标签：#productengineering

Variant Multiplier already let an editor swap one section of a winning ad and keep the rest. The next request from a real production job — replacing product SL-603 with SL-808, a different hearing-aid SKU, across an entire finished ad — was a different shape of problem. It's not "change one section," it's "change every mention of the product, everywhere it appears, while keeping literally everything else the same." Two direct quotes from the editor drove the whole five-PR arc: the transcript editing was too rigid for word-by-word changes, and separately, "the music, voice, etc. should retain the same, we should keep the quality the same, and not make it do a lot of changes." If a re-render can degrade something the editor explicitly asked to keep untouched, the render path is wrong for the job — no matter how good the model is. The cheap fix first: let editors actually edit PR #67 shipped before any product-swap work started, because it was the cheap, high-value half of the same feedback: "I am just able to select word by word here but I am not really able to change the whole sentence a lot easier," and separately, "I'm able to double click on these words and then just type it in." Both were UI gaps in the transcript editor, not pipeline gaps — selecting by sentence or scene instead of only by word, and retyping a line verbatim instead of only substituting individual words. Shipping this first, standalone, meant the harder product-swap work that followed didn't also have to carry an unrelated UX fix in its diff. A product catalog the tool never had PR #69, stacked directly on top of the transcript work, is pure groundwork with no user-visible feature of its own: a product catalog, because Variant Multiplier had no concept of "a product" at all before this. The editor's own framing made the requirement explicit: "have a product selection right here, for Pro Bluetooth, for [the other SKU], and maybe other tons of products" going forward. The catalog data itself is mai

Get ready for a bit of a ramble about looking for a job as a software engineer in 2026. No, it's not about AI changing the definition of software engineering in 2026. But there's obviously some truth in that. It's about product engineering. Specifically, it's about the challenges engineers face when searching for new opportunities because of the massive shift toward product engineering. I should preface what comes next with this: Searching for a software engineering job in 2026 is really hard. Scroll through LinkedIn or any software career blog and you'll see plenty of posts about how the recruitment system is broken, how good engineers are being ghosted, how CVs are being filtered out by AI screening for keywords. These frustrations are valid, but... you know what else is really hard in 2026? Being a software engineering recruiter. Being a software engineering hiring manager. And software engineering is about solving problems. With that said, you can't solve a problem you don't define. So to lay the foundation, I want to address some challenges I've recognised before addressing what can be done about them. The Problem Space First, the thing that's been haunting me for the last 6 months. Impact articulation . I suspect this isn't a problem that's unique to product engineering, but it's certainly one I've faced as a product engineer. Earlier this year, I completed full interview processes with two separate companies. I felt confident about both. The roles were the type of engineering I'm great at: sitting close to users, working through ambiguity and owning product areas end to end. But neither resulted in a job offer. The feedback I received was surprisingly consistent: I demonstrated strong technical execution, methodical problem-solving, clear communication and product judgement, and consistently sought to understand the "why" behind the "how". But also, I struggled to connect my product decisions to business or user outcomes. It was clear that I was a great engin

AI has made it easier to produce working software. That part is real. It can write code, draft documents, research a topic, scaffold a prototype, and debug a problem faster than most teams can finish writing a decent ticket. But faster building doesn't automatically mean better product decisions. That's the part I keep coming back to. For decades, software teams optimized around delivery. Requirements, design, development, QA, release. Waterfall softened into Agile. Agile grew into DevOps. The practices changed, but the assumption underneath stayed pretty stable: building software is expensive, so plan carefully before you start. That made sense because, for a long time, it was true. Now that assumption is breaking. AI is doing to software what calculators did to accounting. It isn't eliminating the job. It's moving the job up a level. The syntax, boilerplate, first draft, and some of the debugging are getting offloaded. The work doesn't disappear. The bottleneck moves. Learning is still expensive Here's what didn't get cheaper: understanding what people actually need getting stakeholders aligned deciding what evidence would change your mind putting something real in front of users reading the signal without fooling yourself The old question was: Can we build it fast enough? The new question is: Do we understand the problem well enough? That sounds like a small shift, but it changes the work. It changes what strong engineers spend time on. It changes what product people need from engineering. It changes how teams should define "done." If the code ships but nobody learns anything, did the team actually move forward? Sometimes yes. Often no. Users don't know until they can touch it People are not great at specifying requirements up front. Not because they're difficult. Because they're human. Most of us don't know how we feel about something until we can react to a version of it. A mockup. A prototype. A rough slice. A real workflow with sharp edges. So the fastest pat