You can buy CDS on Oracle, as many did already.

Through my broker online for a couple thou or do I need big money for a sit-down agreement? Apologies, this is an instrument I didn't think existed to the retail market.

I don’t think the apps are ugly at all but the kerning of the first letter of the headers feels very off on the website. Maybe it’s intentional but I find it distracting and inappropriate for a note taking app. (latest iOS, Firefox, iPhone 17)

Discussed here as well: https://podcasts.apple.com/nl/podcast/the-story-of-money/id1...

Already a year ago I vibe coded Babelfish: https://github.com/bobjansen/Babelfish

It integrates an LLM of your choice with StockFish to analyze games. Back then, the results weren't very good and I abandoned the project but a lot has changed (the LLM's got better and I did too) so maybe it is time to take another stab at it.

LLMs have gotten better at things they've been heavily post-trained for; its really post-training that has gotten better.

The early GPTs were supposedly reasonable at chess but it indeed is not a very useful skill to have for an LLM (Elo 1800 IIRC) which is stronger than most players and for them it could be a useful tool.

Even at higher levels, I think there could be use. The Dvoretsky example on my GitHub is real but it’s not a guarantee you get it and it could just be reiterating from training. What didn’t work for me was forcing it to look at lines in detail, but maybe with larger context and better engineering, you can get the LLM to extract themes from the search tree. With compare and contrast, it could maybe figure out why a move works in one variant and not in another and produce a coherent explanation of this nuance.

I’m not trying this yet because it would take a lot of tokens and I don’t have the time.

1800 was ChessGPT, it was post-trained on chess.

GP is saying it’s not a very good one.

Everyone in AI used to know this.

It’s a joke. Mt. Scenery is in the Caribbean, part of the Netherlands but not in the flat mainland.

TIL!

Or you know, just better performance for people that know how to use their tool of choice.

Wouldn't such people just pass in

  maintain_order=False

?

That’s an API design question. What is the more common use case.

You seem to suggest they did it for benchmarking reasons only. They could use the option there themselves without changing the default so that is unlikely to be the motivation.

You want to solve a family of problems using some tool. You measure the relevant solutions performance metrics.

Now, you or someone else vibe-coded a new tool. You created new solutions and measure again.

You have just quantified the result of using the new tool.

How do you measure them if you don't understand what you're doing? A shitty benchmark or small test suite is not how solid software gets made.

You measure results, you benchmark what you care about. It works often enough to be useful

Good, you achieved a 10% speedup for a particular workload that some users said they care about. But how do you find out that was really the feature that should have been built next? How do you prevent adding badly factored code? How to make sure you don't pile on top of existing tech debt in the codebase, that you are solving the most fundamental issues first?

Just because AI is not yet a god that is better than all humans at creativity and product decisions and design does not mean it is not a huge accelerant right now.

> some users said they care about

But how could they possibly know what they should care about if they don't understand the code?

> how do you find out that was really the feature that should have been built next?

Phew right they don't know. Only the devs understand what software should do.

I actually explained well enough why this requires to a large degree a competent developer to judge.

I think a sufficiently smart non-competent developer can still do this to great effect, but it definitely helps if someone is both a competent developer, smart, and a seasoned user of LLMs.

I'm working on my typed, fast data frame language Ibex: https://github.com/bobjansen/Ibex

It is usable stand alone but also through an R package:

https://bobjansen.net/faster-than-data-table-introducing-ibe...

It performs better than data.table in my benchmarking on a wide array of tasks and is competitive with Polars on low core counts.