sophiabits

> You can't fire Claude if it fucks up

What's the difference between "firing" Claude vs moving to a model from a different provider? The latter seems very analogous to firing an employee for performance and backfilling with someone new.

Re the rest, it's just not my experience that models become incapable of making good decisions in cases where input token count > the context window, but ymmv based on domain.

A very extreme example of this: a couple years ago when GPT 4 was state of the art and the 32K context variant was gated to design partners I worked at an EdTech company in the college admissions space that wanted to produce quarterly reports on student progress for parents. That involved crunching a LOT of data (multiple hours of meeting transcripts per week, very detailed notes about student activities, their general profile - UK and US admissions function very differently!)

It was a difficult problem, but we _did_ manage to produce these reports 4K output tokens at a time at a level of quality that exceeded what humans could do internally, and models+the surrounding tooling have only gotten better since then.

TIL you can get RSS feeds for YouTube channels. Thanks for this!

> Selfishly, I don't want my screen scraped because it feels like an invasion of my privacy, [...] But zooming out, I don't want to live in a world where humans—employees or otherwise—are exploited for their training data.

I wonder whether this Meta employee felt the same way about privacy a month ago, before they were personally impacted by this new initiative.

A browser is OOM more expensive to run than a terminal app, regardless of what you're running inside said browser

Purchasing power is probably a better metric in a vacuum, but it's hard to analyze accurately

For example, the comment you're citing is claiming that because minimum wage has increased only 3x over the same period of time in which inflation has eroded the relative value of a dollar by 6x, that wages overall have increased at half the rate of inflation. But minimum wage is a measurement of a minimum, while inflation is a measurement of /average/ price increase so they can't be compared 1:1 in this way.

The housing argument also seems odd. In New Zealand (where I'm from -- I'm not familiar with the US' housing market, so the commenter could be right about that geo!) house prices have increased by far more than 20x since the 70s, but the houses available are of substantially higher quality due to improved regulations (e.g. all newer homes are subject to healthy homes rules which mandate insulation) so just comparing inflation-adjusted home prices vs income doesn't tell the full story

(Aside from that, a whole heap of items like food, electronics, transportation are all both far cheaper AND higher quality today than in the 70s)

> the MCP server is automatically launched when the Agent loads that skill

The main problem with this approach at the moment is it busts your prompt cache, because LLMs expect all tool definitions to be defined at the beginning of the context window. Input tokens are the main driver of inference costs and a lot of use cases aren't economical without prompt caching.

Hopefully in future LLMs are trained so you can add tool definitions anywhere in the context window. Lots of use cases benefit from this, e.g. in ecommerce there's really no point providing a "clear cart" tool to the LLM upfront, it'd be nice if you could dynamically provide it after item(s) are first added.

Comments are often the best tool for explaining why a bit of code is formulated how it is, or explaining why a more obvious alternate implementation is a dead end.

An example of this: assume you live in a world where the formula for the circumference of a circle has not been derived. You end up deriving the formula yourself and write a function which returns 2piradius. This is as simple as it gets, not hacky at all, and you would /definitely/ want to include a comment explaining how you arrived at your weird and arbitrary-looking "3.1415" constant.

I wanted to document a particular genAI antipattern which I've seen a few times now.

LLMs are theoretically pretty fungible, because you send English and get English back--but in practice you still need to do some amount of technical due diligence before swapping model. These things are benchmarked on tasks which rarely resemble your specific use case. Blindly swap models at your own risk!

Something that has become very clear since the advent of GPT-3.5 is that LLMs are far from magic, and using them does not remove the need for good engineering fundamentals. It's important to have a solid eval suite so you can quickly benchmark your system against different LLMs, because the APIs we're all building on are constant moving targets.

Even with this setup in place you need a heightened level of caution relative to a monolith. In a monolith I can refactor function signatures however I desire because the whole service is an atomically deployed unit. Once you have two independently deployed components that goes out the window and you now need to be a lot more mindful when introducing breaking changes to an endpoint’s types

Not sure what the situation is like now, but we stopped using LangChain last year because the rate of change in the library was huge. Whenever we needed to upgrade for a new feature or bug fix we’d be 20~ versions behind and need to work through breaking changes. Eventually we decided that it was easier to just write everything ourselves.

This is from the first half of 2023 or so; maybe things are more stable now, but looks like the Python implementation is still pre-v1.