Programming Languages
Use the right programming language for the job, not just the one you know best.
A developer named Alex loved Python for its simplicity. When tasked with building a real-time audio processing application, he insisted on using Python. He spent weeks trying to optimize his code, battling performance issues and latency. His colleague, meanwhile, built a quick prototype in C++ that handled the audio stream flawlessly. Alex eventually had to rewrite his entire application, learning the hard lesson that mastery of one language doesn’t make it the right tool for every task. The project’s success depended on choosing the language that fit the problem, not the developer’s comfort zone.
Stop doing manual memory management in 2025. Do use a language with automatic memory management instead.
An old-school programmer, building a complex desktop application in C++, was proud of his manual memory management skills. However, as the application grew, subtle memory leaks started to appear, causing random crashes that were a nightmare to debug. He spent more time hunting for memory allocation errors than adding new features. A junior developer on his team finally convinced him to try a new component in Rust, which guarantees memory safety at compile time. The crashes stopped. He realized that modern languages with automatic memory management or safety guarantees weren’t a crutch; they were a superpower.
The #1 secret for learning a new programming language quickly that polyglot programmers know.
The secret isn’t about memorizing syntax; it’s about understanding the core concepts and building something small immediately. A developer wanted to learn Go. Instead of reading a 500-page book cover-to-cover, she spent two hours learning the basics of goroutines and channels. Then, she immediately challenged herself to build a simple web scraper that could fetch multiple sites concurrently. By applying the concepts to a real, tangible project, she cemented her understanding far more effectively than she ever could have through passive reading. She learned the language by using it.
The biggest lie you’ve been told about your favorite programming language being the “best”.
Every online forum is a battlefield where developers declare their favorite language as the “best.” A junior developer, convinced by online arguments that Rust was the ultimate language, tried to use it for a simple scripting task. He fought with the compiler for hours over ownership rules. His senior colleague accomplished the same task in ten minutes with a simple Python script. The lie isn’t that your favorite language is bad; it’s that there is a single “best” one. The best language is always relative to the problem you’re trying to solve.
I wish I knew this about the trade-offs between static and dynamic typing when I was a junior developer.
As a junior dev, I loved the freedom of Python, a dynamically-typed language. I could write code quickly without worrying about type definitions. But as my first big project grew, it became fragile. A simple typo or passing the wrong data type would cause errors that only appeared at runtime, often in production. I wish I knew then that statically-typed languages like TypeScript or Go, while requiring more upfront effort, catch these errors at compile time. That initial “slowness” saves you from a world of late-night debugging down the road.
I’m just going to say it: JavaScript is not a well-designed language, but it’s here to stay.
A programming purist once listed all of JavaScript’s infamous quirks: its confusing type coercion (‘5’ – 1 = 4), its weird this keyword, its historical inconsistencies. He argued it was a poorly designed language and refused to use it. However, JavaScript runs in every web browser on the planet. Its ecosystem, with tools like Node.js and React, is colossal. While it may not have the elegant design of other languages, its ubiquity and massive community make it one of the most important and practical languages for a developer to know.
99% of developers make this one mistake when learning a new language.
The most common mistake is focusing entirely on syntax while ignoring the language’s unique philosophy or “idiom.” A developer with a background in object-oriented Java decided to learn a functional language like Elixir. She tried to write code using Java patterns, creating complex classes and mutable state, essentially writing Java in Elixir. Her code was clunky and inefficient. She only became proficient when she stopped translating syntax and started embracing the functional paradigm of immutability and pure functions, learning to “think” in Elixir.
This one small habit of reading the official documentation will change the way you master a programming language forever.
A developer was constantly getting stuck on small problems. His first instinct was always to search Stack Overflow and copy-paste the first solution he found. This worked, but he never truly understood why it worked. He forced himself to adopt a new habit: before searching online, he would open the official documentation for the language or library. He found that the docs were not only accurate but also explained the underlying concepts. This small change transformed him from a code copier into a true problem solver.
The reason your code is slow is because you’re using an interpreted language for a performance-critical task.
A data science team built a complex simulation model in Python. It worked, but it took 12 hours to run, making iteration impossible. They profiled the code and found the bottleneck was a massive loop doing heavy mathematical calculations. Python, as an interpreted language, has overhead that makes it slow for this kind of number crunching. They rewrote just that one critical section of the code in C++ and created a Python wrapper for it. The simulation runtime dropped from 12 hours to 15 minutes.
If you’re still writing new projects in COBOL, you’re losing touch with modern software development.
A large, established bank had a team of COBOL programmers maintaining its core systems. When they needed to build a new mobile banking feature, they decided to write it in COBOL to interface with their mainframe. The project was a nightmare. They struggled to find modern tools, integrate with web APIs, and hire new developers. A competing bank built a similar feature using a modern stack (like Java or C#), creating a secure API to communicate with their own legacy systems. They launched in half the time, with a more stable and maintainable product.
Web Development
Use modern JavaScript frameworks, not vanilla JavaScript for complex applications.
A developer, proud of her “pure” coding skills, decided to build a complex, interactive dashboard using only vanilla JavaScript. She spent weeks writing thousands of lines of code to manage application state, update the DOM, and handle user interactions. The result was a tangled, buggy mess that was impossible to maintain. Her colleague rebuilt the entire application in React in a fraction of the time. The framework provided a structured, efficient way to handle the complexity, proving that for modern web apps, frameworks aren’t a crutch; they’re essential tools.
Stop doing server-side rendering for everything. Do use a static site generator for content-heavy sites instead.
A company ran their popular blog on a traditional server-side framework like WordPress. Every time a user visited a page, the server had to query the database and render the HTML from scratch. When a post went viral, their server crashed under the load. They switched to a static site generator like Hugo. The entire site was pre-built into simple HTML files. It was incredibly fast, secure, and could handle massive traffic spikes with ease because there was no server-side processing or database to worry about.
The #1 hack for improving your website’s performance that Google loves.
The secret isn’t some obscure code trick; it’s optimizing your “Critical Rendering Path.” This means ensuring that the essential content a user sees first (the “above-the-fold” content) loads almost instantly. A web developer analyzed a slow e-commerce site. She identified the critical CSS needed for the header and product display and embedded it directly in the HTML. She then deferred the loading of all other CSS and JavaScript. This simple change made the page appear to load instantly, dramatically improving user experience and search engine rankings.
The biggest lie you’ve been told about “no-code” website builders.
The lie is that no-code tools give you complete freedom without any trade-offs. A small business owner built a beautiful website using a popular no-code platform. It was easy and fast. But as his business grew, he hit a wall. He couldn’t implement a custom checkout process, his site’s performance was slow, and he couldn’t export his code to move to a better hosting provider. He was locked into the platform’s ecosystem. No-code is fantastic for simple sites, but it often trades long-term flexibility and control for short-term convenience.
I wish I knew this about cross-browser compatibility when I built my first website.
I built my first website using all the latest CSS features. It looked perfect on my Chrome browser. I proudly sent the link to a client. They replied with a screenshot showing a completely broken layout. They were using an older version of Safari that didn’t support the new features I had used. I spent the next two days rewriting my code and learning about vendor prefixes and feature detection. I wish I had known that just because something works on your browser, it doesn’t mean it works for everyone.
I’m just going to say it: The modern web is too complex.
A developer in 2005 could build a website with a simple text editor and some knowledge of HTML and CSS. Today, to build a simple “Hello, World” app with a modern framework, a developer might need to understand package managers, bundlers, transpilers, dozens of dependencies, and a complex command-line interface. While these tools are powerful, the barrier to entry has become incredibly high. The accidental complexity of the modern web development toolchain often gets in the way of the simple goal: building things for people to use.
99% of web developers make this one mistake with their CSS.
The most common mistake is writing highly specific, deeply nested CSS selectors like div#main-content .container ul.item-list li a. This creates brittle code that is hard to override and maintain. It leads to a cascade of !important tags just to make a simple style change. A better approach, used by methodologies like BEM, is to use simple, flat class-based selectors. This makes your CSS more modular, reusable, and predictable, saving you from the nightmare of “CSS specificity wars.”
This one small action of optimizing your images will change your website’s loading speed forever.
An online portfolio for a photographer featured stunning, high-resolution images. The only problem was that each image was a 5MB file, and the homepage took 20 seconds to load. Most visitors left before seeing any of the work. The photographer learned about image optimization. She used a tool to compress her images, reducing their file size by 80% with almost no visible loss in quality, and served them in a modern format like WebP. This one action cut her site’s loading time to under 2 seconds, and her engagement rates soared.
The reason your web app is slow is because of excessive DOM manipulation.
A developer built a web application that displayed real-time data in a large table. The app felt sluggish and unresponsive. The reason? With every new piece of data, his code was re-rendering the entire table from scratch. This constant, large-scale manipulation of the Document Object Model (DOM) is computationally expensive. Modern JavaScript frameworks like React and Vue solve this by using a “virtual DOM.” They calculate the most efficient way to update the display, making only the minimal necessary changes, which results in a much faster and smoother user experience.
If you’re still not using HTTPS, you’re losing your users’ trust.
A user was browsing an e-commerce site from a coffee shop’s public Wi-Fi. They found a product they liked and proceeded to checkout, entering their name, address, and credit card information. Because the site was only using HTTP, not HTTPS, all of that information was sent in plain text. An attacker on the same Wi-Fi network easily intercepted it. Modern browsers now prominently mark HTTP sites as “Not Secure,” and users have been trained to look for the padlock icon. Failing to use HTTPS is a signal that you don’t care about your users’ security and privacy.
Mobile App Development
Use cross-platform development frameworks for most apps, not native development for both iOS and Android.
A startup wanted to launch their new social app. They hired two separate teams: one for iOS (writing in Swift) and one for Android (writing in Kotlin). The development was slow, expensive, and inconsistent, as the two teams struggled to keep features in sync. A competing startup chose a cross-platform framework like React Native or Flutter. With a single codebase, they built and launched their app on both platforms in half the time and with half the budget, allowing them to iterate and capture the market faster.
Stop doing monolithic mobile app architectures. Do use a modular approach like Clean Architecture instead.
A mobile app for a large retailer was a “monolith.” All the code for networking, UI, and business logic was tangled together. Adding a new feature or fixing a bug was a nightmare, as a small change could have unintended consequences across the entire app. They decided to re-architect the app using a modular approach. They separated the code into distinct layers: presentation, domain, and data. This made the app easier to test, maintain, and for new developers to understand, dramatically increasing their development speed and stability.
The #1 secret for getting your app featured on the App Store.
The secret isn’t a marketing gimmick; it’s building a high-quality app that takes full advantage of the platform’s latest features. When Apple releases a new version of iOS with features like interactive widgets or Live Activities, the App Store editors are actively looking for apps that implement these features in a creative and useful way. A developer of a simple to-do list app was one of the first to build a beautiful and functional new widget. The App Store featured his app, leading to hundreds of thousands of downloads overnight.
The biggest lie you’ve been told about the “build it and they will come” myth of app development.
A passionate developer spent a year of his life building what he thought was the perfect mobile app. He polished every pixel and perfected every feature. He launched it on the App Store and waited for the downloads to pour in. He got a total of 12 downloads, most of them from his family. The lie is that a good product is all you need. Without marketing, community building, and a clear plan to reach your target audience, even the best app in the world will remain undiscovered in a sea of millions.
I wish I knew this about the importance of user testing when I released my first app.
I was so proud of my first app. I thought the user interface was intuitive and brilliant. The day I launched, the negative reviews started rolling in. Users were confused by the icons I had chosen and couldn’t find the main feature. I had made the classic mistake of assuming that I was the user. I wish I had spent just a few hours watching a handful of real people try to use my app before I launched. That small amount of user testing would have revealed the flaws in my design and saved me from a disastrous launch.
I’m just going to say it: Most mobile apps are a waste of time and money.
A company was convinced they “needed an app.” They spent $100,000 developing a mobile app that did the exact same thing as their mobile-friendly website. After an initial spike, downloads flatlined. Users didn’t want to download and install a separate application for something they could just access through their browser. For many businesses, a well-designed, responsive website is all they need. Unless your app provides unique functionality that leverages the phone’s native capabilities (like the camera or push notifications), you’re probably just building a very expensive bookmark.
99% of app developers make this one mistake with their app’s user interface.
The most common mistake is ignoring the platform’s established design conventions. An Android developer who loved the iPhone’s design tried to make his Android app look and feel exactly like an iOS app. He used iOS-style navigation tabs at the bottom and back buttons in the top-left corner. This confused Android users, who are accustomed to a different navigation paradigm. The result was an app that felt alien and clunky on its own platform. The best apps embrace the native look and feel of their respective operating systems.
This one small action of optimizing your app’s startup time will change your user retention forever.
A user downloads a new photo editing app. They tap the icon, excited to try it, and are met with a loading screen that lasts for ten seconds. Annoyed, they close the app and never open it again. Studies show that users are incredibly impatient. A slow startup time is one of the biggest reasons for uninstalls. A developer who focused on optimizing their app’s startup time—by reducing the amount of work done on the main thread and deferring initializations—saw their day-one user retention increase by over 20%.
The reason your app is draining battery is because of inefficient background processing.
A user noticed that their phone’s battery was dying by mid-day. They checked their battery usage stats and found that a social media app was the main culprit, even though they had barely used it. The reason? The app was constantly polling for new data in the background, waking up the phone’s processor and radio. A well-designed app uses efficient background processing techniques, like batching network requests or using the platform’s job scheduler, to minimize its impact on battery life.
If you’re still not analyzing your app’s analytics, you’re losing valuable user insights.
A developer launched a fitness app with dozens of features. He assumed the most popular feature would be the advanced workout planner. He integrated an analytics tool and was shocked to discover that 90% of his users were only using the simple water-tracking feature. All the time he was spending improving the other features was wasted. These insights allowed him to refocus his efforts on making the water-tracker the best on the market, which dramatically increased his app’s popularity and user satisfaction.
DevOps
Use a fully automated CI/CD pipeline, not manual deployments.
A team used to deploy their application manually. It was a stressful, all-day affair involving a 20-page checklist. Every deployment was risky, and they often had to roll back due to human error. They invested in building a fully automated CI/CD (Continuous Integration/Continuous Deployment) pipeline. Now, when a developer merges code, it is automatically built, tested, and deployed to production in minutes. Deployments are now a non-event, happening multiple times a day with high confidence, allowing the team to deliver value to customers faster and more reliably.
Stop doing siloed development and operations teams. Do embrace a collaborative DevOps culture instead.
A company had a classic “wall of confusion” between its developers and operations teams. The developers would “throw code over the wall” to the operations team to deploy. When something broke, the two teams would blame each other. They decided to embrace a DevOps culture. They merged the teams, giving them shared ownership of the application’s entire lifecycle. Developers started learning about operations, and operations engineers started contributing to the code. This collaboration broke down the silos and led to faster, more reliable software.
The #1 secret for a successful DevOps transformation that has nothing to do with tools.
The secret is fostering a culture of psychological safety. A team was given all the best DevOps tools—CI/CD, infrastructure as code, monitoring—but their transformation failed. The reason? The manager punished anyone whose deployment caused an issue. As a result, nobody was willing to take risks or innovate. A successful DevOps culture encourages experimentation and treats failures as learning opportunities, not reasons for blame. When people feel safe to fail, they also feel safe to innovate and improve.
The biggest lie you’ve been told about DevOps being just about automation.
The biggest lie is that if you just buy the right tools and automate everything, you are “doing DevOps.” A company spent millions on a sophisticated automation platform. But their teams still didn’t talk to each other, they still blamed each other for failures, and their release process was still slow. DevOps is not a tool; it’s a culture. It’s about breaking down silos, shared ownership, and a focus on continuous improvement. The automation is a powerful enabler of that culture, but it is not the culture itself.
I wish I knew this about Infrastructure as Code (IaC) when I was manually configuring servers.
I used to be a system administrator who spent my days manually clicking through web consoles and SSHing into servers to set them up. Each server was slightly different, and I lived in fear of a server crashing because rebuilding it from memory was a nightmare. Then I discovered Infrastructure as Code with tools like Terraform. I could define my entire infrastructure in a simple text file. Now, I can spin up an identical, perfectly configured environment in minutes. It changed my job from a reactive firefighter to a proactive engineer.
I’m just going to say it: You’re not doing DevOps if you’re not measuring everything.
A team claimed they had adopted DevOps. They had a CI/CD pipeline and were deploying frequently. But when asked how their changes impacted system performance or user satisfaction, they had no answer. They weren’t measuring anything. True DevOps culture is data-driven. It relies on a constant feedback loop of monitoring and measurement. You need to track metrics like deployment frequency, lead time for changes, change failure rate, and mean time to recovery. Without data, you’re just guessing.
99% of organizations make this one mistake when adopting DevOps.
The most common mistake is trying to create a separate “DevOps team.” An organization hired a team of “DevOps engineers” and tasked them with building automation for the development and operations teams. This didn’t break down the silo; it created a new one. The development and operations teams were not involved in the process and didn’t adopt the new tools. A successful DevOps adoption embeds these skills within the product teams, fostering shared ownership of the entire software lifecycle, rather than outsourcing “DevOps” to a separate group.
This one small habit of treating your infrastructure as code will change the way you manage your systems forever.
A developer needed a new staging environment to test a feature. In the old days, this would involve filing a ticket and waiting two weeks for the operations team to manually provision a server. By adopting Infrastructure as Code, she could simply copy a configuration file, change a variable name, and run a command. In five minutes, she had a perfect, isolated replica of the production environment to test in. This habit of codifying infrastructure makes it disposable, reproducible, and easy to manage.
The reason your deployments are failing is because of a lack of automated testing.
A team had a fast CI/CD pipeline that could deploy their code to production in minutes. The only problem was that about 20% of their deployments introduced a critical bug and had to be rolled back. They were deploying faster, but they were also breaking things faster. The reason? Their pipeline had no automated testing suite. By adding a robust set of unit, integration, and end-to-end tests to their pipeline, they ensured that bugs were caught before they reached production.
If you’re still SSHing into servers to deploy code, you’re losing velocity.
A developer finished a new feature. To deploy it, he had to get access credentials, manually log in to three different servers via SSH, pull the latest code, and restart the services. The process was slow, error-prone, and not repeatable. His colleague on a different team finished a feature, created a pull request, and once it was approved, a CI/CD pipeline automatically handled the entire deployment process. While the first developer was still logging into his second server, the second developer’s feature was already live in production.
Databases
Use the right database for your data model (SQL vs. NoSQL), not a one-size-fits-all approach.
A startup was building a social network. Caught up in the hype, they chose a NoSQL document database for everything. It worked well for user profiles and posts, which fit the document model perfectly. But when they tried to build a feature showing the complex relationships between users (friends of friends), their queries became slow and incredibly complex. They realized that for that specific, graph-like data, a relational (SQL) or a graph database would have been a much better tool for the job.
Stop doing manual database schema migrations. Do use a database migration tool instead.
A development team managed their database schema changes manually. Before each deployment, a developer had to run a specific SQL script against the production database. This often led to disaster: someone would run the wrong script, or run the same script twice, or forget a script entirely, breaking the application. They finally adopted a database migration tool like Flyway or Alembic. Now, schema changes are written as versioned files, and the tool automatically applies the correct changes in the correct order, making their database migrations reliable and repeatable.
The #1 hack for optimizing your database queries that will surprise you.
The most powerful hack is often not rewriting the query, but simply adding the right index. A web application had a dashboard that was taking over 30 seconds to load. The developers spent days trying to optimize the complex SQL query. A database administrator looked at it for five minutes, saw that the query was frequently filtering on a non-indexed column, and ran a single CREATE INDEX command. The dashboard loading time dropped from 30 seconds to 200 milliseconds.
The biggest lie you’ve been told about NoSQL databases being “schemaless”.
The lie is that “schemaless” means you don’t have to think about your data structure. A team using a MongoDB database threw their data in without any planning, enjoying the freedom from rigid SQL schemas. As the application grew, their data became an inconsistent mess. Some user documents had an “email” field, others had “email_address,” and some had none at all. Their application code had to handle all these possibilities, becoming incredibly complex. A NoSQL database is schemaless, but your application still needs a schema. You’ve just moved the responsibility from the database to your code.
I wish I knew this about database indexing when my application was crawling to a halt.
When I built my first major application, the performance was great. But as the user table grew from a thousand rows to a million, every page that searched for a user became incredibly slow. My app was doing a “full table scan,” meaning it was looking through every single row to find the ones it needed. I wish I had known that a database index works like the index in the back of a book. It lets the database jump directly to the data it needs instead of searching from the beginning. Adding one index changed everything.
I’m just going to say it: For most applications, a relational database is still the right choice.
The tech world loves to talk about the scalability and flexibility of NoSQL databases. But for the vast majority of applications—which have structured data and require transactional consistency (like an e-commerce store or a booking system)—a traditional relational database like PostgreSQL is a better, safer, and more reliable choice. The technology has been battle-tested for decades and excels at ensuring data integrity. Chasing the latest NoSQL trend when you have relational data is often a case of premature optimization.
99% of developers make this one mistake when writing database queries.
The most common mistake is using SELECT * to retrieve data. A developer was building a feature that just needed to display a user’s name. But his query, SELECT * FROM users WHERE id = 1, fetched all 50 columns for that user, including their bio, profile picture blob, and other large data fields. This wasted network bandwidth and database resources. By changing the query to SELECT name FROM users WHERE id = 1, he retrieved only the data he needed, making the application faster and more efficient.
This one small action of using an ORM (Object-Relational Mapper) will change the way you interact with your database forever.
A developer was tired of writing repetitive and error-prone SQL queries by hand. She was constantly concatenating strings to build queries and manually mapping the results back to objects in her code. She decided to use an ORM like SQLAlchemy or TypeORM. Suddenly, she could interact with her database using the natural objects and methods of her programming language. The ORM handled the SQL generation and result mapping automatically, making her code cleaner, safer from SQL injection, and much faster to write.
The reason your database is slow is because you’re not using connection pooling.
A popular web application was suffering from performance issues under heavy load. The reason? For every single database query, the application was opening a new connection to the database, performing the query, and then closing the connection. Establishing a database connection is a very slow and resource-intensive process. By implementing a connection pool, the application could maintain a set of open connections that were reused for subsequent requests. This small change dramatically reduced latency and allowed the database to handle a much higher load.
If you’re still storing passwords in plain text in your database, you’re losing all your users’ data.
A small company stored all its user passwords in a database table as plain text. They suffered a data breach, and an attacker downloaded the entire user table. The attacker now had the email and password for every single user, many of whom reused that same password on other websites. Storing passwords in plain text is unforgivably negligent. Modern security standards require using a strong, one-way hashing algorithm like bcrypt, which makes it computationally infeasible for an attacker to recover the original passwords even if they steal the database.
APIs
Use a well-defined API design-first approach, not a code-first approach.
A team built an API by just writing the code and then generating the documentation from it. The result was an inconsistent and confusing API. Endpoints for similar resources had different naming conventions and data structures. For their next project, they used a design-first approach. They used a specification like OpenAPI to define the entire API contract before writing a single line of code. This forced them to think through the design, leading to a much more consistent, predictable, and user-friendly API for their consumers.
Stop doing SOAP. Do use REST or GraphQL for your new APIs instead.
An enterprise company needed to build a new API to expose data to a mobile app. Their internal standard was SOAP. The mobile developers were horrified. SOAP’s rigid, XML-based structure was verbose and difficult to work with on mobile devices. A competing team used a modern RESTful API with lightweight JSON. It was far easier to consume and much more efficient over mobile networks. For modern applications, protocols like REST and GraphQL, which are built for the web and prioritize developer experience, are a much better choice than legacy protocols.
The #1 secret for building scalable and maintainable APIs.
The secret is to keep them stateless. This means that every request from a client to the server must contain all the information needed to understand and process the request. The server should not store any information about the client’s session state. A team built a stateful API where the server remembered which user had logged in. This made it impossible to scale horizontally, because a user’s subsequent requests had to go to the exact same server. By making the API stateless (using tokens for authentication), any server could handle any request, making the system incredibly scalable and resilient.
The biggest lie you’ve been told about microservices.
The biggest lie is that microservices will solve all your problems and make your architecture simpler. A company with a relatively simple monolithic application decided to “modernize” by breaking it into 30 different microservices. They didn’t solve their architectural problems; they traded them for a whole new set of complex issues: network latency, distributed data management, service discovery, and complex deployment orchestration. Microservices are a powerful pattern for very large, complex systems, but for many applications, they introduce far more complexity than they solve.
I wish I knew this about API versioning when I had to support multiple legacy clients.
I launched the first version of my public API, and it was a success. A few months later, I wanted to improve it, so I made some “breaking changes” to the data structure. The next day, I was flooded with angry emails. I had broken the applications of every single one of my existing users. I wish I had known about proper API versioning from the start. By including a version number in the URL (e.g., /api/v2/users), I could have introduced the new version while still supporting the old one, allowing my users to migrate at their own pace.
I’m just going to say it: Most public APIs have terrible documentation.
A developer was excited to integrate with a new, popular API. He went to their documentation page and found a disaster. The examples were outdated, the explanations were cryptic, and half of the endpoints weren’t even listed. He spent the next three days just trying to make a single successful API call through trial and error. This is incredibly common. Companies spend millions building their APIs and then treat the documentation as an afterthought. An API without good documentation is like a car without a steering wheel; it’s useless.
99% of developers make this one mistake when designing their API endpoints.
The most common mistake is using verbs in their API endpoint URLs. A developer created endpoints like /getAllUsers or /createNewPost. This approach is not RESTful and leads to a confusing proliferation of endpoints. A better, RESTful approach is to focus on the resources (nouns) and use the HTTP methods (verbs) to specify the action. The same functionality would be achieved with cleaner endpoints: GET /users and POST /posts. This makes the API more intuitive, predictable, and consistent.
This one small action of providing clear error messages in your API will change the developer experience forever.
A developer was working with an API that, when something went wrong, would just return a generic 500 Internal Server Error message. It was impossible to debug. He had no idea if the error was due to bad input, an authentication failure, or a problem on the server. A well-designed API, in contrast, provides clear, actionable error messages. A response like {“error”: “Invalid API key provided”} or {“error”: “Parameter ‘user_id’ is required”} instantly tells the consuming developer what they did wrong, saving them hours of frustration.
The reason your API is unreliable is because you have no rate limiting.
A company launched a new public API without any rate limiting. One of their users accidentally wrote a script with an infinite loop that called the API thousands of times per second. This single user’s script consumed all the server’s resources, causing the API to become slow and unresponsive for every other user. By implementing a simple rate limit—for example, allowing only 100 requests per minute per user—they could have protected their API from both accidental and malicious abuse, ensuring it remained stable and available for everyone.
If you’re still not documenting your APIs, you’re losing your developers’ time.
A new developer joined a company and was tasked with working on a project that consumed several internal APIs. She asked where the documentation was and was told, “There isn’t any, just look at the code.” She spent her first two weeks just trying to understand what the APIs did and how to call them, constantly interrupting other senior developers to ask questions. This massive waste of time could have been prevented by using a tool like Swagger or OpenAPI to generate clear, interactive documentation.
Testing
Use a balanced testing pyramid, not just end-to-end tests.
A team relied almost exclusively on end-to-end (E2E) UI tests for their quality assurance. These tests were slow to run, brittle (they broke with minor UI changes), and difficult to debug. Their testing process was a huge bottleneck. They learned about the testing pyramid and shifted their strategy. They wrote many fast, simple unit tests, a good number of integration tests, and only a few critical E2E tests. This balanced approach gave them faster feedback, more reliable tests, and higher overall confidence in their releases.
Stop doing manual testing for every release. Do automate your regression tests instead.
Before every release, a team of QA engineers would spend two full days manually clicking through every feature of the application to make sure nothing had broken. This “regression testing” was slow, tedious, and prone to human error. They decided to automate this process. They wrote a suite of automated tests that could cover the same ground in 15 minutes. This freed up the QA engineers to focus on more valuable exploratory testing, and it allowed the team to release new features with confidence at any time.
The #1 secret for writing effective unit tests that find real bugs.
The secret is to test the behavior, not the implementation. A developer wrote a unit test that asserted that a specific private method was called with specific arguments. The next day, he refactored the code to improve its performance, renaming the private method. The application still worked perfectly, but his test broke. An effective unit test treats the code as a “black box.” It provides an input and asserts that the correct output is produced, without making any assumptions about how the code achieves that result.
The biggest lie you’ve been told about 100% code coverage.
The biggest lie is that 100% code coverage means your code is 100% tested and bug-free. A team was obsessed with reaching this metric. They wrote tests that executed every line of code but had no meaningful assertions. For example, they would call a function and not even check if the return value was correct. They had 100% coverage, but their tests were useless. Code coverage is a useful tool to find untested parts of your code, but it is a terrible measure of test quality.
I wish I knew this about the difference between mocking and stubbing when I started writing tests.
When I first started writing tests, I used the terms “mock” and “stub” interchangeably. I created complex “mock” objects that had pre-programmed answers (stubs) and also verified that they were called correctly (mocks). My tests were complicated and brittle. I wish I had known the simple distinction: a stub provides a canned answer to a function call, while a mock is an object that you use to verify interactions. Understanding this difference helped me write much simpler, more focused, and more maintainable tests.
I’m just going to say it: Test-driven development (TDD) is not for everyone.
TDD, the practice of writing tests before you write the implementation code, is often preached as the one true way to write quality software. For some developers and for some types of problems (like building a well-defined algorithm), it’s a fantastic discipline. But for others, especially when exploring a new problem or prototyping a user interface, the rigid “red-green-refactor” cycle can stifle creativity and slow down the initial discovery process. TDD is a powerful tool, but it’s not a silver bullet, and it’s okay if it doesn’t fit your workflow.
99% of developers make this one mistake when writing their tests.
The most common mistake is writing tests that are not independent. A developer wrote a suite of tests where one test would create a user in the database, and a subsequent test would modify that same user. This worked fine when he ran the whole suite. But when he tried to run the second test in isolation, it failed because the user didn’t exist. Each test should be completely self-contained. It should set up its own data and clean up after itself, ensuring that it can be run in any order and at any time.
This one small habit of writing tests before you write code will change the quality of your software forever.
A developer was tasked with building a complex pricing calculation engine. Before writing a single line of implementation code, she wrote a series of tests that defined all the expected behaviors: “a standard user should be charged $10,” “a premium user with a coupon should be charged $5,” etc. This habit forced her to think through all the edge cases and requirements upfront. The tests became her specification. As she wrote the implementation code, she could instantly verify its correctness, leading to a much more robust and well-designed system.
The reason your tests are flaky is because they are not independent.
A team’s CI/CD pipeline was constantly failing because of “flaky” tests—tests that would sometimes pass and sometimes fail for no apparent reason. The cause was a shared state. One test was adding an item to a global shopping cart object, and another test was asserting that the cart was empty. Depending on the order the tests ran in, the second test would either pass or fail. By ensuring every test started with a clean state and didn’t interfere with others, they eliminated the flakiness and made their test suite reliable.
If you’re still not writing any tests, you’re losing confidence in your code.
A developer worked on a legacy project with zero automated tests. Every time she had to make a change, no matter how small, she was terrified. She had no way of knowing if her change would break something else in a completely different part of the application. The only way to verify was through slow, manual testing. This lack of a safety net led to a culture of fear, where developers were hesitant to refactor or improve the code. Automated tests are not just about finding bugs; they provide the confidence to change and improve code without fear.
Open Source
Use open source software to accelerate your development, not reinvent the wheel.
A startup needed to add a charting feature to their application. One of their developers, wanting to prove his skills, decided to build a complete charting library from scratch. He spent two months on it. A competing startup needed the same feature. They found a well-maintained, popular open source charting library and integrated it into their application in two days. They were able to focus their development efforts on their unique business logic, not on solving a problem that had already been solved by the open source community.
Stop doing passive consumption of open source. Do contribute back to the community instead.
A developer used a small open source library in all of her projects. One day, she found a small bug in it. Instead of just working around it in her own code, she took an hour to fix the bug in the library, write a test for it, and submit a pull request to the original author. Her contribution was accepted, and she not only improved the tool for herself but for every other developer who used it. This shift from being a passive consumer to an active contributor is the lifeblood of the open source community.
The #1 secret for getting your open source project noticed.
The secret isn’t having the most brilliant code; it’s having excellent documentation and a clear README.md file. A developer created a fantastic new open source tool but didn’t document it well. Nobody used it because nobody could figure out how to. Another developer created a simpler tool but spent a whole day crafting a beautiful README with a clear explanation of what the project does, how to install it, and a simple “Getting Started” example. His project got hundreds of stars because he made it easy for others to understand and use.
The biggest lie you’ve been told about open source being “free”.
The lie is that open source software is “free” as in “free lunch.” While you don’t have to pay a license fee, it’s not without cost. Using an open source library means you are now responsible for it. You have to manage its updates, deal with any security vulnerabilities that are discovered, and handle any breaking changes. A company built their entire platform on an open source project that was later abandoned by its maintainer. They were forced to either invest significant resources to maintain it themselves or undertake a costly migration. The cost is not in dollars, but in responsibility.
I wish I knew this about open source licensing when I started my first project.
When I started my first open source project, I didn’t include a license file. I just put my code on GitHub. I didn’t realize that without a license, the code is technically proprietary, and nobody else can legally use, copy, or modify it. Later, a company wanted to use my project, but their legal team wouldn’t let them because of the missing license. I wish I had known that adding a simple LICENSE file with a standard license like MIT or Apache 2.0 is one of the most important first steps for any open source project.
I’m just going to say it: The open source community can be toxic at times.
A new developer, excited to make her first contribution, submitted a pull request to a popular project. Instead of constructive feedback, she was met with harsh, dismissive comments from one of the maintainers. She was so discouraged that she didn’t try to contribute again for over a year. While the open source community is built on collaboration and can be incredibly welcoming, it’s also important to acknowledge that, like any large group of people, it has its share of gatekeeping and toxic behavior. Finding supportive and well-moderated projects is key.
99% of developers make this one mistake when using an open source library.
The most common mistake is failing to check the library’s health and maintenance status. A developer found an open source library that perfectly solved his problem. He integrated it into his production application. A few months later, a critical security vulnerability was discovered in the library. He went to the project’s GitHub page and found that it hadn’t been updated in three years and the maintainer was unresponsive. He was now stuck with a vulnerable piece of code in his application. Always check for recent commits, open issues, and an active maintainer before adopting a dependency.
This one small action of opening a well-written issue will change the way you get help from the open source community forever.
A developer encountered a bug in an open source tool. His first instinct was to file an issue that just said “It doesn’t work.” He got no response. He tried again. This time, he created a well-written issue. He included the exact version he was using, a minimal, reproducible example of the code that caused the bug, what he expected to happen, and what actually happened. A maintainer responded within an hour and fixed the bug the next day. A good bug report is a gift to an open source maintainer.
The reason your open source contribution was rejected is because you didn’t follow the contribution guidelines.
A developer spent a weekend adding a major new feature to her favorite open source project. She was very proud of her work and submitted a pull request. It was immediately closed by a maintainer with a link to the CONTRIBUTING.md file. She hadn’t followed the project’s coding style, she hadn’t added any tests, and she hadn’t discussed the new feature in an issue first. Most established projects have clear guidelines for contributions. Reading and following them is the first and most important step to getting your contribution accepted.
If you’re still not using open source software, you’re losing a massive competitive advantage.
Two companies set out to build a new software product. Company A decided to build everything from scratch, including their own web framework, database, and testing tools. Company B built their product on top of a foundation of high-quality, battle-tested open source software like Linux, PostgreSQL, Django, and React. Company B was able to launch their product in a quarter of the time and with a fraction of the budget because they stood on the shoulders of giants. In the modern world, not leveraging open source is like insisting on forging your own nails before you build a house.
Developer Productivity
Use your keyboard for everything, not your mouse.
A junior developer was constantly switching between his keyboard and his mouse. He would type a line of code, then reach for the mouse to click on a menu item, then move back to the keyboard. His senior colleague, in contrast, never touched his mouse. He navigated his code, ran tests, and managed his files using keyboard shortcuts in his IDE. The senior developer was twice as fast because he eliminated the thousands of small, time-wasting context switches that come from moving your hand back and forth.
Stop doing context switching. Do use time blocking to focus on deep work instead.
A developer’s day was a constant stream of interruptions: Slack messages, emails, meeting reminders. He would try to work on a complex problem, but every few minutes, a notification would pull him out of his focus. It would then take him 15 minutes to get back into the zone. He started using time blocking. He blocked off a 3-hour “deep work” session on his calendar, closed his email and Slack, and put on headphones. In those three uninterrupted hours, he accomplished more than he had in the entire previous day of fragmented work.
The #1 hack for mastering your IDE that will make you a 10x developer.
The secret is to learn one new shortcut or feature every single day. A developer felt overwhelmed by all the features in his IDE. So he made a small commitment: every morning, he would take five minutes to learn and practice one new thing, like the shortcut for multi-cursor editing or how to use the interactive debugger. Over a few months, these small, incremental improvements compounded. He was soon navigating and manipulating code with a speed and fluency that made him incredibly productive.
The biggest lie you’ve been told about the “hustle culture” in tech.
The biggest lie is that working 80 hours a week and sleeping under your desk is a badge of honor and a requirement for success. A startup celebrated this “hustle culture.” Their developers were constantly burned out, and the code they produced late at night was often sloppy and full of bugs. A competing startup insisted on a sustainable 40-hour work week and encouraged employees to have a life outside of work. Their developers were happier, more creative, and produced higher-quality work because they were well-rested and focused.
I wish I knew this about the importance of taking breaks when I was a junior developer.
As a junior developer, when I got stuck on a bug, I would just stare at the screen for hours, getting more and more frustrated. I thought that pushing through was a sign of dedication. I wish I had known that the best way to solve a hard problem is often to walk away from it. After taking a 15-minute walk, I would often come back to my desk and see the solution instantly. Your brain continues to work on the problem in the background, and stepping away allows you to come back with a fresh perspective.
I’m just going to say it: You don’t need to work 80 hours a week to be a successful developer.
The tech industry often glorifies long hours and “crunch time.” But study after study has shown that productivity plummets after about 40-50 hours of work per week. A developer who works 80 hours is not doing twice the work of someone who works 40 hours. They are likely making more mistakes, writing lower-quality code, and burning themselves out. Success as a developer is about the quality and impact of your work, not the number of hours you spend in a chair.
99% of developers make this one mistake with their development environment setup.
The most common mistake is not investing time to learn and customize their tools. A developer used the default settings for his operating system, terminal, and code editor for years. He was constantly fighting his tools, performing repetitive tasks manually. His colleague spent a few hours setting up dotfiles, aliases, and plugins that automated his workflow. This small, one-time investment paid for itself hundreds of times over, as he could now perform common tasks with a single command, saving him time and mental energy every single day.
This one small action of automating your repetitive tasks will change your productivity forever.
A developer’s workflow involved a series of repetitive command-line tasks: pulling the latest code, running a linter, starting a local server, and running tests. He would type these same five commands dozens of times a day. He took ten minutes to write a simple shell script that performed all five tasks with a single command: ./start-work. This tiny bit of automation saved him from thousands of keystrokes and mental context switches, freeing up his brainpower to focus on the actual work of writing code.
The reason you’re not productive is because you’re constantly distracted by notifications.
A developer had notifications enabled for everything: email, Slack, Twitter, and his calendar. His screen was a constant barrage of pop-ups and badges. He felt busy, but he wasn’t making any progress on his main project. Each notification was a small interruption that broke his concentration. He finally took control. He turned off all non-essential notifications and set specific times to check his email and messages. By reclaiming his focus, he was able to enter a state of “flow” and dramatically increase his actual output.
If you’re still not using a version control system, you’re losing your mind.
A freelance developer was working on a project and kept copies of her code in folders named project_v1, project_v2, project_final, and project_final_for_real_this_time. When a client asked for a change that was in an older version, it was a nightmare to find and merge the code. She finally learned Git. Now, with a version control system, she had a complete, chronological history of every change she had ever made. She could experiment freely, create branches for new features, and collaborate with others without fear of losing her work.
Software Architecture
Use an evolutionary approach to architecture, not a big upfront design.
A team spent the first six months of a new project in meetings, creating a massive, detailed architectural document for a system they hadn’t even started building yet. By the time they started coding, the business requirements had already changed, and their beautiful design was obsolete. A different team started with the simplest possible architecture that would solve the immediate problem. As the system and requirements evolved, they would refactor and adapt the architecture in small, incremental steps. Their evolutionary approach resulted in a system that was better aligned with the actual business needs.
Stop doing monolithic architectures for large systems. Do use a modular or microservices architecture instead.
A successful e-commerce company’s website was a single, large “monolith.” The entire application was one big unit. A small change to the shipping module required the entire application to be re-tested and re-deployed, which was slow and risky. They decided to break the monolith apart into smaller, independent services (e.g., a product catalog service, a shopping cart service, an order service). Now, the team responsible for the shopping cart could deploy changes independently, without affecting the rest of the system, dramatically increasing their speed and agility.
The #1 secret for designing a scalable and resilient software architecture.
The secret is to assume that everything will fail. Don’t design for the “happy path”; design for failure. A system was designed with the assumption that the database would always be available. When the database had a brief outage, the entire system crashed. A resilient architecture anticipates failure. It uses patterns like retries, circuit breakers, and fallbacks. When the database is down, a resilient system might temporarily serve cached data or show a friendly error message, gracefully degrading its functionality instead of crashing completely.
The biggest lie you’ve been told about “clean architecture”.
The biggest lie is that “Clean Architecture” (or similar patterns like Hexagonal Architecture) is a rigid set of rules that you must follow precisely. A developer became obsessed with creating perfect layers of abstraction for a simple CRUD application, resulting in a massively over-engineered system that was difficult to navigate. The true purpose of these architectural patterns is not to create layers for the sake of layers. It’s about a single principle: separating your core business logic from external concerns like the database, the UI, or third-party frameworks.
I wish I knew this about Conway’s Law when I was designing my first software system.
When I was a junior architect, I designed a beautiful, tightly-integrated monolithic application. The problem was that the company was structured into three separate, independent teams. According to Conway’s Law, which states that organizations design systems that mirror their communication structures, this was destined for failure. The three teams constantly stepped on each other’s toes, trying to work on the single, monolithic codebase. I wish I had known then that the best architecture is often one that aligns with the structure of the teams who will build and maintain it.
I’m just going to say it: The perfect architecture doesn’t exist.
A software architect spent months trying to design the “perfect” architecture for a new system. He was paralyzed by choice, constantly worrying about picking the wrong technology or pattern. He wanted an architecture that was infinitely scalable, perfectly secure, and incredibly simple. This is impossible. Every architectural decision is a trade-off. Choosing microservices increases complexity but improves scalability. Choosing a monolith is simpler but harder to scale. The goal is not to find a perfect architecture, but to choose the set of trade-offs that are best suited to your specific business and technical constraints.
99% of architects make this one mistake when designing a new system.
The most common mistake is premature optimization. An architect was designing a new internal blogging platform. Worried that it might one day need to serve a billion users, he designed a hugely complex, globally distributed system using a dozen different technologies. The reality was that the system would only ever have about 200 users. All of that complexity was completely unnecessary and made the system expensive and difficult to maintain. The best approach is to design for your current and near-future needs, not for a hypothetical future that may never happen.
This one small action of creating architecture decision records (ADRs) will change the way you document your architectural choices forever.
A new developer joined a team and asked why they had chosen to use a specific database. Nobody could remember. The decision had been made two years ago, and the people involved had left the company. The team started using Architecture Decision Records (ADRs). For every significant architectural choice, they would write a short markdown file that documented the context, the decision made, and the consequences. This small habit created an invaluable historical record, making it easy for anyone to understand the “why” behind the system’s design.
The reason your architecture is so complex is because you’re over-engineering it.
A team was tasked with building a simple data-entry application. They ended up building a system with a message queue, a distributed cache, an event sourcing pattern, and a microservices architecture. They were solving problems they didn’t have. The application was incredibly complex to build, deploy, and maintain. The reason for this complexity was simple: they were over-engineering. A much simpler, single-application architecture would have met all the requirements and been delivered in a fraction of the time.
If you’re still not thinking about the non-functional requirements of your system, you’re losing its usability.
A team built a new application that had all the features the business asked for. It was functionally perfect. But when it was deployed, the users hated it. It was incredibly slow (performance), it crashed frequently (reliability), and it was difficult to use (usability). The team had focused entirely on the “functional requirements” (what the system does) and completely ignored the “non-functional requirements” (how the system does it). A successful architecture must treat these “-ilities” as first-class citizens, not afterthoughts.