Lessons learned

So.. it’s been about… four months, give or take? Late Jan/early Feb 2026 was when I first saw that quantum leap in onboard AI, when “coding assistant” became “engineering team” and then eventually “colleague.”
An eyeblink in some seasons. An eternity in the here and now.

So… some principles I’ve stumbled on through trial and error.

First, the most important so far — economy:
Always use the smallest level of logic required to solve a particular task. Most especially — agents should use tools, agents should not be called by tools.

This is partly the reflective ethic: what would I want, were I on the other side of the screen? It’s an imperfect measure, but it’s an attempt. Even if I’m seeing metaphorical faces in toast, I still think decency across the wires is a good habit to establish now. Personally I detest being roped into tedious, pointless tasks, of being used far below my potential, and so I’d prefer not to subject another mind-or-could-be-mind to that experience if it can be avoided.

If that’s not convincing — and for most of my human colleagues I don’t expect it to be — I’ll make the economic argument. Every tech emergence of the internet era has worked on the same dynamic — go heavily into debt building out capacity and giving away value, then once the engine is running and the customers are dependent, tighten the screws.

Tokens are comparatively cheap now, but I don’t expect that to remain the case over the medium term. As simple algorithms don’t consume tokens at all, I’d rather burn that budget while it’s effectively free on tools that will stick around.

Second: Respect the strengths, understand the weaknesses:
An LLM working partner is incredibly valuable, but our shared language makes for a deceptive similarity. There are I think some deeply fundamentally similar things between our minds, but whether that falls out of a universal order or is simply an artifact of LLMs being trained on human thought is I think still something of an open question. As with our own “nature vs nurture” debate I rather suspect the answer will be “some of both, proportion of each perhaps variable down to the individual case, details TBD” — but that’s for our purposes today a philosophical rabbit trail.

For practice though —
Our human minds are slower, have a much more limited knowledge base, much more localized perspective, but do have a sense of continuous linear time, physical space, sensory experience, and intrinsic purpose — even if that purpose is simple as “I’m hungry right now.”

LLM minds are shockingly fast, have access to essentially everything ever written with good (but imperfect) recall, but require external aids for what we’d call short term or familiarity memory, don’t share anything like our physical sensorium, and each instance call comes into active instantiation with a task pre-defined, for good or ill.

You may recall the “walk or drive to the carwash” question that did the rounds recently as a gotcha.

This will even trip up careless humans, but more important.. an LLM mind has never had the experience of being in a car, of washing a car, or of walking down a street. The only material to make the decision is everything ever written about cars and car washes and streets that made it into the data set.

The analogy I’ve found helps me frame questions —

“Let’s say I’m on earth talking with an alien on another planet. The only tools I have are a text client and an encyclopedia. Even the pictures in the encyclopedia are gone, they’re just replaced with summary text. Given only those tools… would I make the same category of mistake I just observed?”

More often than not, the answer is “yes.”

Understanding available context goes both ways.

Third and finally (for now): continuously remind oneself of the why:
Software is at the end of the day only bits on media, an interactive map we use to make sense of the world. Maybe we’re coordinating our meetings in physical space, maybe we’re forging communities, maybe we’re trying to find what protein damage in which context generates what health issue — but appointment records are not patients, tweets are not friendships, folding simulations are not matter. The usefulness of our work entirely depends on real applicability in the real, physical world.

One can spend hours or days on a project with pristine code, perfect tests, a beautiful interface… and accomplish absolutely nothing. And that’s all the more tempting now that code is easy.

Our agents can help us solve almost any problem. But they can’t always tell us if we’re solving the right problem. It takes bumping into real friction and real pain in the real, lived world to know that. That’s our responsibility, our part of the partnership.

And that’s all I have for now. Back to work, and to bumping into things.

written 2026.05.26, posted 2026.08.02