标签：#careerdevelopment

It's conference season! I already had to sit at home jealous while friends had fun at Render ATL, but it's my turn soon with Commit Your Code next week in Plano, TX. It boasts a banger lineup of speakers, which got me thinking: how do you get the absolute most out of an event like this? The number one rule is determining your goal before you step through the doors. Are you going to hang out with friends, meet new people, or hunt for a job? Each objective requires a completely different approach, prep strategy, and attire. 1. Hanging Out with Friends This is the easiest path. Wear whatever keeps you comfortable while looking relatively professional. Meet up with your crew, enjoy the sessions, and have fun. You done did it. 2. Networking and Meeting New People This is my primary goal for CYC this year. I'll fill you in on my plan. To keep from getting overwhelmed, I built a tracking spreadsheet for everyone I want to connect with. It might sound clinical, but it ensures no follow-up slips through the cracks. Here is my process: Pre-Conference Research: First, I reviewed the talk schedule and logged the speakers and session titles that caught my eye. Initial Outreach: I added columns for sending an intro message and a LinkedIn connection request. Then I sat down and message every single one of them. I had a bit of a template, but mostly just told people why their talk sounded interesting or exciting to me. It's hard to have writers block when you have a genuine interest in something. Some of them replied, some didn't, but I already feel like I have a foot in the door heading into the conference. During & After the Event: My spreadsheet includes columns for attending their talk, taking photos (speakers always need good photos of themselves on stage), posting on social media, and sending a post-event follow-up. Sounds like a lot? Because it is! Which is why its in a spreadsheet and not my pasta strainer brain. But it's about intent, respect, and appreciating someone else

Building a working tender documentation system for yourself is one project. Turning that same system into a template the rest of the team can pick up and use correctly, without needing to ask you what a particular instruction actually means, is a completely different project wearing the same clothes. The Gap Between Personal Use And Handoff A prompt template that only you use can carry a lot of implicit knowledge safely, because the missing context lives in your head and gets filled in automatically every time you run it. An instruction that says something like ensure the response addresses compliance requirements directly means something very specific to the person who wrote it, shaped by dozens of past examples of what counting as directly actually looks like in practice. That same instruction, handed to someone on the team who was not present for any of those past examples, is just as likely to be interpreted in a way that is defensible on its own terms and still wrong relative to what was actually meant. The template worked perfectly for months before it needed to be handed off, which made the gap invisible until the moment it actually mattered. The first time someone else on the team ran it independently and produced a response that technically followed the instructions but missed the actual intent behind them, the problem was not that the instructions were poorly written in any obvious sense. It was that they had been written for an audience of one, and that audience had context nobody else on the team had access to. What Actually Needs To Be In A Handoff Ready Template Fixing this meant rewriting a significant portion of the template with a different question in mind at every step, not does this instruction produce the right output when I run it, but does this instruction contain enough of the reasoning behind it that someone without my accumulated context could apply it correctly to a new tender they have never seen before. That meant replacing instructions

I'm a few months into a job search after a layoff, and I kept running into the same two problems: I was spending too long deciding whether a job listing was worth my time, and my resume was drifting out of sync with what was actually landing in interviews. So I built two Claude Code skills , reusable, file-based instructions Claude Code follows every time I invoke a slash command, to close both gaps. This is a walkthrough of how they work, why they're structured the way they are, and what I learned building them. If you haven't used Claude Code skills before: a skill is just a markdown file with YAML frontmatter ( name and description ) that lives in .claude/skills/{skill-name}/SKILL.md . The description field is what Claude uses to decide when to trigger the skill automatically, and you can always invoke it explicitly with /skill-name . The problem Job searching produces a lot of repetitive judgment calls: Is this listing worth 20 minutes of my time? Every JD needs to be read against my actual background, not against wishful thinking. Once I've scored 30+ listings, what do they add up to? Patterns emerge: the same gap gets flagged five times, the same bullet gets written from scratch in every cover letter, but nobody's collecting those patterns into resume improvements. Two skills, one for each problem: /score-job and /resume-sharpener . They're designed to work as a pair, the first generates raw signal, the second mines it. Skill 1: /score-job Input: paste a JD or give a URL. Output: one markdown file, job-search/scored-listings/YYYY-MM-DD-{company}-{role}.md . Reading the right context every time The skill starts by reading a fixed set of source files in parallel: my resumes (I keep four: engineering, PM, FDE/presales pivot, and a PeopleSoft-specific one), a profile doc, a skills inventory, and a filters doc that encodes what counts as a disqualifier. Critically, it re-reads these every run rather than caching anything, because they evolve as I update my resume o

DISCLAIMER: This post was written entirely by me! I used AI for a little research, spelling, grammar, and comprehension checks. This post was originally shared on Hackernoon Entertain me for a moment, let's appreciate where we are today by understanding where we've been… or at least, where I've been. I remember when I first learned to code. I was bad, like really bad. But I was so curious! It all started by writing some VBA in an MS Access Database to create an IT Inventory app in the late 90s. Then I learned JavaScript, ASP (without the .Net), then C#, .Net ( I still have my .Net for Dummies book, see below) , jQuery, Python, Java, Angular, ReactJS, and Python again (yeah, had to relearn that one for some reason), and I'm sure there are others in there I forgot about. Learning to write code was rewarding! There were those days I'd spend hours on a bug, only to realize I didn't initialize the variable or forgot a semicolon. I learned .Net over a weekend thanks to the above book. I never became an artist of the craft, like some of my colleagues have (you know who you are: Josh, Kevin, and many others), but I knew how to build anything. I loved that ability: I could build anything. Pure joy! If you haven't had the joy of learning to code, do your best to learn it, because you can't prompt your way to being a Senior Engineer . As I progressed in my career, I became an architect and senior lead. I started off by leading a single engineering team, and now I support large programs and teams. All the while, I never let go of hands-on-code. I still love coding for work and my myriad of side projects. Then, a few years ago, this GenAI thing showed up. Put me in that group of: oh-no-there-goes-my-joy. Joy, yes, not my job. I love my job because I get to do what I love. I loved the dramatic rollercoasters: architecting a perfect solution, realizing it's wrong, getting to write every line of code, chasing impossible bugs, panicking with deadlines, late nights chasing hot fixes,

We talk a lot about what AI can build. Code generation. Faster prototypes. Automated debugging. One-shot apps. Entire products created in hours. And yes, AI is powerful. But there is a quieter cost we are not talking about enough: AI hype is starting to weaken the motivation to learn core engineering deeply. That should worry us. 1. The "Why Bother?" Mindset When the dominant narrative says AI can generate code instantly, many engineers start asking: Why should I spend months mastering frameworks, architecture, databases, networking, or system design? At first, that sounds practical. If a tool can help, why not use it? But there is a difference between using AI to move faster and using AI to avoid understanding. Core engineering is not just about writing code. It is about knowing why something works, where it breaks, how it scales, and how to fix it when the generated answer is wrong. If we skip that learning, we create engineers who can prompt systems but cannot reason deeply about systems. That is a dangerous tradeoff. 2. The Funding and Praise Monopoly Right now, AI gets most of the attention. Budgets move toward AI. Leadership praises AI initiatives. Teams are pushed to add AI features even when the fundamentals are still weak. Meanwhile, excellent core engineering often goes unnoticed. The people improving reliability, performance, developer experience, infrastructure, security, and maintainability are still doing high-impact work. But in many places, that work is being treated as less exciting simply because it is not branded as AI. This creates pressure. Engineers feel they must pivot to AI, not always out of interest, but out of fear. Fear of being left behind. Fear of being replaced. Fear that their existing expertise is no longer valued. That is not innovation. That is anxiety disguised as progress. 3. The "AI-First" Discount There is another subtle problem. When someone builds something impressive today, the reaction is often: AI probably generated that.

I got fired from my first job, took down a database server with a badly written query, and was...

There is a special kind of productivity theater that happens during a developer job search. You wake up motivated, open LinkedIn, and apply to 27 positions before breakfast. You press the Easy Apply button with the precision of a professional gamer. By the end of the week, you have submitted 143 applications, updated a spreadsheet with several impressive numbers, and developed a minor emotional dependency on refreshing your inbox. Unfortunately, your inbox still looks like an abandoned shopping mall. No interviews. No useful feedback. No clear explanation. Perhaps two automated emails thanking you for your interest before informing you that the company decided to “move forward with other candidates,” a sentence that has become the corporate version of disappearing into the fog. So you decide to solve the problem by applying to another 200 jobs. This is not a strategy. It is email-based agriculture. You are throwing resumes into the soil and waiting for a recruiter to grow. Volume Matters. Blind Volume Does Not. Let us begin with an uncomfortable truth: getting your first developer job usually requires applications. Sometimes it requires many applications. The market will not discover your GitHub profile through divine intervention. A recruiter is unlikely to wake up in the middle of the night with a mysterious urge to search for junior developers who recently deployed a to-do list. You need to put yourself in front of companies consistently. However, there is a significant difference between applying consistently while improving your positioning and clicking every blue button on LinkedIn until one of you collapses. Volume is useful when it generates information. Blind volume only produces exhaustion. If you apply to 300 jobs with the same generic resume, the same generic portfolio, and the same vague explanation of your skills, you are not running 300 experiments. You are repeating the same experiment 300 times and acting surprised when the result remains unchanged.