptions, overly long functions, reinventing the wheel, misusing list comprehensions, excessive lambdas, hard‑coded values, ignoring virtual environments, overusing inheritance, and neglecting code formatting—explaining why they harm readability, maintainability or performance and offering clear, better‑practice alternatives.
This article lists twelve common coding anti‑patterns—such as over‑splitting services, excessive method length, misleading comments, and ignoring logging—that dramatically reduce code readability and maintainability, illustrated with a fictional developer’s missteps and practical lessons for better software engineering.
Discover the most subtle Python anti‑patterns—from using is for value comparison to overusing map and filter—explaining why they harm readability, performance, or correctness, and learn clear, idiomatic alternatives that make your code cleaner, faster, and more maintainable.
This article dissects real-world WMS login failures, exposing captcha hell, chaotic permission setups, and broken login experiences, then offers concrete anti‑pattern designs, permission‑allocation rules, and resilient login strategies to transform a fragile login page into a rock‑solid, user‑friendly gateway.
This article examines common backend anti‑patterns such as inefficient MySQL for‑loops, heavy multi‑query methods, full‑table scans, improper thread‑pool creation, and long‑running transactions, and provides practical optimization recommendations including query consolidation, caching, indexing, and proper resource management.
The article humorously lists twelve common coding “techniques” that reduce readability and increase maintenance difficulty, illustrating each with exaggerated examples of a programmer named Er Gou who over‑splits microservices, writes massive methods, misuses comments, copies code, and ignores logging.
The article humorously lists twenty counterproductive coding “tips” – from using single‑letter variables and omitting comments to writing one‑line code, ignoring errors, creating unused variables, deep nesting, and skipping tests – claiming these practices boost productivity despite harming readability and reliability.
The article explains how strong, open‑ended questions can help Scrum teams identify and fix common retrospective anti‑patterns—such as management attendance, lack of follow‑up, off‑topic discussions, trying to solve everything at once, and unclear action items—by fostering deeper reflection and collaborative problem‑solving.
This article explains what DevOps is, why it matters, and presents nine practical best‑practice recommendations—including culture, CI/CD, testing, observability, automation, security, and IaC—while also highlighting common anti‑patterns to avoid for successful DevOps adoption.
This article examines common Serverless anti‑patterns such as monolithic Lambdas, Lambda orchestration, Lambda‑to‑Lambda calls, and event loops, then presents six proven Serverless design patterns—including Command, Messaging, Priority Queue, Fan‑out, and Pipeline‑Filter—to help architects build cost‑effective, highly available, and maintainable cloud applications.
This article examines common anti‑patterns and best‑practice design patterns for Serverless architectures, explaining why monolithic Lambda functions, orchestration misuse, Lambda‑to‑Lambda calls, and event loops are problematic, and presenting command, messaging, priority queue, fan‑out, and pipeline‑filter patterns to build scalable, maintainable cloud applications.
This article examines common serverless anti‑patterns such as monolithic Lambdas, orchestration misuse, Lambda‑to‑Lambda calls, and event loops, and presents practical design patterns—including Command, Messaging, Priority Queue, Fan‑out, and Pipe‑Filter—to help architects build scalable, maintainable, and cost‑effective cloud applications.
This article explains the fundamentals of CI/CD and pipelines, identifies eight common anti‑patterns such as lack of code‑as‑pipeline, slow execution, unstable results, misuse of production data, excessive complexity, tight coupling, zombie pipelines, and manual intervention, and provides practical causes and remediation strategies for each.
The article lists twenty‑one harmful programming habits—such as reinventing wheels, overusing design patterns, skipping testing, ignoring monitoring, and avoiding proper architecture—that can cripple a development team and serve as a warning for engineers seeking better practices.
A tongue‑in‑cheek list of twenty sarcastic anti‑patterns reveals how ignoring requirements, skipping reviews, avoiding standards, and mishandling deployment and monitoring inevitably lead to constant system crashes and operational chaos.
This article examines ten common Kubernetes deployment anti‑patterns—from embedding configuration inside Docker images to using the latest tag in production—and offers practical recommendations such as externalizing configs with ConfigMaps, employing Helm or Kustomize, setting resource limits, and implementing proper monitoring and rollout strategies.
This article presents a tongue‑in‑cheek guide of nineteen "anti‑best‑practice" rules sourced from a GitHub project, illustrating how to deliberately produce unreadable, unmaintainable code by minimizing typing, mixing naming styles, avoiding comments, and other absurd conventions.
The article lists twelve danger signals—ranging from shallow modules and information leaks to vague naming and unreadable code—that reveal hidden complexity in software design and help developers identify and refactor problematic patterns.
This tongue‑in‑cheek article lists absurd techniques—from confusing variable names and deceptive comments to overloaded operators and chaotic class designs—that deliberately make software hard to read, test, and maintain, illustrating what not to do when writing clean, maintainable code.
This article gathers subtle yet frequent Python anti‑patterns—misusing range, improper list comprehensions, performance traps, variable leakage, global‑scope misuse, and PEP8 violations—explaining why they occur, their drawbacks, and offering clear, Pythonic alternatives to write more readable and bug‑free code.
