Career on PHP Boy Scout

The campsite was never the point

Thu, 18 Jun 2026 00:00:00 +0000

I named myself, professionally, after a rule about litter.

The Boy Scout Rule is the one every camp drills into you: leave the campsite cleaner than you found it. Robert Baden-Powell’s version, in the last message he left for Scouts to be found among his papers after he died, was tidier and bigger: “try and leave this world a little better than you found it”. But the campsite is where a child first meets it. Ten years ago I stood up at a conference in a Scout uniform and argued that the same rule runs a codebase: leave the code better than you found it, every time you touch it. I still think I was right. I’ve just spent the decade since learning that the rule is bigger, and a good deal harder, than the tidy version I was selling then.

A clarification I owe you up front: I’m not an active Scout any more. I was one as a boy and again as an adult leader, and a few years ago I stepped back to put my energy elsewhere. The uniform’s in a box. The creed isn’t… And that, I’ve come to think, is the only real test of whether a thing was ever a value or just a rule someone was checking up on. A rule you follow while the warden’s watching is a rule. One you keep after you’ve handed back the woggle is a value. The campsite taught me the rule. Twenty-odd years of work taught me it was a value, and what that costs.

It’s the same rule, at every size

The thing people get wrong about the Boy Scout Rule is thinking it’s small. They file it next to “tidy your desk”, a nicety for when you’ve got a spare minute.

It isn’t small, and it isn’t about size at all. It scales from a single function to an entire company without changing shape, because the load-bearing part was never the litter or the lines of code. It’s the intention: that you leave a place in better order than you found it, on purpose, as a matter of course. Pick up one crisp packet on the way to the bus and the field is measurably better for the next troop. Fix one silently-failing lint rule on your way through a file and the codebase is measurably better for the next engineer. The act is trivial. The habit, held by enough people, is how a whole environment stops degrading and starts improving, quietly, without anyone running a project to do it. A campsite, a codebase, a team, a company: same rule, same intention, bigger blast radius.

Anyone can pick up litter; a leader buys a vacuum

Where it stops being a personal virtue and becomes a job is the moment you’re responsible for more than your own hands.

One scout picking up one crisp packet leaves the field a little better. But I can’t follow forty engineers around pointing at litter, and I wouldn’t want to. So the work of leadership is to stop relying on individual heroics and put a vacuum on the whole campsite: the standards that make the tidy thing the default, the automation that catches the mess before a human has to, the CI gate that won’t let the litter on the bus in the first place. On the teams I’ve run it became a mantra, that every project ticket you pick up, you also pick up something small alongside it, a CVE bump, a failed check, a confusing name. None of those was anyone’s job. All of them made the system better to work in, easier to understand, less prone to bite someone at three in the morning.

And here’s the part that surprised me, because I’d assumed the hard bit was getting people to care. It wasn’t. The scarce resource isn’t willingness, it’s confidence. Engineers see the litter. What they lack, under a deadline and the pressure coming from every direction, is the permission to bend down and pick it up, the belief that thirty minutes spent on something nobody asked for won’t be the thing they’re hauled up for. Giving people that permission, and meaning it, and backing them when they take it, turned out to be most of the job.

Then get on the bus

If I stopped there you’d have a poster, and posters are how good ideas turn into sanctimony. So here is the limit, because a rule without judgement is just yak-shaving in a woggle.

Every camp has the over-keen scout who is still deep in the bushes hunting for one last sweet wrapper while the rest of the troop has loaded the bus and the engine’s running. That scout hasn’t understood the rule better than everyone else, they’ve understood it worse. They already have a bagful; the field is already better than they found it; and now the right thing, the thing the rule is actually in service of, is to get on the bus so forty people get home. The litter stopped being the point a while ago.

It’s identical with engineers. Chasing the next improvement is a good instinct right up until it costs the team the thing that actually mattered, the release, the commitment, the colleague waiting on you. It’s a team sport, and the rule only works in the hands of a team player. I have, more than once, had to tell a good engineer to stop making something better and ship it, which feels like contradicting everything above and isn’t. Knowing when “better” is done, when the bag is full enough and it’s time to drive home, is not a betrayal of the rule. It’s the adult version of it.

The campsite was never the point

Which brings me to the thing it took me far too long to say out loud. The code is the easy half. The campsite was never the point.

“Leave it better than you found it” applies, most of all and most lastingly, to people. Every engineer I’ve helped pick up a new tool, every junior I’ve talked through a design they were afraid of, every review I treated as teaching rather than gatekeeping, that is the rule, far more than any lint fix. Code I improve decays; entropy comes for it eventually. A person I help to grow carries that forward into work I’ll never see, and teaches it to people I’ll never meet. If you want the rule to actually compound, you stop applying it to the codebase and start applying it to the people who’ll outlast your codebase. The campsite is just where you practise.

The scout who doesn’t get it

And then, occasionally, there’s the one who doesn’t get it. The scout, or the engineer, who won’t pick up the litter, who treats the whole idea as someone else’s fuss, who hands the messy work to the next person without a second thought.

Our industry has a reflexive answer to that person: route around them, manage them out, label them and move on. I’ve never been able to make that answer sit right, and I’ve stopped trying to. That person isn’t flawed. They’re not a write-off and they don’t deserve to be quietly frozen out. Far more often they’ve simply never been shown why it matters, or they’re carrying something that’s left them no room to care this week. What they need is the least scalable thing I have: time, patience, and the assumption of good faith. The same thing, now I think about it, that somebody once spent on a younger me before the rule stuck.

That’s the hardest application of the whole thing, and the one no standard and no amount of automation will ever do for you. You can put a vacuum on a campsite. You cannot automate the slow, human work of helping a person understand why they’d want to leave a place better than they found it, and that it’s worth doing for the rest of their life, long after anyone’s checking, long after the uniform’s in a box.

I stepped back from Scouting, and I kept the rule, because once upon a time someone gave me the patience to understand it rather than just obey it. Ten years on from standing up in that uniform, I’m clearer than I’ve ever been about what the badge was really for. Not the campsite. Never the campsite. The least I can do with the rule that named me is pass it on the way it was passed to me, one person at a time, and then have the good sense to get on the bus.

The rung we sawed off

Wed, 17 Jun 2026 00:00:00 +0000

I was in a job interview yesterday, on the wrong side of the desk for once. After years of being the one asking the questions I’m having a look at what’s next, and somewhere in a long, wandering technical conversation the inevitable arrived: where do I think AI is going, and what does it mean for how we build software?

I gave my answer. You can probably guess most of it. The more interesting thing was the question I’ve started asking them back. Not the salary, not the stack. What is your actual position on AI, and how are you building a team out of both its human and its non-human parts? I ask the company and I ask the interviewer personally, because the two answers are rarely the same, and because I’ve decided I can’t work somewhere that hasn’t sat with the question properly.

Here is why it has become my litmus test.

The rung, and who’s standing on it

I wrote recently that the greybeards’ edge was never typing: agentic tools give a senior a boost because they have the judgement to steer and verify, and give a junior a drag because they don’t have it yet and the machine hands them more rope than they can hold. The cold incentive that falls out is to hire seniors and automate the juniors.

The data has since caught up with the worry. Entry-level software postings have fallen by something like 40% from their 2022 peak. The share of juniors and graduates in IT employment has dropped from roughly 15% to 7% in three years, and Stanford researchers tracking early-career workers in AI-exposed jobs found the youngest cohort down sharply from its peak. The numbers are genuinely grim, and plenty of people are putting it bluntly: the industry killed the junior on purpose.

That framing is half right, and I think it’s worth getting the other half right too.

It was never about efficiency. It was about cost.

We didn’t automate the junior because the work needed doing better. We did it because people are expensive. We need sleep, we draw a salary, and our thinking takes time and effort that a quarterly target can’t see the point of. AI got sold as round-the-clock labour with none of that overhead, and to a business that is an almost irresistible line on a spreadsheet. There’s a grim irony arriving, mind: the bills are starting to land, and the same conversations that hyped the cheap labour are now quietly working out that all those tokens aren’t cheap at all.

Step back, though, and none of this is new. Man finds a shortcut, man takes a shortcut. From the industrial revolution onward, every time we found a way to get more done with less human effort we took it, and the work reshaped itself around the new tools. We are still here, still employed, just doing different things than our great-grandparents did.

What is genuinely new is what we’re automating. Every technological advance before this one automated the machinery of the body, the muscle and sinew and bone. This is the first time we have automated thinking, and that is a modern marvel, something we should be proud of as a species. The problem isn’t the marvel. It’s the rate. AI is improving faster than we can adapt to it, and adaptation is the entire game.

So where does the blame sit? Not on one logo. No single company did this, however easy Meta or Google make it to point at the latest round of cuts. Society did, our collective and very human hunger to build bigger and faster. That makes it harder to fix, because there is no villain to regulate, only ourselves to out-think.

The bit that should frighten you

Cutting the junior intake isn’t a saving. It’s occupational suicide.

A junior is not cheap labour that AI happens to have made cheaper. A junior is a senior who hasn’t happened yet. Saw off the bottom rung and for a good while nothing bad happens… because you’ve still got your seniors holding everything up. Then the greybeards retire, and I have a cabin and a woodstove with my name on it for exactly that day, and the role that used to grow their replacements has been hollowed out for a decade, and there is simply nobody left who learned to tell when the machine is wrong. That isn’t a hiring problem. It’s an existential one, and you can’t fix it retroactively.

It starts before the first job, too. We teach primary-school children the basics of programming in this country, which is a wonderful thing, except the curriculum was written for a world without AI in the room, and by the time those children reach secondary school a good deal of it will be teaching a craft that has already moved on. We’re throttling the pipeline at both ends at once: hollowing out the entry-level job, and feeding it from a school system running a step behind.

It’s a split, not a collapse

The counterweight to the doom is that none of this is uniform, and the loudest version, “the junior is dead”, simply isn’t true. IBM just tripled its US entry-level hiring while most of the industry was cutting, and its HR chief said the quiet part out loud: AI can handle most of the routine entry-level tasks now, the work still needs a human, and the companies that double down on early-career hiring in this environment are the ones that win in three to five years. They didn’t keep the junior role as it was. They rewrote it, less boilerplate, more time spent with customers and supervising what the AI produced.

That is the shape of the thing. The juniors who are thriving in 2026 aren’t the fastest typists. They’re the ones building judgement, which is precisely the edge I argued was the senior’s real value all along. The market hasn’t stopped wanting juniors, it’s stopped wanting the version of the junior whose job was the work AI now does.

Day zero

So what does a junior actually look like now? I don’t know yet… and anyone telling you they’ve got it worked out is selling something. We are at day zero of this.

The junior gauntlet, the rite of passage every one of us runs to earn our stripes, isn’t going anywhere. Doing your time is a cold fact of the craft and it always will be. What changes is what the gauntlet contains, and that will keep changing, day one, day two, day five hundred and twelve. The only way we redefine it well is to put juniors and seniors on it together, with the AI in the room from the start instead of bolted on afterwards. Bring it closer to our people, and bring it earlier.

Open the floodgates, in other words. Let engineers of every creed and calibre in, and let them evolve with the machine, because that is the only way the symbiosis everyone keeps promising actually happens. Darwin’s line was survival of the fittest, and fitness here means adapting alongside the tool, not being spared by it. Choke off the flow of the very people who could do that adapting, and we don’t get fitter. We go extinct.

The end I’m holding

Which is the long way back to that interview. I keep asking the question, what is your real position on AI and how are you building a team of people and machines together, because the answer tells me whether a company is optimising for this quarter or for the survival of the craft. I want to work where it’s the second one, and I think any engineer sitting across that desk should be asking the same.

And it’s why, whatever desk I land at, there’s one thing I already know I’ll do. I don’t have the map. Nobody does. But every junior who works under me is going to get the chance to run the gauntlet, to grow into a senior, and to be in the room while we work out what the next gauntlet should even be. That isn’t charity. It’s the only sane investment any of us can make. The last properly useful thing my generation does, before we go and find our cabins, is make sure there’s somebody left to hand the thread to. I intend to be holding my end of it.

Why I still write code

Wed, 10 Jun 2026 00:00:00 +0000

By any sensible reading of an org chart, I have no business being in this file. I’m a Head of Software Engineering. My calendar reckons I should be in a room somewhere talking about headcount and roadmaps. Instead it’s late, everyone sensible has logged off, and I’m three retries deep into a release that refuses to tag itself, muttering at a Rust workspace I built with my own hands.

So why am I here? I’ve been asking myself a version of that question for about twenty-five years, and I think I’ve finally got an answer. It’s just not a flattering one.

I’m a builder, and that isn’t really a choice

Strip away the job titles and I’m a builder. I like to make things, I like to solve problems, I like to learn how something works by taking it apart and putting it back together slightly differently. That urge predates every role I’ve ever held and it has survived all of them. In jobs where I wasn’t allowed to scratch it, I went and built in the open instead, which is a polite way of saying open source has spent years absorbing energy my day job wouldn’t take.

I’ll go further, because being coy about it helps no one: it’s closer to an addiction than a hobby. I don’t fully switch off. The current outlet, when I’m not in a terminal, is converting a campervan, which is just software engineering with worse error messages and a real risk of electrocution. The shape of the thing changes. The compulsion doesn’t.

Underneath the building there’s a less charming engine, and I might as well name it: a fairly grim case of impostor syndrome. I wrote about it years ago when I stopped calling myself “Dev in Charge”, and a decade on it hasn’t gone anywhere. The only thing that ever quiets the anxiety is staying genuinely good at the thing, and staying good at the thing means using it. I’m a firm believer in use it or lose it. People say technical skill is like riding a bike, you never forget. Maybe. But step away for a few years and when you climb back on, someone’s bolted a jet engine to the frame and moved the pedals. The bike doesn’t wait for you.

What it actually buys

Here’s the part that justifies the indulgence, because on its own “I enjoy it” isn’t a reason to stay technical as a leader, it’s a reason to have a hobby.

The load-bearing belief is simple, and it’s the one line I’d carve into the desk: I will never ask an engineer to do something I’m not willing to do myself. Everything good about staying hands-on flows from that. Because I’m still in the work, I can give my engineers proper support, the right tools and a clear path, rather than guessing at what they need from a slide. I can steer them through a genuinely hard technical call instead of nodding along. I can sniff out a duff estimate, mine or theirs, because I know what the work actually costs. And I can hold them to account with a straight face, because the accountability runs both ways. They answer to me for what they ship, and they get to hold me to account for what I contribute. That second half is the bit a lot of technical leaders quietly drop, and it’s the half that earns you the right to the first.

The bill, and who paid it

I’d be selling you a fairy tale if I stopped there, so here’s the cost, and some of it is steep.

The obvious one is burnout. I’ve been there more than once over the years, and it’s the single biggest reason I now pitch myself deliberately as a Technical Leader rather than an Engineering Manager. I can do the manager stuff, the HR and the planning and the project-management bollocks, and after enough years in the role I do it well, because it demanded that I did. But competence isn’t appetite. Given the choice I’ll take a technical problem or a bit of mentoring over running the process around either, every time, and spending your days on work you’re good at but don’t much enjoy is its own slow road back to the wall. Sticking to my strengths isn’t ego, and it isn’t an admission I can’t do the rest. It’s self-preservation, learned the hard way.

The steeper bill came due at home. When my kids were small I poured my own time into pushing my skills and chasing the next rung, even starting my own agency. Between that and the burnout, I missed big chunks of their early years, and that is one of the real regrets of my life. I’m not going to dress it up or hide it behind a lesson. It was my decision, I made it, and I own it. I’m immensely proud of the people they’ve grown into, and since their mum and I separated I’ve put everything I have into giving them a stable home, the builder instinct quietly turning into a nest-building one, which is the better use of it. I put this here, plainly, because if you’re reading this with a young family asleep upstairs, I’d sooner you heard it from someone who got the balance wrong than learn it the way I did. The code will still be there next year. They won’t be five next year.

And there’s a smaller, daily cost that I still haven’t fully mastered: knowing when to put the keyboard down. A builder who can’t stop building is exactly the person who becomes the bottleneck, disappears down a rabbit hole, or hoards the interesting problem that would have stretched someone on the team. Stepping back to let them solve it, when every instinct I have is screaming to just fix the bloody thing, is genuinely one of the hardest skills I’ve had to learn, and some days it still feels like walking a knife edge. Open source is a big part of how I manage that. It’s a release valve, somewhere I can let the compulsion run with no brakes on, precisely so I’m not stealing the meaty work off the people I’m meant to be growing.

Does it still count when the robot types?

Fair challenge, given the year. I build solo now with an AI pair, to the point where it’s changed how I branch and release. So when a model writes a good chunk of the actual characters, am I still “writing the code”?

I think I’m doing it more than ever, and I’m certainly learning faster. My typing is genuinely terrible, a quarter-century of practice and still mostly thumbs, so being freed from being the typist is no loss at all. What’s left when you take the keystrokes away is the part that was always the point: reading, reviewing, judging, steering. I can review more code, faster, than I ever could when I was the one hammering it out, and I can run several projects at once by pointing my judgement at each in turn. That is leadership work and engineering work at the same time, which is rather the whole thesis.

It did not come free, mind. I was elbow-deep in AI and ML long before GPT made it fashionable, and I’ve seen the messy version up close. Getting to the point where the tools are good enough and I’ve built the guardrails and habits that make them safe took a long time and a lot of getting it wrong. Owning the judgement when the machine does the typing is harder than it sounds, not easier. The typing was never the hard bit.

What I’d actually put my name to

Not that every leader should write code. Plenty of excellent ones don’t, and they’re brilliant at the parts of the job I’m middling at. The narrower, truer claim is the only one worth making: I lead better when I stay in the work, because it’s the only way I know to support, steer and be held to account without faking any of it, and because I meant that line about never asking for what I won’t do myself.

Staying technical isn’t the job. It’s the thing that lets me do the job honestly. I’m a builder who learned, slowly and at a price I’d rather have not paid, how to keep building without it costing the people around me what it once cost the people closest to me. That’s the balance I’m still working at. I suspect I always will be.

The greybeards' edge was never typing

Wed, 27 May 2026 00:00:00 +0000

I have a retirement plan, and it is gloriously low-tech. A cabin, some trees, a woodstove, and a firm rule that no wifi symbol ever appears within a mile of me again. I think about it more than is probably healthy.

There’s a snag, though, and it’s the same one the whole industry is currently pretending it can’t see. For me to vanish into the woods, somebody has to be able to do my job after I’ve gone. And right now, collectively, we are working very hard to make sure nobody can.

The boost, and the drag

I wrote the other day about how AI made producing plausible work nearly free while verifying it stays expensive and human. Point that same lens at a team and something uncomfortable falls out. It isn’t mine; it belongs to Mark Russinovich and Scott Hanselman of Microsoft, who laid it out in Communications of the ACM: agentic coding tools give a senior engineer an AI boost, multiplying what they ship, because a senior has the judgement to steer and verify the output. The same tools give an early-career engineer an AI drag, because they don’t have that judgement yet, and the machine hands them far more rope than they can hold.

The cold incentive writes itself, and they name it: hire seniors, automate juniors. It isn’t hypothetical, either. Meta cut 8,000 roles last week, in a round the Times filed under mounting AI casualties. For any single quarter you care to look at, the maths is impeccable.

The bill is just deferred

Here’s the line the spreadsheet leaves off. The grindy work a junior used to cut their teeth on, the small fixes, the boring migrations, the read-the-stack-trace-and-figure-it-out, is exactly the work AI now does. So the proving ground is gone. And the entry-level seats where they’d have stood on it are the ones being cut. Squeezed from both ends at once: no reps, and nowhere to take them.

Russinovich and Hanselman put the consequence plainly. Without early-career hiring the talent pipeline collapses, and you arrive at a future with no next generation of experienced engineers. The seniors you’ll be desperate for in 2032 are the juniors you declined to train in 2026. The bill doesn’t vanish. It just falls due long after the people who cut the cheque have moved on.

How to manufacture a world of AI slop

I named the last piece for its villain; let me name this one’s too. Raise a generation that can produce with AI but was never taught to validate, and here is what you get: people shipping machine-built products at speed with no instinct for where the output is quietly wrong, because they never had to be wrong the slow way first. Software nobody genuinely understands, human-written and AI-written alike, and a steady leak of trust out of all of it.

That isn’t a productivity problem. That’s a world of AI slop, and not in one project’s inbox this time but everywhere at once. We’d have automated our way clean out of the one job AI cannot do for us: knowing when not to trust the machine.

It’s a choice, and it’s yours

Andrew Murphy put it with more bite than I’d quite dare: AI didn’t kill your junior pipeline, you did. He’s right. This isn’t weather. Nobody is making you do it. It’s a decision, taken quarter by quarter, and a decision is a thing you can take differently.

The fix isn’t complicated, it’s just unfashionable. Keep hiring early-career engineers. Say out loud that they cost you capacity at first, and treat their growth as an actual goal rather than something meant to happen by osmosis. Russinovich and Hanselman call it preceptorship at scale: senior mentorship, deliberately structured, turning the ordinary day’s work into teachable moments.

And the proving ground can be rebuilt, just not where it stood. If AI does the writing now, the apprenticeship moves to the reviewing. Put juniors in the loop on the machine’s output and have them hunt for the subtle wrongness, the way a scanner is an argument, not an order. That’s how judgement gets built now: not by grinding out the work, but by verifying it. Which, as luck would have it, is the single most valuable thing anyone on your team can learn to do.

The part that’s on the greybeards

This is where I stop letting the companies wear all the blame, because some of it is mine, and yours. Verification is a craft, and crafts pass from person to person or not at all. I know where every one of my own AI misfires comes from: I gave it too little context, or too much rope, and didn’t check the result closely enough. The tool rarely went rogue. The gap was always my diligence. That’s not a confession, it’s the curriculum, and it’s precisely the judgement a junior can only earn by sitting in the loop beside someone who has already made those mistakes.

So the senior engineer’s job has quietly changed underneath us. It was never really the typing. It was knowing when something is off, and what the customer actually needs, and now it is also handing that on, deliberately, while there’s still time to. Mentor and guardian first; fastest prompt in the room a distant second.

The ladder you’re standing on

There will always be something AI can’t do well enough, and for a good while yet it’s the thing that matters most: being the accountable human who genuinely understands what’s needed and can be held to it when it goes wrong. A simulation can be enormously convincing. It cannot be responsible.

Which brings me back to my cabin. I do want it one day, the trees and the woodstove and the blissful disconnection. But I only get to go if the work outlives me, and the work only outlives me if the people do. So the last useful thing my generation does, before we shuffle off to find our trees, isn’t shipping a little more code. It’s making sure there’s somebody left who can tell when the machine is wrong. Pull the ladder up behind us and there’ll be nobody to notice the rot, and no cabin quiet enough to make that sit right.

Technical CV writing is still hard, and now a robot reads it first

Fri, 22 May 2026 00:00:00 +0000

Seven years ago I wrote a post called Technical CV writing is hard, pulled my own CV apart, and explained every choice in it. I even bragged that it converted to a first interview about eighty per cent of the time, then added “watch me now jinx myself for the future”. Reader, I jinxed myself. I’m back on the market, the same CV that served me for two decades went out into the world, and what came back was a sort of stunned silence. Not even rejections. Just nothing.

The CV that suddenly stopped working

The thing about that silence is how specific it was. Some applications behaved exactly as they always had: a human read the CV, liked it or didn’t, and replied like a person. Others went into a void. And the void had a pattern to it. It was the bigger, more process-heavy outfits, the ones you’d bet good money have an Applicant Tracking System and an “AI-assisted screening” line item in some HR budget.

That’s when the penny dropped. My CV wasn’t failing to impress anyone. It wasn’t reaching anyone. The first thing reading it wasn’t a person at all.

The reader changed, and I hadn’t noticed

I’ve made this exact point on this blog before, only about software: half your users don’t have eyes. A CLI tool’s output has two audiences, the human at the terminal and the script parsing the output, and they want completely different things. It turns out a CV is now precisely the same. It has two readers, and the first one is a machine.

A human recruiter reads a CV the way I designed mine to be read: narrative, personality, a sense of the person. An ATS or an AI screen does nothing of the sort. It parses for structure, for keyword density, for recency, for numbers it can latch onto. My CV was a beautifully tailored sales pitch aimed squarely at a human who, increasingly, never gets to see it, because a parser in front of them scored it and quietly binned it first.

Everything that made it a good human document was, to the machine, either invisible or actively confusing.

So I asked an AI what the AI hated

There’s an irony here I’m choosing to enjoy rather than resent. The way I worked out what the filters object to was to sit down with Gemini, hand it my CV, and ask it to read the thing the way a recruitment AI would and tell me where it tripped. Using one AI to get past another. Fight fire with fire.

The one instruction I was firm about, and I’ll come back to it, was that the CV had to stay recognisably me. I wasn’t asking Gemini to launder my career into something generic and machine-shaped. I was asking it to help me keep as much of my own voice and judgement as possible, while making the thing easier for an AI to approve and a human to enjoy. There’s a practical edge to that, too: the screening tools are increasingly tuned to spot the patterns of generated text and weight them down, so a CV that reads as though a model wrote it can trip the very filter you were trying to please, quite apart from leaving the human at the end of it cold.

With that ground rule set, the hurdles it surfaced were genuinely illuminating, and a bit humbling given I’d written a whole confident blog post about how to do this.

The skills tables are worse than useless. My CV led with two lovely tables: Management Skills and Technical Skills, each with a level and years of experience. Clean and scannable for a human. To a lot of parsers, a table is a trap: they flatten it into a jumble and lose the structure entirely. Worse, listing “20+ years” against nearly everything triggers what I can only call the recency trap. Modern screening looks for skills that show up in your recent job descriptions, not in a header table. A language sitting in my skills table but not in my last two roles reads as stale or unverified, no matter how many years I claimed next to it. Gemini put it plainly: “if a tool sees Golang in a top table but doesn’t see it explicitly mentioned in your last two job descriptions, it assumes the skill is stale or unverified.”

“I have a passion for what I do” is noise. My opening profile statement, which I was rather proud of, is exactly the sort of thing a screening tool discards wholesale. As Gemini noted, these tools “completely ignore subjective self-assessments … because they cannot be measured or verified.” It wants a dense, factual summary full of the nouns it’s searching for, right at the top.

My numbers thin out the further back you go. My recent roles are full of the data these tools love: a 75% reduction in deployment times, three thousand-odd Kubernetes clusters, a GitLab instance with four hundred thousand repositories. My older roles, written years ago in a more narrative style, are all “oversaw the delivery of solutions” with not a metric in sight. The machine reads that as a career that got vaguer over time, which is the opposite of true.

Four pages is at least two too many. Parsers weight the first page or two most heavily. My education and the foundational stuff sat on pages three and four, where the algorithm barely bothers to look.

It couldn’t work out what I am. This was the sharp one. With “pre-sales”, “client management” and “Managing Director” sitting next to deep technical keywords, the classifier genuinely can’t decide whether I’m a commercial manager who used to code or a hands-on engineer who drifted into management. As Gemini described it: “the algorithm gets confused … It struggles to classify you: Are you a commercial manager who used to code, or a hands-on techie who got pushed into management?” So it does the safe thing and matches me to neither.

What I’m actually changing

Knowing the hurdles, here’s what the rebuild looks like. This is the part I want to be useful, so it’s concrete.

The tables are gone. In their place is a “Core Expertise” section, plain text the parser can read, grouped so my leadership sits next to my technical stack. And I’ve done the thing 2019-me was too much of a show-off to do: tiered it honestly. Instead of “Expert+” against everything, there’s a primary tier of what I actually do day to day, a proficient tier I can deploy without blinking, and a frank “familiar, not current” tier for the languages I last touched in anger a decade ago. That honesty isn’t just decency. A wall of “expert at everything” reads as noise to a machine and as bluster to a human, and I’d been doing both.

The subjective profile is replaced with a keyword-rich professional summary that says, in the first two lines, exactly what I am and at what scale.

The keywords that mattered have been woven down into the recent role bullets, so the parser sees them where it trusts them. And I’ve reframed the people-management and pre-sales language toward technical enablement and architectural advisory, because what I’m actually chasing is the technical-leader sweet spot: the person who owns the architecture and mentors the engineers, without the HR admin and the sales pitches. The CV now points at that, deliberately, so the classifier stops dithering.

There’s also a more personal beat in here. A previous employer handed me a role with a “VP” title, sold to me as exactly the technical-leadership job I’d been chasing. It wasn’t. The title turned out to be a pay-grade bracket rather than a description of the work, the work itself was hands-on firefighting with little of the leadership or empowerment I’d been promised, and I moved on within a few months. To a screening AI, that pairing is doubly awkward. A “VP” title files me as a meeting-heavy executive and rules me out of the hands-on Principal and Lead roles I actually want, and a sub-six-month stint trips the flight-risk flag that some trackers quietly score you down for. So the fix is to stop letting the inflated label do the talking: describe the functional reality of the work, retitle it to the technical track it actually was, and let the scale of what I wrestled with speak instead of the job title. Titles, it turns out, are for the pay band. The bullets are for the truth.

Keeping myself in it

Back to that ground rule. Every one of these changes is in service of getting past the machine to the human behind it, and neither reader is well served by a CV with the person scrubbed out of it. The screen, increasingly, is trained to notice generic generated phrasing and mark it down; the human, always, would rather read something with a pulse. So the keywords go in, the structure gets fixed, the metrics come forward, and the voice stays mine. No “results-driven synergistic leveraging of cross-functional paradigms” that nobody would ever say out loud. That was the whole point of doing it this way: let the AI help reshape the structure a parser cares about, while the words stay mine, so what comes out is easier for a machine to approve, easier for a human to enjoy, and still unmistakably written by me. Optimising for the filter and sounding like myself turned out not to be in conflict at all.

I genuinely don’t know if this works yet

Here’s the part that makes this a post and not a victory lap. I don’t know if any of this lands. The old CV converted at around eighty per cent, on my own possibly-generous reckoning, right up until it abruptly didn’t. The new one is going out now, into the same market and the same filters that were stonewalling me a fortnight ago.

So this is a promise as much as a post. I’m going to keep count, the way I should have all along, and come back with the actual numbers: did reshaping my CV for a reader with no eyes genuinely move the needle, or did I just make it uglier and learn nothing? Either way you’ll get the truth, because a follow-up that only reports good news isn’t worth writing. Watch this space, and if you’re sending CVs into the same silence, maybe try reading yours the way a machine would first. It’s a deeply odd exercise, and I suspect it’s now an essential one.