Let me be clear, he's no cormac mccarthy(my favorite prose is the passage from blood meridian describing the raiders). My bar for fan fiction prose is basically whether it annoys me, its a low bar. Most of what makes hpmor good is the cleverness with which yudkowsky subverts the original harry potter world to try to make a rationalist argument, whether that be by inverting a character or playing off the harry/voldemort relationship in thoughtful ways.
'"Voldemort," said the old wizard. "I understand him now at last. Because to believe that the world is truly like that, you must believe there is no justice in it, that it is woven of darkness at its core. I asked you why he became a monster, and you could give no reason. And if I could ask him, I suppose, his answer would be: Why not?
They stood there gazing into each other's eyes, the old wizard in his robes, and the young boy with the lightning-bolt scar on his forehead.
"Tell me, Harry," said the old wizard, "will you become a monster?"
"No," said the boy, an iron certainty in his voice.
"Why not?" said the old wizard.'
as you can see its nothing special, just good enough
I see, thanks. It reminds me a little of those spam emails which are sprinkled, intentionally, with obvious spelling errors. They aren't trying to trick the average person, they are trying to filter for a much smaller, more valuable audience.
fwiw, I don't think that was intentional here. He's just more interested in "ideas" than prose or people. A more extreme example of Andy Weir. Definitely a niche. I read it years ago while backpacking and it perhaps needed an editor but the overall struggle/war and reimagining of logical fallacies of the HP universe's magic were fun.
yes exactly. Fan fiction readers largely do not care about grammar or nice prose, just has to not be overly distracting which unfortunately most fan fic fails at
It is getting a lot harder for those people to justify using OpenAI to assist such endeavours. Afterall, OpenAI might just front-run you if they hear a rumor you solved some marquee problem that they can brag about in PR campaigns.
artificialanalysis just updated their benchmark after the release of GPT-6. They removed old, saturated benchmarks and replaced them with new, until GPT-6 floated to the top with the cream. One of those new benchmarks is AutomationBench-AA, where GPT-6 had a clear lead. Today that benchmark is topped by DeepSeek v4.1 Flash.
Edit: For those who are not familiar with it, this model is quite a bit faster, and about 100x cheaper, per token, than Fable and Astra.
> For instance the task naming in the task file starts with an optimistic 1, 2, 3, 5, 5a but then eventually gets to 8a, 8a1, and then ends up with 8b2c2b3 and “8b2c2b2b checkpoint1”. The code that it produced got ever more wild. I don’t want to bore you with what it tried to build, but here are some example pieces of the interpreter changes:
Hardcoded constants everywhere
Multiple same-line macro invocations in C
Random indexes in production code
Hideous tokenizer code in C
Astra scores the same on DeepSWE 1.1 (~75%) as Gemini Flash 3.8 and Deeepseek Flash 4.1
So general coding ability has plateaued, for now.
Also consider the context windows. 1M token models where a breakthrough two years ago. Today they are still limited to 1M. In fact, if you don't want intelligence to drop off a cliff, you are really limited to 200k tokens.
Gemini Flash is a joke for coding. If you can get the same output as you can get with Sol/Astra I'm impressed. Not to mention that Antigravity is awful.
It is not a universal opinion at all that general coding ability has plateaued.
Is there a reason they scoped that so narrowly to Buckmaster/codex/2 months
two people worked on this for a year before the breakthrough. Perhaps that earlier work reduced the search space sufficiently to brute force the problem with 10,000 agents?
Just knowing that there had been progress is enough to have an idea that throwing more compute at it might work (OpenAI had previously tried all the Millennium Prize problems with somewhat limited compute and failed).
It's comparable to Magnus Carlson saying that if he wanted to cheat, all he would need would be for someone to tell him to spend more time thinking about a specific move (just a wink would be enough) as an indication that a computer had found something interesting.
It's as-if after OpenAI first failing on Navier-Stokes (which OpenAI had just tweeted about 2 days earlier!), someone winked at them and said "you might want to try a little harder ...".
The comment you replied to quoted "no user inputs after July 3rd" with no restriction to Buckmaster or Codex.
Obviously the result of OpenAI's investigation was that no usage data has interacted with the system after that date.
What else do you expect them to investigate?
If Buckmaster and co. provide their chats, OpenAI could potentially search for them in the anonymized opted-in usage data. Then they could say if any data has been used.
By all accounts individual usage data does not have the direct impact on the model most here fantasize about. To prove this, OpenAI would need to do new training runs to replicate the system used minus the particular usage data in question, if it exists, and then benchmark this on the problem again.
Potentially multiple times, in order to reach a conclusion.
Openai said that a new model became available to them during this. But that could mean anything from a big new base model to a LoRA, fine-tuned on a few dozen prompts...
hmm I'm hoping there is a bug on their API because my first impression is not good. I asked it to return bash code between <bash></bash> tags. It is failing frequently and writing it's own tool calling format instead.
Quite a flex calling their GPT-6 competitor "Flash"! But it is faster than their last flash model due to a combination of architectural innovations including engrams and a new encoder/decoder design that uses 8B parameters for prefill and 16B for generation.
This is definitely not on par with GPT-6 astra. Not with GPT-5.6 sol either. But probably will set as a new baseline for modern API based LLM because it's so cheap.
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