Words on signal.kiwi

Don't quit at night

Wed, 30 Sep 2026 00:00:00 +0100

Last year I started participating in long distance cycling events. This came out of a conversation with a co-worker at lunch, who was telling me about how he would be riding the one way course of the Berlin-Munich-Berlin Super Brevet in July and I (half-jokingly) said I’d join him. To be honest, I doubted that I’d have the resolve to follow through on that. I liked riding my bike, but 850km in 64 hours was a different game entirely. I signed up anyway.

We both started pedalling on July 30th, making our way to Cottbus, Dresden, Marktredwitz, Regensburg, and finally Munich. Even though I was a few hours over the time limit, I was surprised to make it to the finish at all. I didn’t know how to fuel properly so I was completely exhausted and dopey. I had saddle injuries, my knees were on fire, and I had swelling around my achilles.

Across the whole ride, the last night was longer and more difficult than the others. It was darker than I was used to, riding in between towns and villages where there are no street lamps. I had to keep my light bright enough that I could see but not so bright that the battery would run out. It was cold, the fatigue was kicking in, bouncing between sweating and shivering. It was quiet. There’s almost nothing happening around you to keep you alert, even with what you can see on the road ahead. Occasionally there’s a car, a deer, a rabbit. Hours of nothing in between. A lot of empty space to let the mind wander: “why am I doing this. I should just quit. The train station isn’t far away. It would be warm. It would be comfortable. I could be home.”

I don’t know what I was trying to prove or what got me through those low points, but I’m glad I did. What I took from that and every event since then, is the the moment you notice the dawn light starting to grow, all of the negative self talk gets a little quieter. It seems a little more manageable. The pain is still there, now it’s not the only thing that’s there.

Every event since then, I’ve noticed that pattern. The day is strong, you make progress, then evening falls, the world starts to go to sleep, and you have to push on through the cold and dark where everything just feels harder, every problem another mountain to climb.

It reminded me of my first job in my current profession, when I definitely did quit in the middle of the night.

I’d spent the summer as an intern at the company, and after that they’d offered to keep me on part-time while I was still studying. It was a good job, too: they made space for me to learn, I had a very patient and skilled mentor, and the people were nice to work with. This was where I learned to use vim, a tool I continue to carry through my career. It’s where I learned Ruby and how welcoming the Ruby community could be. Some of my (pair-programmed) code from that time is still kicking around in the jira-ruby gem.

Not long after university courses picked back up for the year, and winter started closing in, I started to feel overwhelmed. I wasn’t even remotely healthy during university, it gave me some of the lowest points in my mental health journey, and I simply didn’t know how to handle everything that was going on or even recognise how much “everything” there was. I remember clearly sitting at my computer around midnight, crying, and sending an email to my boss telling him I was quitting. He replied a few minutes later, disappointed in the way I’d communicated, but understanding about why.

While “stuff is harder at night” is obviously a literal statement, I find it’s also a pliable metaphor that helps me in some of life’s other challenges. Winter is long, cold, dark, and has its own challenges. Mental health has its own waves, sometimes they sneak up and you need to be careful about what decisions you make when. You also need to be mindful about cultivating the metaphorical morning, since it requires deliberate work to come out of those dips.

Since that first night riding experience, I’ve joined more events, ridden with more experienced riders, and learned more about how to get through these things in once piece. One of the lines I have seared in my mind now is from a film about Further Elements, an adventure bike race through remote parts of Scotland I would like to join one day:

“don’t scratch at night, do it in the morning, it’s always better.”

It’s not surprising to hear this kind of thing at rider briefings and from those more experienced, yet this particular instance of it, the undramatic delivery (and probably the film’s editing), it stuck with me. It’s good advice, too: not some motivational “you can do it!” speech, but a more sober “don’t scratch at night”. It acknowledges the reality that it’s going to suck out there sometimes and you’re going to want to give up. At least give yourself some rest and the chance to see the morning before you do it.

This is all a long winded way of saying: make big decisions whenever you want, but make them quietly to yourself at first. Let them marinate. Find a quiet space to reflect before you commit to them.

For now, in this era of turbo-capitalism, it’s feeling pretty fucking dark. Daylight can come from many places. The sun, friends & family, hobbies, nature. Make space away from what’s creating urgency, and cultivate the things that give you opportunities to reflect.

After writing all of this out that, I remembered a lot of the people I worked with at that first internship were passionate about cycling. After leaving that job I didn’t really ride bikes for the next 15 years. It’s only now I realise how important cycling is, the kinds of experiences it can unlock, and the skills it gives me that help navigate other parts of my life.

I wonder what would be different if I’d waited until the morning back then.

Boundaries and AI hype

Tue, 10 Mar 2026 00:00:00 +0100

Here I am writing another ranty draft about AI. Perhaps this time I’ll make it public.

After a tense and frustrating meeting today to discuss the “AI first” / agentic coding future of the engineering department, I’m again thinking about my boundaries as a professional.

Over the last year or so, I’ve observed this intense thirst from leaders in organisations to ‘see the light’; their networks are full of people in similar roles that have been baffled as to why some engineers aren’t as excited about the future of software development, or are at least approaching it more cautiously. I can’t shake the feeling that whatever is driving this thirst is at least somewhat disconnected from reality (or, at least most of our realities), and it means anyone who doesn’t share that view has the pleasure of being gaslit on a daily basis.

Meanwhile, I’ve also observed engineers using and experimenting with these products, genuinely excited about them, and considering how to fit them into their practices. Others have experimented, and concluded that they aren’t useful to them at that time, or only fill a certain niche. Both of these responses seem a lot more rational to me, even though I don’t necessarily hold that same excitement.

So. Boundaries.

Since this hype cycle began, I’ve been trying to make sense of it both as a professional who at times has to be pragmatic to align with organisational goals, and as a person who has their own values and ideals.

As my professional self, I see plenty of ways in which this can change how we write software, both at the more abstract level of implementing automations using natural language, and at a practical level for code generation/manipulation. That’s fairly uncontroversial. I also see a bunch of failure modes due to the way LLMs inherently work: they generate plausible text based on an input, there is no reasoning or understanding, making them prone to subtle and not-so-subtle errors in their output. This is not something that can be ‘fixed’, it’s inherent in the technology. Depending on the way it’s deployed and what problem it’s intended to solve, that may also be a totally acceptable tradeoff.

As my normal, messy, every-day self, I see different things.

Environmental: these models are energy intensive to train and operate, requiring specialised hardware and the associated infrastructure to run it (often consuming potable water for cooling.) There seems to now be data centre gold rush, of sorts, and these data centres often seem to land near marginalised people or where there is cheap land despite problems that make potable water scarce. The companies operating these products (OpenAI/Microsoft, Anthropic, Google, etc.) do not have a good track record when it comes to transparency, but the consumption does not appear to be negligible. Given that we are in a climate crisis and we are not already on 100% carbon neutral energy, it doesn’t seem like we need their data to conclude that this makes our problem worse.

Moral: the way these models were trained, through the mass exploitation of peoples’ work (creative, scientific, etc.) without attribution or compensation, and assigning reinforcement tasks to underpaid and under supported labourers in developing countries to improve model performance and moderate content to the point of psychological damage.

Social: AI induced psychosis is a thing now. People have taken their lives after ChatGPT has obediently helped them write a plan. Children’s toys are telling them how to start fires. Education systems are struggling in the wake of lobbying by tech companies to digitalise the classroom, despite resistance from educators (this started a long time ago, AI is the newest layer.)

There are a lot of incredibly important issues tangled in with this AI push, and from what I’ve observed, tech leaders seem to either not be aware of them, shrug them off with “it’ll get cheaper/more efficient”, or simply don’t care.

Personally and professionally, I can’t justify participating in the ways my industry is using LLMs at all. It is simply not worth the cost to the planet and our people.

The really frustrating part of this is that it’s solvable—it just takes work. Work to construct datasets that respect the peoples’ rights and culture. Work (and money) to train the models using well-supported/-compensated labour. Here’s a sublime example of people doing that work.

Unfortunately, it’s work that seemingly the majority of the loudest voices in the room are not willing to do, for the sake of profit.