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Everyone's talking about agentic AI shipping production code. Nobody's talking about what happens when you actually sit down and grade thousands of lines of it against a rubric, line by line, for months. I have. And the failure pattern that shows up over and over isn't the one Twitter/X is arguing about. The job title that didn't exist two years ago "AI evaluator." "AI trainer." "Expert contributor to frontier model training data." None of these existed as job titles when I started my career. Now they're where a chunk of the most interesting engineering signal in the industry is actually happening — quietly, behind NDAs, far from the demo videos. Here's what the job actually is: agentic coding outputs land on your desk, and you grade them against a structured rubric — correctness, instruction adherence, quality, edge-case handling. You design adversarial prompts to find where the model's reasoning breaks. You decide which checks can be programmatic and deterministic, and which genuinely need a human who's shipped production systems to make the call. This is RL environment design and LLMOps in its rawest form, and it's a completely different skill from "prompt engineer" or "ML researcher." It's closer to being a QA lead for a junior engineer who never sleeps, never gets embarrassed, and will confidently ship the wrong answer with perfect syntax. The pattern: agents are great at code, bad at consequences Here's the uncomfortable part. The failure mode people are loudest about — hallucinated APIs, made-up library functions — is the easy failure mode. It's loud, it's obvious, and any decent test suite catches it in seconds. The failure mode that actually matters, the one that slips past a surface read and even past a naive test suite, looks like this: The code is syntactically perfect and semantically wrong about failure. It handles the happy path beautifully and quietly assumes the retry, the timeout, the partial write, the duplicate message never happens. It optimises

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.