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The short leash AI coding method for beating Fable

blog.okturtles.org

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Re: The short leash AI coding method for beating Fable

#31

Post by ofjcihen »

semiquaver wrote: post #29
moezd wrote: post #18

LLMs are still next token predictors, just because you can give it more vague instructions and it still finds the right steps to follow, it doesn't mean it's intelligent. It means you're speaking the same language as the harness they trained your model on. And that has a limit. If you are stuck at PoC level or simple apps, you have no idea how limited the current models still are. There you really need to break tasks…

Yeah, and you’re just a next-word-sayer.

I love this argument. Not because it’s true but because it betrays the posters doubt in their own sentience.

Re: The short leash AI coding method for beating Fable

#32

Post by guessmyname »

johndough wrote: post #14
guessmyname wrote: post #8

Earlier quoted context omitted.

As a Mythos user (I’m part of Project Glasswing), I would say that abliterated models [1][2] produce similar, if not identical, results. While good prompting and steering won’t give Claude Opus 4.8 the same capabilities as Mythos (preview 1), using abliterated models (if you have the computational power to run the larger ones) will get you close to the same goals as people who have access to Mythos (preview 1) [3]. […

Any specific abliterated big models you can recommend?

There are many out there, depends on what you need them for:

• https://huggingface.co/huihui-ai/Huihui-GLM-5.2-abliterated-...

• https://huggingface.co/huihui-ai/Huihui-Kimi-K2.5-BF16-ablit...

• https://huggingface.co/huihui-ai/Huihui-Qwen3.5-397B-A17B-ab...

• https://huggingface.co/huihui-ai/Huihui-DeepSeek-V4-Flash-ab...

• https://huggingface.co/huihui-ai/Huihui-Qwen3-VL-235B-A22B-I...

• https://huggingface.co/huihui-ai/Huihui-Qwythos-9B-Claude-My...

• … so on and so forth.

Re: The short leash AI coding method for beating Fable

#33

Post by root_axis »

semiquaver wrote: post #29
moezd wrote: post #18

LLMs are still next token predictors, just because you can give it more vague instructions and it still finds the right steps to follow, it doesn't mean it's intelligent. It means you're speaking the same language as the harness they trained your model on. And that has a limit. If you are stuck at PoC level or simple apps, you have no idea how limited the current models still are. There you really need to break tasks…

Yeah, and you’re just a next-word-sayer.

This is wrong. Human thinking and speech isn't autoregressive like LLM inference.

Re: The short leash AI coding method for beating Fable

#34

Post by ed_mercer »

I feel like OP is still in the year 2025.

> The AI will have gone off the rails multiple times and you will only notice it later when you actually try to use the software.

Except that said AI can now themselves use your software and find and fix bugs themselves, not to mention drive new features.

>Your agent might go “off the rails” and start doing something you don’t want it to do

This happens but far less often than it used to, and the case for full autonomous agents is getting stronger, not weaker.

>It is humanly impossible to build your own understanding of a codebase

This again feels outdated. I think we're mving towards humans no longer needing to understand a codebase, and letting AI drive it.

Re: The short leash AI coding method for beating Fable

#35

Post by infinite_spin »

moezd wrote: post #18

LLMs are still next token predictors, just because you can give it more vague instructions and it still finds the right steps to follow, it doesn't mean it's intelligent. It means you're speaking the same language as the harness they trained your model on. And that has a limit. If you are stuck at PoC level or simple apps, you have no idea how limited the current models still are. There you really need to break tasks…

> it doesn't mean it's intelligent

I'm not sure how you're defining "intelligent", but I'd like to know how it is able to exclude a language model, while still including humans, without simply defining it with an axiom that predefines LLMs as lacking intelligence.

Re: The short leash AI coding method for beating Fable

#36

Post by infinite_spin »

root_axis wrote: post #33
semiquaver wrote: post #29

Earlier quoted context omitted.

Yeah, and you’re just a next-word-sayer.

This is wrong. Human thinking and speech isn't autoregressive like LLM inference.

while the how is different, the what has many parallels. E.g. both the brain and LLMs appear to learn distributions of representations, they both develop a hierarchy of those representations, both have early layers that process simple features, with later ones processing more abstract concepts, both predict missing information...

Re: The short leash AI coding method for beating Fable

#37

Post by [deleted] »

ed_mercer wrote: post #34

I feel like OP is still in the year 2025. > The AI will have gone off the rails multiple times and you will only notice it later when you actually try to use the software. Except that said AI can now themselves use your software and find and fix bugs themselves, not to mention drive new features. >Your agent might go “off the rails” and start doing something you don’t want it to do This happens but far less often tha…

[deleted]

Re: The short leash AI coding method for beating Fable

#38

Post by semiquaver »

root_axis wrote: post #33
semiquaver wrote: post #29

Earlier quoted context omitted.

Yeah, and you’re just a next-word-sayer.

This is wrong. Human thinking and speech isn't autoregressive like LLM inference.

Do you not say your words one-at-a-time like everyone else? Otherwise I can’t see how my comment is “wrong”

Re: The short leash AI coding method for beating Fable

#39

Post by ACCount37 »

moezd wrote: post #18

LLMs are still next token predictors, just because you can give it more vague instructions and it still finds the right steps to follow, it doesn't mean it's intelligent. It means you're speaking the same language as the harness they trained your model on. And that has a limit. If you are stuck at PoC level or simple apps, you have no idea how limited the current models still are. There you really need to break tasks…

"Next token prediction" is an interface, not an algorithm. A process that "predicts next tokens" can be arbitrarily complex or simple, and arbitrarily capable or incapable of performing a given task.

Saying that an LLM can or can't do something because it's a "token predictor" is a category error. The interface isn't a hard limit.

Re: The short leash AI coding method for beating Fable

#40

Post by 1123581321 »

gambiting wrote: post #13
jonplackett wrote: post #6

I thought this was how everyone who can actually code uses AI for anything that’s actually important. Am I wrong? Are you guys just YOLOing everything these days?

>>You never use “YOLO” mode (aka “dangerously skip permissions”) Do you mean this? I'm curious how are people using Claude in any way other than bypass-permissions. I've tried for so long to maintain a curated list of things Claude can use, but inevitably I would always come back only to find it stuck because it decided to pipe an output of one tool into another and that's not explicitly allowed so it stopped even th…

I did it by making a huge database of allowlisted bash and having hooks check each one against the list. It makes a recursively parsed tree so it can handle gnarly blocks of bash. And then it outputs to the agent what failed and tells it to break it up next time. Then, in agent instructions, I impress on it strongly to use composable bash tools rather than trying to write python/ruby/perl scripts.

It was a bit of work, admittedly, but it's picked up a few users and I learned a lot from designing the research process and parsing the syntax trees.

I actually want to be alerted about everything that's not auto-approved, though. With safe commands auto-approved, it's much less noisy. I think it's important to read your code, as it develops, not just at the end, and understand what agents are doing.

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