Session transcript using Simon Willison's claude-code-transcripts
https://htmlpreview.github.io/?https://gist.githubuserconten...
Reddit post
https://www.reddit.com/r/ClaudeAI/comments/1q9fen5/claude_co...
OpenRCT2!!
https://github.com/jaysobel/OpenRCT2
Project repo
Although git revert is not a destructive operation, so it's surprising that it caused any loss of data. Maybe they meant git reset --hard or something like that. Wild if Codec would run that.
Stop spamming
And what would that reason be? You can git revert a git revert.
And the reason jj helps in that case is that for jj there is no such thing as an uncommitted change.
Then, `git notes` is better for signature metadata because it doesn't change the commit hash to add signatures for the commit.
And then, you'd need to run a local Rekor log to use Sigstore attestations on every commit.
Sigstore.dev is SLSA.dev compliant.
Sigstore grants short-lived release attestation signing keys for CI builds on a build farm to sign artifacts with.
So, when jujutsu autocommits agent-generated code, what causes there to be an {{AGENT_ID}} in the commit message or git notes? And what stops a user from forging such attestations?
> you can manually stage against @-: [with jujutsu]
You could take those, make the tools better, and repeat the experience, and I'd love to see how much better the run would go.
I keep thinking about that when it comes to things like this - the Pokemon thing as well. The quality of the tooling around the AI is only going to become more and more impactful as time goes on. The more you can deterministically figure out on behalf of the AI to provide it with accurate ways of seeing and doing things, the better.
Ditto for humans, of course, that's the great thing about optimizing for AI. It's really just "if a human was using this, what would they need"? Think about it: The whole thing with the paths not being properly connected, a human would have to sit down and really think about it, draw/sketch the layout to visualize and understand what coordinates to do things in. And if you couldn't do that, you too would probably struggle for a while. But if the tool provided you with enough context to understand that a path wasn't connected properly and why, you'd be fine.
For this to work the way people expect you’d need to somehow feed this info back into fine tuning rather than just appending to context. Otherwise the model never actually “learns”, you’re just applying heavy handed fudge factors to existing weights through context.
what a world!
People don’t appreciate what they have
A machine generating code you don't understand is not the way to learn a programming language. It's a way to create software without programming.
These tools can be used as learning assistants, but the vast majority of people don't use them as such. This will lead to a collective degradation of knowledge and skills, and the proliferation of shoddily built software with more issues than anyone relying on these tools will know how to fix. At least people who can actually program will be in demand to fix this mess for years to come.
Just like how we still need assembly and C programmers for the most critical use cases, we'll still need Python and Golang programmers for things that need to be more efficient than what was vibe coded.
But do you really need your $whatever to be super efficient, or is it good enough if it just works?
There was a time when you had to know ‘as’, ‘ld’ and maybe even ‘ar’ to get an executable.
In the early days of g++, there was no guarantee the object code worked as intended. But it was fun working that out and filing the bug reports.
This new tool is just a different sort of transpiler and optimiser.
Treat it as such.
No, there wasn't: you could just run the shell script, or (a bit later) the makefile. But there were benefits to knowing as, ld and ar, and there still are today.
This is trivially true. The constraint for anything you do in your life is time it takes to know something.
So the far more interesting question is: At what level do you want to solve problems – and is it likely that you need knowledge of as, ld and ar over anything else, that you could learn instead?
And, yes, I'm aware that most compilers are not entirely deterministic either, but LLMs are inherently nondeterministic. And I'm also aware that you can tweak LLMs to be more deterministic, but in practice they're never deployed like that.
Besides, creating software via natural language is an entirely different exercise than using a structured language purposely built for that.
We're talking about two entirely different ways of creating software, and any comparison between them is completely absurd.
Meanwhile, 9front users have read at least the plan9 intro and know about nm, 1-9c, 1-9l and the like. Wibe coders will be put on their place sooner or later. It´s just a matter of time.
Everyone else are deluding themselves. Even the 9front intro requieres you to at least know the basics of nm and friends.
Going to arcane websites, forum full of neckbeards to expect you to already understand everything isn’t exactly a great way to learn
The early Internet was unbelievably hostile to people trying to learn genuinely
Assembly programmers from years gone by would likley be equally dismissive of the self-aggrandizing code block stitchers of today.
(on topic, RCT was coded entirely in assembly, quite the achievement)
LLM outputs are akin to a mutant tree that can decide to randomly sprout a giant mushroom instead of a branch. And you won't have any idea why despite your input parameters being deterministic.
Code is a project that has to be updated, fixed, etc.
So when something breaks - you have to ask the contractor again. It may not find an issue, or mess things up when it tries to fix it making project useless, etc.
Its more like a car. Every time something goes wrong you will pay for it - sometimes it will get back in even worse shape (no refunds though), sometimes it will cost you x100 because there is nothing you can do, you need it and you can't manage it on your own.
Also out of nowhere an invasive species of spiders that was inside the seed starts replicating geometrically and within seconds wraps the whole forest with webs and asks for a ransom in order to produce the secret enzyme that can dissolve it. Trying to torch it will set the whole forest on fire, brute force is futile. Unfortunately, you assumed the process would only plagiarize the good bits, but seems like it also sometimes plagiarizes the bad bits too, oops.
I find this very interesting of us humans interacting with AIs.
Maybe this is obvious to Claude users but how do you know your remaining context level? There is UI for this?
There might be an input that would produce that sort of effect, perhaps it looks like nonsense (like reading zipped data) but when the LLM attempts to do interactive in it the outcome is close to consuming the context?
i enjoy playing video games my own self. separately, i enjoy writing code for video games. i don't need ai for either of these things.
It's still a neat perspective on how to optimize for super-specific constraints.
It's kind of like how people started watching Let's Plays and that turned into Twitch.
One of the coolest things recently is VTubers in mocap suits using AI performers to do single person improv performances with. It's wild and cool as hell. A single performer creating a vast fantasy world full of characters.
LLMs and agents playing Pokemon and StarCraft? Also a ton of fun.
Biggest downside was it's inability to see (literally), getting lists of interact-able game objects, NPCs, etc was fine when it decided to do something that didn't require any real-time input. Sailing, or anything that required it to react to what's on screen was pretty much impossible without more tooling to manage the reacting part for it (e.g. tool to navigate automatically to some location).
I still have some parts of the old Rei-net forum archived on an external somewhere.
SirPugger's youtube channel has loads of videos monitoring various bot farms.
Am I reading a Claude generated summary here?
> "This was surprising, but fits with Claude's playful personality and flexible disposition."
Is this sentance seriously about a computer? Have we gone so far that computers wont just do what we tell them to anymore?
> The park rating is climbing. Your flagship coaster is printing money. Guests are happy, for now. But you know what's coming: the inevitable cascade of breakdowns, the trash piling up by the exits, the queue times spiraling out of control.
A linear puzzle game like that I would just expect the ai to fly through first time, considering it has probably read 30 years of guides and walkthroughs.
not just make up bullshit about events
Gemini models are a little bit better about spatial reasoning, but we’re still not there yet because these models were not designed to do spatial reasoning they were designed to process text
In my development, I also use the ascii matrix technique.
It really seems to me that the first AI company getting to implement "spatial awareness" vector tokens and integrating them neatly with the other conventional text, image and sound tokens will be reaping huge rewards. Some are already partnering with robot companies, it's only a matter of time before one of those gets there.
As far as 3d I don't have experience however it could be quite awful at that
pretty heavy/slow javascript but pretty functional nonetheless...
It was interesting that the poster vibe-coded (I'm assuming) the CTL from scratch; Claude was probably pretty good at doing that, and that task could likely have been completed in an afternoon.
Pairing the CTL with the CLI makes sense, as that's the only way to gain feedback from the game. Claude can't easily do spatial recognition (yet).
A project like this would entirely depend on the game being open source. I've seen some very impressive applications of AI online with closed-source games and entire algorithms dedicated to visual reasoning.
I'm still trying to figure out how this guy: https://www.youtube.com/watch?v=Doec5gxhT_U
Was able to have AI learn to play Mario Kart nearly perfectly. I find his work to be very impressive.
I guess because RCT2 is more data-driven than visually challenging, this solution works well, but having an LLM try to play a racing game sounds like it would be disastrous.
An LLM could potentially make events far more aimed at your character, and could actually respond to things happening in the world far more than what the game currently does. It could really create some cool emerging gameplay.
But isn't the criticism rather that there are too many (as you say repetitive, not relevant) events - its not like there are cool stories emerging from the underlying game mechanics anymore ("grand strategy") but players have to click through these boring predetermined events again and again.
HN second-chance pool shenanigans.
Genuinely interested.