quantumHazer

Fable didn't even need to debug it; I believe essentially all the assembly worked correctly on the first try.

so you don't know if it works properly or not

Probably llm slop

There are many ways to build for the web, both with and without AI. If AI is a part of your workflow, we think this tool will help make it even more productive. And if it isn’t, that’s OK too.

Crazy thing to say in 2026 where if you write code and not delegate every bit to an agent you're considered a noob by some people.

also, there is multiple models called codex or that have codex as a "suffix" eg. "gpt-5.3-codex"

you can’t iterate fast over quality though. it takes patience and expertise, not a bloated repo like this.

every example you mentioned is not something you should delegate to LLMs, unless quick prototyping

not using GenAI could mean project won't be competitive in current landscape

why? this is false in my opinion, iterating fast is not a good indicator of quality nor competitiveness

the author is an employee of Cursor/Anysphere. i think this should've been declared at the start of the article. it's always like this.

Not unpopular at all. I think it's one of the best Ferrari ever made.

Or maybe, hear me out, we don't need any of this ""agent"" first shiny thingy

I wouldn’t be surprised if this is undisclosed PR from Anthropic

Why are we commenting the Claude subreddit?

1) it’s not impartial

2) it’s useless hype commentary

3) it’s literally astroturfing at this point

Seems pretty false if you look at the model card and web site of Opus 4.5 that is… (check notes) their latest model.

it won’t make sense to learn how to code.

Sure. So we can keep paying money to your employer, Anthropic, right?

Last year’s model were at 50-60% on SWE bench-verified actually

there is also Normal Computing[0] that are trying different approaches to chips like that. Anyway these are very difficult problems and Extropic already abandoned some of their initial claims about superconductors to pivot to more classical CMOS circuits[1]

[0] https://www.normalcomputing.com

[1] https://www.zach.be/p/making-unconventional-computing-practi...

there is also Normal Computing[0] that are trying different approaches to chips like that.

[0] https://www.normalcomputing.com

yess, but: https://arxiv.org/pdf/2303.10728

dev bootcamp

i will not comment any further

just admit it, interns are struggling competing with Claude Code, Cursor and Codex

They are not. This is false, zirp ended, this is the problem. Not LLMs.

Upload your pdf so we can gather more training data but you’re providing it!

Both of you are wrong and this is not good discussion level for HN

This comments shows that most of the users in this thread have not done game development.

We are judging the usefulness of a LLM in a project that is really over represented in training data (a bunch of Intro to Programming courses requires a project like this, and in southern Europe countries we have a lot of similar games like the one in the blog)

In the first year of college I reimplemented Moon Patrol in Python, I had no previous experience basically and it tooks me two/three months of work coding 3 days per week mostly.

Coding a card game is easier than that. LLMs are useful for certain things but this is not a good way to benchmark their usefulness in any type of game development coding.

this is false and the two things are not correlated.

if you followed news during the GAN cycle you could extrapolate that deep NN could do this type of things. it is really cool that this things happened so fast, but we are talking about companies that have the money to deploy thousands of cars around the globe to collect data, so they absolutely know how to gather data

It is also true that models doesn't give a ** about instructions sometimes and the do whatever text predictions is more likely (even with reasoning)

a 1 minutes research on the internet led me to discover that you are MARKETING MANAGER at amazon. so your take is full of conflict of interest and this should be disclosed.

a 1 minutes research on the internet led me to discover that you are MARKETING MANAGER at amazon. so your take is full of conflict of interest and this should be disclosed.

yeah, tipically they are building a to do list and organizer app and have not found that github is flooded with college students' project of their revolutionary to-do apps

it's very well documented behavior that models try to pass failed test with hacks and tricks (hard coding solutions and so on)

It's open-weight, not open source though

Machine learning as it is needs human data and input to progress further.

Synthetic data can be useful until a certain point, but you can’t expect to have a better model on synthetic data alone indefinitely.

The moat of GDM here is YouTube. That have a bazillion of gameplay and whatever videos. But here it is.

The downside I can see is that most people will stop to publish content online for free since this companies have absolutely no respect whatsoever for the humans that created the data they use.