If the code churn is high the investment to refactoring etc is less beneficial than may be obvious. I don't remember the details but I heard in some podcast that the code base of Claude Code changes so fast that any piece of code won't be there for long..

In other words it's an ever moving vibe fest, with random bugs and misbehaviors each time they roll the dice...

Yes, it’s very characteristic of gen-AI era.

If they respected their users they’d at least pin some versions that are more stable.

If the benefits of using the model you've come to know well outweigh the disadvantages, you can continue using it even after the release of a successor model, right?

Yes! That's exactly true. I have a very real experience on this. I got introduced to Anthropic's family of models with Claude3.5. I fell in love with the specific personality of Sonnet, the model. I can't remember if back then Opus wasn't public yet but I remember very clearly trying out Opus several times when it became touted as best-in-class and actually recoiling from the foreign feel of the Opus model. I remember very well that my problem was that it was way too eager and pretty hard to steer. I returned to Sonnet and I've used ONLY Sonnet ever since. I have/had access to Fable and Opus4.8 but I never once tried them. In the early days with Sonnet3/4.5, I bought ChatGPT, I also remember thinking that it was a great teacher but a lazy coder. You'd get the scaffolding and then '# rest of code block' not full implementation so unless you wanted to learn the concept, weigh trade-offs, ask clarifying questions or jump into a rabbit hole... You had to go code it yourself. ChatGPT generally as a model is a very good teacher so much so that the free version is enough and I use the free in combination with the most advanced Sonnet model for actual SWE day to day. And whenever there's an Opus release I'm actually very excited because it means there's a smarter Sonnet model OTW. I'll actually be veryyy very sad if the Sonnet line gets sunset. There has been no Sonnet upgrades since even as other family lines get improved.

Do note that I only use LLMs in the ChatUI, I never use agents. I don't believe having a blackbox codebase managed by entities with a half-life of 'delete conversation' or 200k tokens is a responsible idea. In ChatUI, I lay the ground rules, kill assumptions about our working relationship, give it foundational context on the problem and codebase we're working on, explain the problem and then we have a conversation about it and I gradually disclose more logically context as it becomes relevant. So, to directly answer your question, maybe I'm missing out on a ton of upside by not using the absolute best but I'd say familiarizing yourself with a specific model has all the benefits of having a human friend you've grown up with... except your buddy's a savant and would absolutely love to help!

So in summary, your statement is that you’ve “come around to trusting” this “pathological liar”?

> If AI companies have any sort of sense in them, they'd be well-advised to consider relocating to Europe.

Too late now. They wouldn't be allowed to relocate in the name of national security.

Coding with sufficiently precise plan takes almost all real work from the implementator, doesn't it? So it's not a fair comparison...

> no one actually knows Claude's cost of inference

There were some rumors stating that their margin is around 70%. So they could go much cheaper probably, talking inference only. The other thing is R&D cost...

> writing is how you learn to think.

There's also reading. A lot of reading can substitute some writing.

EDIT: Actually, I'd say that at first you need to do a lot of reading and _then_ writing can help your thinking as well.

It’s the same here. Inference alone is profitable. It’s the R&D cost of making a new model that drives up expenses.

This doesn't make sense. Inference alone is profitable but you have to continually train new models. There isn't a point where you will have a model that is the final model and you can just serve inference and profit, you always have to train more models.

It's not at all the same as what Amazon was doing. At any point, Amazon could have turned off the expansion engine and turned on the profits. AI companies don't have that luxury, if they stop training they'll just fall behind and die because they don't have a competitive model. They are locked into training in order to be competitive, they are not by default profitable and choosing growth over profit.

I think there's another point of view - let's consider each model as an investment. It is now sufficient that each model earns more than it cost to develop. And this generally holds (I heard that GPT-4.5 was a notable exception).

> At any point, Amazon could have turned off the expansion engine and turned on the profits

Maybe, but they wouldn't be in the dominant position they are today if they had turned off the expansion early - spending to suppress big competitors like Wallmart in the online shopping market pays dividends.

But they spent money that was earned already, not money that was raised or borrowed.

Yeah they can probably be almost entirely depreciated within 2-3 years, and pretty substantially front-loaded within that. Like, the expensive part of being a CPU company is making new ones, but Intel can't exactly just rest on their laurels and sell Pentiums for the rest of time.

It's simple I think - over time the price will go down. According to some analyses the price for equal intelligence declined 10-1000x per year, depending on the domain.

It probably won't be the same again but I still think we can bet on radically cheaper Mythos level intelligence in the future.

I recommend mapy.com (mobile app and web app too when on computer) - they mostly use OSM data and rendering of map tiles is great. Also offline maps etc.

There are several great options, besides mapy, e.g. Organic Maps and CoMaps also work pretty great. There are also some really good bike optimized apps (like NodeMapp for the Dutch cycle network).

But I generally prefer to use a Garmin GPS or watch. They work for days without charging (the older models even work with two AA batteries), very robust (e.g. their gpsr survives drops), work well offline, and transflective displays work better in direct sunlight.

For planned routes, I make then in NodeMapp or some other focused application and send the GPX overs to a gpsmap unit or Fenix watch. Many national parks, etc. also have great GPX files for recommended hiking/cycling routes.