What’s wrong with just pushing Postgres as far as possible (you said it wasn’t complex)?

  yet again I reached for a dependency when Postgres is enough

What you're describing is a glorified mirror.

Doesn't that sound ridiculous to you?

I reached this conclusion pretty quickly. With all the hand holding I can write it faster - and it’s not bragging, almost anyone experienced here could do the same.

Writing the code is the fast and easy part once you know what you want to do. I use AI as a rubber duck to shorten that cycle, then write it myself.

I found myself agreeing with quite a lot of this article.

I'm a pretty huge proponent for AI-assisted development, but I've never found those 10x claims convincing. I've estimated that LLMs make me 2-5x more productive on the parts of my job which involve typing code into a computer, which is itself a small portion of that I do as a software engineer.

That's not too far from this article's assumptions. From the article:

> I wouldn't be surprised to learn AI helps many engineers do certain tasks 20-50% faster, but the nature of software bottlenecks mean this doesn't translate to a 20% productivity increase and certainly not a 10x increase.

I think that's an under-estimation - I suspect engineers that really know how to use this stuff effectively will get more than a 0.2x increase - but I do think all of the other stuff involved in building software makes the 10x thing unrealistic in most cases.

One of the negative consequences of the “modern secular age” is that many very intelligent, thoughtful people feel justified in brushing away millennia of philosophical and religious thought because they deem it outdated or no longer relevant. (The book A Secular Age is a great read on this, btw, I think I’ve recommended it here on HN at least half a dozen times.)

And so a result of this is that they fail to notice the same recurring psychological patterns that underly thoughts about how the world is, and how it will be in the future - and then adjust their positions because of this awareness.

For example - this AI inevitabilism stuff is not dissimilar to many ideas originally from the Reformation, like predestination. The notion that history is just on some inevitable pre-planned path is not a new idea, except now the actor has changed from God to technology. On a psychological level it’s the same thing: an offloading of freedom and responsibility to a powerful, vaguely defined force that may or may not exist outside the collective minds of human society.

Here's the full paper, which has a lot of details missing from the summary linked above: https://metr.org/Early_2025_AI_Experienced_OS_Devs_Study.pdf

My personal theory is that getting a significant productivity boost from LLM assistance and AI tools has a much steeper learning curve than most people expect.

This study had 16 participants, with a mix of previous exposure to AI tools - 56% of them had never used Cursor before, and the study was mainly about Cursor.

They then had those 16 participants work on issues (about 15 each), where each issue was randomly assigned a "you can use AI" v.s. "you can't use AI" rule.

So each developer worked on a mix of AI-tasks and no-AI-tasks during the study.

A quarter of the participants saw increased performance, 3/4 saw reduced performance.

One of the top performers for AI was also someone with the most previous Cursor experience. The paper acknowledges that here:

> However, we see positive speedup for the one developer who has more than 50 hours of Cursor experience, so it's plausible that there is a high skill ceiling for using Cursor, such that developers with significant experience see positive speedup.

My intuition here is that this study mainly demonstrated that the learning curve on AI-assisted development is high enough that asking developers to bake it into their existing workflows reduces their performance while they climb that learing curve.

Shilling my book "Effect Oriented Programming" https://effectorientedprogramming.com/

The book uses Scala & ZIO but intends to be more about the concepts of Effects than the actual implementation. I'd love to do a Flix version of the book at some point. But first we are working on the TypeScript Effect version.

- Extremely strict linting and formatting rules for every language you use in a project. Including JSON, YAML, SQL.

- Using AI code gen to make your own dev tools to automate tasks. Everything from "I need a make target to automate updating my staging and production config files when I make certain types of changes" or "make an ETL to clean up this dirty database" to "make a codegen tool to automatically generate library functions from the types I have defined" and "generate a polished CLI for this API for me"

- Using Tilt (tilt.dev) to automatically rebuild and live-reload software on a running Kubernetes cluster within seconds. Essentially, deploy-on-save.

- Much more expansive and robust integration test suites with output such that an AI agent can automatically run integration tests, read the errors and use them to iterate. And with some guidance it can write more tests based on a small set of examples. It's also been great at adding formatted messages to every test assertion to make failed tests easier to understand

- Using an editor where an AI agent has access to the language server, linter, etc. via diagnostics to automatically understand when it makes severe mistakes and fix them

A lot of this is traditional programming but sped up so that things that took hours a few years ago now take literally minutes.