Flagship OpenAI/Anthropic models refuse this task due to "Cybersecurity" so I have no idea how good flagship models do. It's unfortunate as IMO minecraft is a sandbox

Java clients (pc version) use the actual game's files modified to not open a window. Minecraft is source available, anyone can load it into an IDE, modify it to double jump height, and run it in minutes without an unmodified server caring. Bedrock clients (the version for phones) just figure it out based on packets and how the anticheat corrects them to what the movement should be.

I give it access to write manual packet sequences and a server to try to break my logic, such as crashing the application, giving it an open ended arena to fall 10 blocks without taking damage, or just trying to jump higher than usual.

It requires a bit of pushing to know what type of issues it should even be looking for. Telling it to move even just 0.00001 blocks upwards to reset fall damage mid-fall. Telling it to figure out how to fake being on the ground to jump mid-air to reach the impossible platform. This all used to be done manually, but paying a couple dollars to run it overnight and attempt to find bypasses is worth the cost.

I haven't figured out how to run LLMs to write code 24/7 yet, they just can't see the big picture.

Java does save chunks after generation, likely due to world gen updates not seamlessly transitioning with older versions of the game and PCs not caring about save size.

Who is using Fable?

At work, it was able to one shot a dashboard. Of course, my prompts are vague as I'm not exactly sure what I want yet, but it did a better job than I could do as a backend dev forced to work on frontend sometimes.

Usage is also great, it just feels so much more efficient than older models in terms of thinking and time. Cost is barely better though.

It can burn a million tokens in less than a minute, at least at launch where there's likely less load on the servers.

At home, it feels like I'm fighting the AI less while letting it refactor code. I'm glad that I left this 12,000 line vibe coded port of a hand written codebase to future models to refactor. It feels like the model has better judgement than old models that would destroy your codebase so long as it meant accomplishing your prompt.

I'm almost disappointed that it's this good.

Comments URL: https://news.ycombinator.com/item?id=48289049

Points: 4

# Comments: 0

https://github.com/GrimAnticheat/Grim/pull/1131

GPT 5.4 and Claude Opus/Sonnet 4.5/4.6 are now billed at API rates for all users, even enterprise customers. Previously, they were subsidizing these models around a factor of 10x, billing per request and not per token. Composer 2 bills for $0.08 per request on the fast model, and $0.04 per request on the slower model - no matter the tokens used.

It seems like they are targeting Enterprise above all else, relying on a enterprises signing up a bunch of paying users that rarely touch Cursor to subsidize the power users using an excessive amount of Composer tokens. It's a fair strategy, as cursor seems to increase output by 20-30% so the price is well worth it for Enterprise customers.

This is a key candidates to use AI as we have built hundreds of warehouses in the past. We have a standard product that spans over a hundred thousand lines of code to build upon. Still, we rely on copying code from previous projects if features have been implemented before. We have stopped investing in the product to migrate everything to microservices, for some reason, so this code copying is increasingly common as projects keep getting more complex.

Teams to implement warehouses are generally around eight developers. We are given a design spec to implement, which usually spans a few hundred pages.

AI has over doubled the speed at which I can write backend code. We've done the same task so many times before with previous warehouses, that we have a gold mine of patterns that AI can pick up on if we have a folder of previous projects that it can read. I also feel that the code I write is higher quality, though I have to think more about the design as previously I would realize something wouldn't work whilst writing the code. At GWT though, it's hopeless as there's almost no public GWT projects to train an AI on. It's also very helpful in tracing logs and debugging.

We use Cursor. I was able to use $1,300 tokens worth of Claude Opus 4.6 for a cost of $100 to the company. Sadly, Cursor discontinued it's legacy pricing model due to it being unsustainable, so only the non-frontier models are priced low enough to consistently use. I'm not sure what I'm going to do when this new pricing model takes affect tomorrow, I guess I will have to go back to writing code by hand or figure out how to use models like Gemini 3.1. GPT models also write decent code, but they are always so paranoid and strictly follow prompts to their own detriment. Gemini just feels unstable and inconsistent, though it does write higher quality code.

I'm not being paid any more for doubling my output, so it's not the end of the world if I have to go back to writing code by hand.