Inspector’s Post

To craft software which is most useful, programmers need to model the world as it is, not as they wish it to be. Find the natural joints and behaviors in the world we are modeling and decompose it into actors and objects which behave in defined ways. This practice of decomposing our problem space by behavior, rather than data or routines, makes our software more user friendly and easier to extend and maintain for many generations. This doesn't mean we'll never refactor or rewrite components of the system. To the contrary, we'll rewrite more often as our problem domain changes, because it will be easier to do so without starting over from scratch

2024 is the year of DAG. Directed Acyclic Graphs (DAGs) is a MUST KNOW principal (for what I would argue pretty much all developers) and powerful design pattern that transcends from application state management, especially in program and web design to machine learning to distributed file systems. By clearly defining tasks and their dependencies as nodes and edges, DAGs help ensure that different parts of an application can run in parallel, boosting efficiency and flexibility. In machine learning, DAGs organize complex workflows, making it easier to manage data flow and model training stages. DAGs help state management in apps by clearly defining the flow of data and dependencies between tasks, ensuring that each state transition occurs in clean and clear order without conflicts. Overall, this structured design strategy enhances clarity and performance and is intuitive by nature, making DAGs essential for modern software development.

Survey shows documentation is the No. 1 way that developers learn about your API

🚀 𝗨𝗻𝗹𝗼𝗰𝗸 𝗣𝗲𝗮𝗸 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗳𝗼𝗿 𝗧𝘂𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻! 🚀 Is your application running slower than you'd like? Performance tuning and optimization are crucial for delivering high-quality, efficient software. Whether you're a developer, architect, or IT professional, mastering these best practices can significantly enhance your projects. 🔧 𝗧𝗼𝗽 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗧𝘂𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: 𝗣𝗿𝗼𝗳𝗶𝗹𝗲 𝗬𝗼𝘂𝗿 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Identify bottlenecks using profiling tools. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗖𝗼𝗱𝗲: Refactor inefficient code and leverage asynchronous programming. 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Indexing, query optimization, and proper use of transactions. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Reduce memory leaks and optimize garbage collection. 𝗖𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘀𝗺: Utilize multi-threading and asynchronous operations. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Efficient use of CPU, memory, and I/O resources. 💼 𝗪𝗵𝘆 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲? 𝗨𝘀𝗲𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: Faster applications provide a better user experience. 𝗖𝗼𝘀𝘁 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Optimized applications use fewer resources, reducing costs. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: High-performance apps handle increased loads better. 📚 𝗟𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗘𝘅𝗽𝗲𝗿𝘁𝘀! Step-by-step guides on profiling and optimizing your applications. Real-world case studies highlighting common pitfalls and solutions. Practical tips to implement immediately for noticeable improvements.

😵 "Complexity kills." This is especially true in software development. Have you ever tried to add a new feature and it took weeks instead of days, causing new bugs? 😱 Let's explore coupling and how it can affect your system! - A THREAD - 📝 Example: function displayData() { const rawData = "hello"; console.log(formatData(rawData)); } function formatData(data: string): string { return data.toUpperCase(); } `displayData` depends on `formatData`. If `formatData` changes, `displayData` must change too. How can we solve the coupling? 🔄 Reduce coupling by passing dependencies as parameters: function displayData(formatter: (data: string) => string) { const rawData = "hello"; console.log(formatter(rawData)); } Now, `displayData` is more flexible and less dependent on `formatData`. 🚀 Why is this better? The refactored version allows `displayData` to work with any function that formats data. This makes it easier to change or extend functionality without modifying `displayData`. 🧐 This is just a tiny example, but there's a lot more to reveal about coupling and its impact on software design. Dive deeper to learn how to balance coupling and create maintainable systems in my blog post. https://lnkd.in/eRAx65Cm

😵 "Complexity kills." This is especially true in software development. Have you ever tried to add a new feature and it took weeks instead of days, causing new bugs? 😱 Let's explore coupling and how it can affect your system! - A THREAD - 📝 Example: function displayData() { const rawData = "hello"; console.log(formatData(rawData)); } function formatData(data: string): string { return data.toUpperCase(); } `displayData` depends on `formatData`. If `formatData` changes, `displayData` must change too. How can we solve the coupling? 🔄 Reduce coupling by passing dependencies as parameters: function displayData(formatter: (data: string) => string) { const rawData = "hello"; console.log(formatter(rawData)); } Now, `displayData` is more flexible and less dependent on `formatData`. 🚀 Why is this better? The refactored version allows `displayData` to work with any function that formats data. This makes it easier to change or extend functionality without modifying `displayData`. 🧐 This is just a tiny example, but there's a lot more to reveal about coupling and its impact on software design. Dive deeper to learn how to balance coupling and create maintainable systems in my blog post. https://lnkd.in/eRAx65Cm

Software is eating the world - so every company needs to produce their own software? This often quoted article from a16z from 2011 seems to imply that all companies need to up their software game or risk getting left behind. More than 10 years later I'd argue this has mostly been true, however we shouldn’t forget the fact that producing software is becoming more and more accessible. Gone are the days where you need IT departments with hundreds of people to get started. Companies software toolkit of the future consists of a combination of AI generated code, low code and custom code. I often hear that low code introduces too many limitations and I have seen this myself back when I was working as a corporate consultant but that can easily be avoided by combining multiple tools i.e. using a workflow tool for simple tasks and calling custom code wherever needed. My colleague Jan-David Wiederstein wrote a great article where he combines a self hosted AI, a self hosted workflow tool and custom Python code for a web crawl, summarisation and publication use case. The entire infrastructure can be set up in minutes on Codesphere. Lot's of insights in this one! Link: https://lnkd.in/dHRGmGwa

Build a lot. Do it for a long time. The biggest cause of poor reusability and bad software generally is bad assumptions. It’s including contextual information in the implementation, causing the consumer to have to find a way to work around it when your assumptions don’t match their context. It’s a deceptively hard problem to discover what is intrinsic to your problem and what is circumstantial. It’s why we have the rule of three (or five, or twelve, depending on who you ask). One use case gives you no information about this. The more experience you have the easier it is to sift out the context from the problem. Go get your data points. Then consider your abstraction. Building an abstraction before the context is removed is a recipe for un-reusable code and lots of churn. The longer you’ve been writing software, the more and varied experiences and datapoints you have to draw on. There is no substitute for this. Built a lot. Do it for a long time.

Today, I picked up "How Software Works: The Magic Behind Encryption, CGI, Search Engines, and Other Everyday Technologies" by V. Anton Spraul recommended by my Mentor Ayebakuro Oruwori, and I’m already fascinated by its insights. Chapter One breaks down the basics of software into comprehensible parts: 1. Binary System: Everything in software boils down to ones and zeros, highlighting the simplicity and universality of binary language. 2. Algorithms: Step-by-step procedures are essential for every software function, emphasising the need for efficiency and optimisation. 3. Data Storage and Manipulation: Understanding how data is stored, retrieved, and manipulated in memory is crucial, focusing on data structures and trade-offs between speed and space. 4. User Interfaces: Good software design translates complex operations into intuitive user experiences, balancing functionality with user-friendliness. 5. Real-World Applications: Connecting theory to real-world examples like search engines and encryption shows the practical impact of these principles. These insights have reshaped my perspective on software: a. Binary Simplicity: Complex systems are grounded in simple binary operations, making software more accessible. b. Algorithm Efficiency: Optimized algorithms are key to solving problems effectively. c. Data Structure Balance: Effective software design requires a balance between data structure efficiency and user interface intuitiveness. Spraul’s book has expanded my knowledge and reinforced my approach to software development, making it clear that every complex system starts with simple, fundamental building blocks. This clarity will guide me in future projects and technical challenges.

Boost developer productivity by identifying and mitigating cognitive load drivers in software development. Focus on tools, information, and work processes. #DeveloperProductivity #CognitiveLoad #SoftwareDevelopment