As someone who has used LLMs heavily at work and at home in various serious experiments, I agree. It still requires heavy babysitting and a lot of its limitations wrt context length are fundamental, not something that’s going to be easy to overcome.

It will be good to see what jj will look like with more funded dev work, but I'm always a little worried about financial incentives mixing with the tools I use for the long term. I guess the saving grace is that I don't really need more upgrades to jj or jjui as it stands. I'm pretty much content with the features and so I could just save this copy of the repo for future reference.

As far as large assets goes, I have my own VCS-ish system which I just integrate with jj, but it would be nice to see a non-git backend handle large assets better as well.

A big reason being that anyone who is using Fable seriously will run out of usage limits very quickly, and so will lean on the "Fable for review + design discussion, Opus 5 agents for implementation" paradigm. But an incredibly annoying UX problem is that the resulting report from the agents that Fable reads isn't surfaced to us in the main dialog, it's only summarized back to us (unless you idle in the agent's window to avoid it closing so you can read what it said directly). As a consequence of this game of telephone, the Fable agent will start using some "terms of art" that it and the agents invented, leaving out literally all context that would be useful in helping me understand what converged/diverged from the implementation attempt. It will often try to ask me for input or say that I have to deliberate on something while also referring to things I've never seen (from the agent result) and without providing any context.

I have to repeatedly prompt it to verbosely explain every time (putting it into the system prompt did little to improve this) and remind it that I can't see what the hell it's talking about.

I'm not sure I'll re-subscribe or even really use AI again because it's honestly more frustrating than it's worth, and so the net emotion I'm left with is frustration and without the satisfaction of learning + building something myself. But at the very least, I thought I'd give someone at the company a tip on what seems to me like a common and obvious UX/UI/workflow failing for using Fable, as some last bit of good will.

- the existence and severity of the financial AI bubble

- the claimed efficacy of AI in terms of its utility vs the actual observed utility

- whether the net good provided by AI outweighs its very heavy costs

I find the section of listing a bunch of selected "predictions" and just saying "Wrong" to elucidate very little. Not that a sentence is sufficient to provide explanation, but Dan stops even doing that bare minimum partway through and just saying "Wrong" full-stop. The reasoning is left up to the reader I guess?

How is it wrong? What was the actual thesis behind it? Is the underlying idea wrong or just the specifics on execution? Was there undetermined factors that mled to the wrong prediction? What can we learn from those factors in order to update our model?

We saw that even though the underlying financials in 2008 were trash and lots of people knew they were trash, things didn't quite collapse in the time frame or way we expected, because an unknown part is how much shenanigans companies can do to extend the runway.

As an example, credit ratings agencies didn't drop ratings to match reality because of customer relation incentives, which is a factor that is not easy to account for and strongly affects the timing of the collapse.

I find the positive reactions to this blog to be confusing. I feel like I learned nothing at all, which makes sense considering under "why write this?" he says "I got four hours of sleep and my brain wasn't good for much of anything and I saw someone posted a screenshot of a reddit post dunking on Ed Zitron's prediction record."

I was planning to build it with ultralig.ht, but I'm not 100% sure if it's ready for it. But since most of the content I'm interested in for research is textual/reader mode, and the rest can be viewed with yt-dlp, I think it can render them and it seems the lightest weight. Otherwise it's webkit or servo that I could think of for this.

Good to know there's interest in this.

Outside of very very short isolated template creation for some kind of basic script or poorly translating code from one language to another, they have wasted more time for me than they saved.

The area they seem to help people, including me, the most in is giving me code for something I don't have any familiarity with that seems plausible. If it's an area I've never worked in before, it could maybe be useful. Hence why the less breadth of knowledge in programming you have, the more useful it is. The problem is that you don't understand the code it produces so you have to entirely be reliant on it, and that doesn't work long term.

LLMs are not and will not be ready to replace programmers within the next few years, I guarantee it. I would bet $10k on it.

I'm pretty excited for variables too! I really wanted them for when I'm using the CLI. Same with query/query_table! I appreciate the push for features that make people's lives easier while also still improving performance.

Everyone who I've introduced duckdb to (at work or outside of work) eventually is blown away (some still have lingering SQL stigma)

I wonder if the `enter|exit ...` syntax might be too limiting but for a lot of stuff it seems nice and easy to reason about. Easier than tree-sitter's own queries.

I think if you really wanted performance and whatnot, you might end up compiling the queries to another target and just reuse them.

I could see myself writing a lua DSL around compiling these kinds of queries `enter/exit` stanzas or an SQL one too.

FWIW I am genuinely asking. I don't know anything about the current tech. There's something about "zero knowledge proofs" but I don't understand how much of that is used in practice for real blockchain things vs just being research.

As far as I know, the throughput of blockchain transactions at scale is miserably slow and expensive and their usual solution is some kind of side channel that skips the full validation.

Distributed computation on the blockchain isn't really used for anything other than converting between currencies and minting new ones mostly AFAIK as well.

What is the great tech that we got from the blockchain revolution?

An 80% solution is to keep the last N states in memory. Snapshots compress well within a small time frame, so whenever we "paused" the playback, we could stash deltas from the pause point to reconstruct stuff (I sadly never got around to implementing this part before I left since it wasn't high enough priority).