Unless you’ve been completely disconnected from recent tech advancements, you’re likely aware of the surge in generative artificial intelligence systems (GAIS) like ChatGPT, Stable Diffusion, Bard, Claude, and Midjourney. These systems, at their core, comprehend text and generate new content in forms of text, computer code, images, or videos. The current state-of-the-art in GAI leverages humanity’s collective works, introducing a new era of content analysis and generation. The quality of results is determined by the clarity and quality of the questions we ask and demands we place on the system. Asking poorly worded questions, omitting details or jumbling too many things into a single request will produce low quality results. On the contrary, learning to adapt requests so the system can shine will produce stunning results that were once thought only possible in science fiction. This leads to an intriguing question: How can we apply the strategies for maximizing outputs from these AI systems to enhance our human interactions and experiences?

I always thought it was kind of cool to stumble on a Manager README file from a “successful manager”. It gave me insight into their thought process and with that I hope to find something that can help me level up my own skills. I had a decision to make when I became an official manager - is a Manager README something I want to create for myself? With hindsight as a friend, the answer is a resounding Heck Yeah! My Manager README is published here. But is authoring a Manager README the correct decision for you?

Roger joined our team as a strong software engineer with valuable experience and lofty expectations from our team. We had every reason to believe Roger would be a great hire. And from the very beginning, Roger struggled to accomplish any work assignment. When I checked his work history I found that it took him forty-seven days to complete his first change in our codebase. Yikes. The next question I pondered was “how long does it take on average for a developer to complete their first pull request?” I needed a larger dataset to tell me more about the one data point about Roger. This question, and others like it, would send me down my very first rabbit hole of collecting, organizing and analyzing the data from our software engineering teams.

Are you struggling to break into the software engineering industry, wondering if there’s a shortcut to success? Let me tell you: there’s no easy hack or secret formula to swiftly acquire the skills needed to excel in this field. As someone who transitioned from a college graduate to a junior software engineer, I know the challenges and hurdles involved. It took time, determination, and a relentless pursuit of my passion for coding to finally achieve my dream role. The truth is, getting a foot in the door and securing that job offer is about more than just qualifications – it’s about proving your capabilities and demonstrating your skills effectively.

In the dynamic realm of software engineering, orchestrating a team towards cohesive productivity is both an art and science. It’s a landscape filled with intricate tasks, demanding precision and an eye for detail. As a software engineering manager, my journey through this world has been a constant endeavor to ensure the team’s efficiency. However, I’ve often found myself pondering one crucial question: How do we get the team to do the things we need them to do? This inquiry has led me to a simple yet profound realization: the path to success isn’t just in commanding action but in crafting an environment where the correct choices are the effortless ones. How do we achieve this in a field where tunnel vision is common, and the minutiae can eclipse the bigger picture? How do we transform good intentions into seamless, habitual practices? How can we guide our teams to not just hear but embody the unspoken language of action?

I’ve never been a complete believer in the purist form of the Test Driven Development (TDD) approach where developers first write failing tests and then the functional code to achieve passing tests. Instead, I opt for a more practical and pragmatic mantra: “write the code and tests at approximately the same time.” Write code, add tests for it, refactor the code, update the tests, write more code, write more tests, refactor the tests, refactor the code, etc. It’s a practical paradigm that aligns with the dynamic nature of development. It’s the 80/20 rule in action. Let’s get most of the value from the process and minimize the headache.

As a software engineering manager and hiring manager, I’ve had the privilege of evaluating countless candidates for various technical positions. Over the years, I’ve gathered valuable insights and tips for success in interviews. In this blog post, I’ll share these insights with you, the students of a software engineering bootcamp, to help you prepare effectively and increase your chances of landing that dream job.

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Are you ready to crack the code to secure that dream job interview? From setting your salary expectations to acing the phone screen, this comprehensive guide unveils the key strategies to maximize your chances of landing that coveted interview. Whether you’re revamping your LinkedIn profile or navigating the application process, discover the essential steps to stand out and seize the opportunities waiting for you. Get ready to take charge of your career destiny and nail that next big interview!