The Unspoken Truth: When Best Practices Become Bad Habits
Let me tell you something you won't hear in most tech meetings: some of the 'best practices' we've been religiously following for years are quietly becoming obsolete. I've been building web applications for over a decade now, and I've watched as certain rules transformed from helpful guidelines into cargo cult rituals. You know what I'm talking about—those practices we follow because 'that's how it's done,' not because they actually solve our problems.
Recently, a Reddit discussion in r/webdev blew up with developers confessing which sacred cows they've stopped worshipping. The thread hit 432 upvotes with 342 comments—clearly, this resonated. Developers are tired of following rules that don't deliver value. They're questioning practices that look good on paper but feel wrong in practice.
In this article, we'll explore the specific practices developers are abandoning, why they're making these changes, and what you should actually focus on in 2026. This isn't about being lazy or cutting corners. It's about being smart—about recognizing when a rule has outlived its usefulness and when it's time to write new ones.
The 100% Test Coverage Myth: Why Perfect Isn't Practical
Here's the confession that started it all: "I used to chase that number like it meant something." That developer was talking about 100% test coverage, and honestly? I've been there too. Early in my career, I'd obsess over coverage reports, writing tests for every single function, every component, every line of code. It felt professional. It felt thorough.
But here's what I learned the hard way: 100% test coverage doesn't mean 100% confidence. Not even close. I've seen codebases with perfect coverage that were brittle nightmares to maintain. I've also seen code with 70% coverage that was rock-solid and reliable. The difference? What you're testing matters infinitely more than how much you're testing.
These days, I focus on testing what actually matters. Business logic? Absolutely. Integration points between systems? Critical. But a test that verifies a button renders with the right CSS class? That's just noise. That's a test that will break when you refactor styling but won't catch any meaningful bugs. It gives you a false sense of security while adding maintenance overhead.
The real shift happened when I started asking: "What could go wrong here, and how would a test catch it?" If the answer is "nothing meaningful" or "the test would be testing the framework, not my code," I skip it. My test suites are smaller now, but they're more valuable. They catch real issues. They give me actual confidence when I deploy.
Component Purity: When DRY Becomes Drowned
"Keeping components 'pure' and lifting all state up." That was the second confession, and it cuts to the heart of modern frontend development. We've all been taught that components should be pure functions of their props. That state should live as high up in the tree as possible. That we should avoid local state like it's some kind of code smell.
In theory, it sounds clean. In practice? It often creates a mess.
I remember building a complex form with dozens of fields. Following the 'pure component' rule, I lifted all state to the top-level component. The result? A massive, unwieldy state object that had to be passed down through six layers of components. Every field change triggered re-renders of everything. The code was 'pure' in the technical sense, but it was also slow, hard to reason about, and painful to modify.
Now, I take a more pragmatic approach. If state is truly local—if only one component cares about it—I keep it local. A checkbox's checked state? A dropdown's open/closed state? A form field's temporary value before submission? These often belong right where they're used.
The key insight is this: not all state is created equal. Some state is truly global (user authentication, theme preferences). Some is shared across a few related components (a multi-step wizard's current step). And some is genuinely local (is this tooltip visible?). Treating everything as global state creates unnecessary complexity and performance issues.
The Microservices Madness: When Distributed Becomes Disastrous
This one wasn't in the original Reddit post, but it came up repeatedly in the comments: the blind adoption of microservices architecture. For years, we've been told that microservices are the 'right way' to build scalable systems. Every conference talk, every blog post, every tech influencer has pushed this idea that if you're not breaking your monolith into microservices, you're doing it wrong.
But here's what they don't tell you: microservices introduce massive complexity. Suddenly, you're not just writing code—you're managing service discovery, inter-service communication, distributed transactions, and eventual consistency. You need to handle partial failures, retry logic, and circuit breakers. A simple feature that would take a day in a monolith can take weeks in a microservices architecture.
I've worked on teams that split their application into microservices prematurely. The result was slower development velocity, harder debugging (tracing requests across five services is no joke), and operational overhead that drained engineering resources. We spent more time on infrastructure than on delivering value to users.
My approach now? Start with a well-structured monolith. Use clear boundaries within the codebase—bounded contexts, if you're familiar with Domain-Driven Design. Only split into separate services when you have a clear reason: different scaling requirements, different technology stacks, or different teams that need independent deployment cycles. And even then, consider a modular monolith first.
API Design Dogma: REST Isn't Always Best
Another practice that's quietly falling out of favor: strict adherence to RESTful principles for every single API endpoint. Don't get me wrong—REST is great for many use cases. But it's not a religion, and treating it like one can lead to awkward, inefficient APIs.
I've built APIs where we twisted ourselves into knots trying to make something fit REST conventions. Batch operations that had to be represented as 'collections' with weird semantics. Complex queries that didn't map cleanly to resource URLs. Real-time updates that REST just doesn't handle well.
In 2026, we have more options. GraphQL gives clients exactly the data they need in a single request. gRPC offers efficient binary communication for internal services. WebSockets and Server-Sent Events handle real-time needs beautifully. And sometimes, a simple RPC-style endpoint is just what you need.
The key is to match the tool to the job. For simple CRUD operations on resources, REST is still excellent. For complex data requirements with nested relationships, consider GraphQL. For internal service communication where performance matters, gRPC might be your answer. For real-time features, WebSockets are your friend.
Stop forcing square pegs into round holes. Choose the right protocol for each use case, even if it means mixing approaches within the same application.
Over-Engineering: The Framework-of-the-Month Club
Here's a trend I've noticed: developers reaching for heavyweight frameworks and libraries for problems that could be solved with simpler tools. Need a simple state management solution? Let's bring in Redux with five middleware packages and a complex reducer structure. Building a basic form? Time to install a form library with all its dependencies.
This over-engineering comes from a good place—we want to use 'professional' tools, we want to follow 'best practices,' we want our code to be 'enterprise-grade.' But often, we're adding complexity without adding value.
I recently worked on a project where the previous team had used Redux for state management. The application had maybe five pieces of global state total. The Redux setup was hundreds of lines of code—actions, action creators, reducers, selectors. It took me longer to understand the Redux code than to understand the actual business logic.
When I rebuilt a similar application, I used React's built-in Context API for the few pieces of truly global state. For everything else, I used local state or simple prop drilling. The code was easier to understand, easier to modify, and had fewer dependencies. It was also significantly smaller.
The lesson? Start simple. Use the tools that come with your framework. Only add complexity when you have a clear need for it. And when you do add a library, make sure you're getting more value than you're paying in complexity.
Continuous Deployment: When Fast Becomes Reckless
Continuous deployment has been held up as the gold standard for modern software teams. The idea is simple: every change that passes automated tests gets deployed to production automatically. It's fast, it's efficient, and it reduces the friction of getting code to users.
But here's the thing: it's not right for every team or every application. I've seen teams implement continuous deployment because it was the 'modern' thing to do, only to create chaos.
One team I worked with deployed to production 20+ times a day. Most changes were small, but the constant churn made it hard to track what was in production. When something broke, pinpointing which change caused it was like finding a needle in a haystack. The team was so focused on deploying quickly that they'd lost the ability to deploy confidently.
Another team deployed a breaking change to a critical API endpoint during peak business hours because the automated tests passed. The tests checked that the endpoint returned data, but they didn't check that the data format was backward compatible. The result? Mobile apps crashed for thousands of users.
My approach now is more nuanced. For low-risk changes to non-critical parts of the application, continuous deployment is great. For high-risk changes or changes to critical systems, I prefer a more deliberate process. That might mean feature flags, canary deployments, or even scheduled deployment windows for particularly sensitive systems.
The goal isn't to deploy as fast as possible. The goal is to deploy as reliably as possible. Sometimes, slowing down actually helps you move faster in the long run by avoiding costly mistakes.
Practical Tips: How to Decide What to Keep and What to Abandon
So how do you decide which 'best practices' to follow and which to question? Here's my framework:
First, ask "Why?" When someone suggests a practice, don't just accept it. Ask why it's recommended. What problem does it solve? What trade-offs does it involve? If the answer is "because everyone does it" or "it's considered best practice," dig deeper.
Second, consider context. A practice that makes sense for a large team working on a complex enterprise application might be overkill for a solo developer building a prototype. A practice that's essential for a financial application might be unnecessary for a personal blog. Tailor your practices to your specific situation.
Third, measure outcomes, not adherence. Don't measure how well you're following practices. Measure the outcomes those practices are supposed to deliver. If you're implementing test coverage to catch bugs, track how many bugs your tests actually catch, not just your coverage percentage. If you're implementing code reviews to improve code quality, track whether code quality is actually improving.
Fourth, be willing to experiment. Try a practice for a few weeks or months, then evaluate whether it's helping. If it's not, stop doing it. Practices should serve you, not the other way around.
Finally, trust your experience. If something feels wrong, even if it's considered 'best practice,' there's probably a reason. Your intuition as an experienced developer is valuable. Don't ignore it just because some blog post or conference speaker says you should.
Common Questions and Concerns
"Aren't you just advocating for sloppy code?"
Not at all. I'm advocating for thoughtful code. There's a big difference between skipping tests because you're lazy and skipping tests because they don't add value. There's a difference between writing messy components and writing components with appropriate local state. The goal isn't to be sloppy—it's to be efficient, to focus your effort where it matters most.
"What about team consistency? If everyone does their own thing, won't the codebase become a mess?"
Absolutely. I'm not suggesting every developer should make up their own rules. Teams need standards. But those standards should be based on what works for that team, for that project, not on generic 'best practices' copied from the internet. Have the discussion as a team. Decide together what makes sense for your context. Document it. Then follow your own standards, not someone else's.
"How do I convince my team or manager to abandon a practice that isn't working?"
Data is your friend. Don't just say "I think we should stop doing X." Show the costs. Measure the time spent on the practice versus the value delivered. Find examples where the practice caused problems. Propose alternatives. And be willing to experiment—suggest trying a different approach for a month and comparing results.
"What practices should we absolutely keep?"
Version control. Code reviews (though the format can vary). Writing tests for critical logic. Monitoring production. Incident response procedures. Documentation for complex systems. These aren't just 'best practices'—they're foundational to professional software development. The practices I'm questioning are the ones on the margins, the ones where the costs often outweigh the benefits.
Finding Your Own Path in 2026
The most important lesson here isn't which specific practices to follow or abandon. It's this: think for yourself. The web development landscape in 2026 is more complex than ever, with new tools, frameworks, and approaches emerging constantly. Blindly following yesterday's 'best practices' won't prepare you for tomorrow's challenges.
What matters most isn't whether you achieve 100% test coverage or keep all your components pure. What matters is whether you're building software that works, that delivers value to users, that you can maintain and improve over time. Sometimes, that means following established practices. Sometimes, it means breaking the rules.
The developers in that Reddit thread weren't being lazy or unprofessional. They were being pragmatic. They'd learned through experience which practices delivered value and which didn't. They'd moved beyond following rules to understanding principles.
My challenge to you: look at your own practices. Which ones are you following because they genuinely help, and which are you following because you think you should? Be honest with yourself. Then have the courage to change what isn't working. That's what being a professional developer in 2026 is really about.
