philomath_mn

Reminds me of this quote from Tonya Riley's _The Staff Engineer's Path_:

> In The Art of Travel (Vintage), Alain de Botton talks about the frustration of learning new information that doesn’t connect to anything you already know—like the sorts of facts you might pick up while visiting a historic building in a foreign land. He writes about visiting Madrid’s Iglesia de San Francisco el Grande and learning that “the sixteenth-century stalls in the sacristy and chapter house come from the Cartuja de El Paular, the Carthusian monastery near Segovia.” Without a connection back to something he was already familiar with, the description couldn’t spark his excitement or curiosity. The new facts, he wrote, were “as useless and fugitive as necklace beads without a connecting chain.”

Why is that?

AI bills are already trivial compared to human time. I pay for claude max, all I need to do is save an hour a month and I will be breaking even.

> most people agree that the output is trite and unpleasant to consume

This is likely a selection bias: you only notice the obviously bad outputs. I have created plenty of outputs myself that are good/passable -- you are likely surrounded by these types of outputs without noticing.

Not a panacea, but can be useful.

Anywhere I can follow your takes on LLM-assisted coding?

ChatGPT is $20 / month?

Under what circumstances would that cost be high? Is OpenAI going to rip off your app? Why would they waste a second on that when there are better models to be built?

The exact same logic applies to any deductible expense, and yet people think they can buy a business vehicle "for free" because it is deductible.

IDK why this is so hard to understand.

By building a good reputation and contact list doing salaried work. Usually you do a good job there, make a bunch of stakeholders happy, and then you have a chance at spinning off on your own.

Best part is that they probably have data to show that all that patience costs the typical passenger mere seconds to a minute on 99% of rides.

This has always bothered me about aggressive or impatient human drivers: they are probably shaving like 30 seconds off of their daily commute while greatly increasing the odds of an incident.

Curious what you are expecting when you say "bottom falls out". Are you expecting significant failures of large-scale systems? Or more a point where people recognize some flaw that you see in LLMs?

While this unfortunate, I am sure I also have single lines in production with greater cost and equivalent value (close to none) -- and I've only worked at small companies. I am sure some of y'all can beat this by ~2 orders of magnitude.

Databricks is happy to have us as a customer.

Any of the leading LLMs could answer this, they just need the right context. So paste in the model cards for the various models you care about, or ask a model with search or "deep research" capability.

That being said, there probably isn't too much alpha in chasing down model tradeoffs outside the top few players (OpenAI, Anthropic, Gemini).

That definitely seems to be the play. Keep funneling in users from Lovable/bolt.new and keep building revenue or hope to be acquired if one of those vibe coding tools gets huge.

I agree: the incentives to use more and more AI are too strong. We're all stuck in some form of the prisoner's dilemma and the odds that nobody will defect are much too low.

So it seems the most rational position is to embrace the tools and try to ride the wave before the gravy-train is over.