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Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?

charlesazam.com

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Re: Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?

#91
post #83

Earlier quoted context omitted.

It's part of the rationale for why one would choose Pi over Claude, even if Pi lacks the feature. I admit my tone is entirely inappropriate though.

> It's part of the rationale for why one would choose Pi over Claude It's unrelated to my direct comment and it's a generic tangent off the thread further up, which is about technical issues with Claude Code. As the guidelines say: "On-Topic: Anything that good hackers would find interesting. That includes more than hacking and startups. If you had to reduce it to a sentence, the answer might be: anything that gratif…

Fair point

Re: Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?

#92

What is /goal?

An agent is an llm running on a loop until it decides it should stop. /goal is a gimmick where you run a "parent" agent on top that runs the agent on a loop until the it decides to stop, just prompting it "nope, not done yet, continue".

isn't this what they called a "ralph loop"?

Re: Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?

#93
post #71
post #61

Great eval! If you are comparing search strategies, ultra mode is likely superior. Would love to see a follow-up evaluating that. Ultra can fan out parallel investigators, run adversarial review at defined checkpoints, and do a bunch of other smart stuff to avoid getting stuck in a local optimum. Generally as the OP notes, /goal works better for single-track investigations or small scale scatter/gather.

I think ultra mode needs to be more clearly documented (or perhaps cautioned against!). Most devs - myself included - who saw “ultra” mode figured that it’s just a magic bullet that makes the model work harder and achieve better results. But, for many tasks, ultra mode is possibly worse and certainly more expensive.

Yeah I think having a detailed mental model of what it’s actually doing helps.

At least for Fable/Opus (didn’t confirm for Sol yet) Ultra means “write an ephemeral programmatic harness encoding this workflow”. There is actually a TS harness that gets run for the workflow.

If you have a task where the agent/sub-agent pattern works, Ultra just adds indirection.

I think it is possible to get an intuition for an individual model but really you need to eval to be sure. My heuristic though is if you need to treat each work item differently depending on the results, probably agent/subagent. If you want to do the same steps across some queue / tree / DAG of work items, ultra is a better bet. (Or actually write a durable scaffold if you are going to repeatedly run it over > thousands of items.)

Re: Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?

#94
post #52

Earlier quoted context omitted.

On Claude if you start with that, it won't stop until it achieves or exhausts your prompt. It feels like "here's your mission, go do it". I use it a few times a week.

I'm way too afraid of a "paperclip problem"-style target overfitting to use it really; how could I even describe a goal so well it doesn't sacrifice other things that are important to me? Like, if my goal is to make an endpoint faster, will it create an over-engineered mess out of the clearly readable code I have to reach that goal?

  /goal open a draft PR that makes  ~200ms faster while still passing tests, and adhering to nearby style+pattern
It's obviously not perfect, but it's not like you're going to YOLO it into prod without evaluating it on whatever you care about.

If you want to be able to use it like that, you need to find a way to encode (most of) your concerns into something that can be programmatically verified.

Re: Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?

#95

Earlier quoted context omitted.

Adding a new lint rule that a lot of code violates.

And that doesn't work with a simple prompt?

It's just a way of defining what "done" means, and passing off evaluation of whether "it's done" is true to a separate context and/or model. If false, it prompts the original context accordingly.

Re: Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?

#97
post #78
post #26

Earlier quoted context omitted.

Compacting at all is a mistake. With 1m context window there is no reason for a single task to require compaction. Much better to spend tokens breaking the task into chunks, documenting and storing them durably, then executing each one in clean context and just /clear after. It’s a similar concept to compaction, just planned in advance. Much much more effective, and doesn’t burn tokens and time (“wall-clock”, Claude)…

You just haven’t worked on tasks that are complicated enough. Occasionally it took more than 1M tokens just to come up with a plausible plan. Personally I find using /rewind judiciously is better than using /compact. The latter essentially gives you no control of what details to discard, but the former at least has coarse-grained control.

Have you ever looked at how much performance drops as context grows? The difference in intelligence between 100k and 1M is huge, like opus drops to haiku level performance, or worse. For that reason I try to keep under 200k. That feels about the upper bound for tasks requiring accuracy.

Re: Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?

#98

Earlier quoted context omitted.

On Claude if you start with that, it won't stop until it achieves or exhausts your prompt. It feels like "here's your mission, go do it". I use it a few times a week.

Is this useful? I feel like the problem is usually not that the model isn't capable of achieving what I give it, but the way it does it. Especially if originally I didn't 100% know how I would do it myself the model often takes weird paths through the code base, takes shortcuts that end up in weird feature interactions or pulls in a dependency without weighting if it could've been done without that. I haven't really…

For some frontier models like Fable 5 it doesn't matter, but for models less trained on long horizon tasks it very useful.

Re: Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?

#99
post #26

Earlier quoted context omitted.

Compacting at all is a mistake. With 1m context window there is no reason for a single task to require compaction. Much better to spend tokens breaking the task into chunks, documenting and storing them durably, then executing each one in clean context and just /clear after. It’s a similar concept to compaction, just planned in advance. Much much more effective, and doesn’t burn tokens and time (“wall-clock”, Claude)…

> With 1m context window there is no reason for a single task to require compaction Only if money is no object. Cache reads are cheap (10% of uncached input costs) but definitely not free, and cached reads dominate session costs at long context lengths. A prompt at 20k context with $0.01 in cached reads would cost $0.40 in cached reads at 800k context, that quickly adds up for long sessions.

I’m not following the implication that these economics argue for jsing compaction instead of just clearing context?

Re: Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?

#100
post #78
post #26

Earlier quoted context omitted.

Compacting at all is a mistake. With 1m context window there is no reason for a single task to require compaction. Much better to spend tokens breaking the task into chunks, documenting and storing them durably, then executing each one in clean context and just /clear after. It’s a similar concept to compaction, just planned in advance. Much much more effective, and doesn’t burn tokens and time (“wall-clock”, Claude)…

You just haven’t worked on tasks that are complicated enough. Occasionally it took more than 1M tokens just to come up with a plausible plan. Personally I find using /rewind judiciously is better than using /compact. The latter essentially gives you no control of what details to discard, but the former at least has coarse-grained control.

Oh my goodness. I’ve used over 1B tokens on a single feature. I’m running at about 25B tokens/month right now.

My whole point was that by planning in advance you can shard the work into manageable sections with clear beginnings and outputs with acceptance croteria, and never compact, or even use more than a few hundfed thousand tokens in context.

It’s all hierarchical. Looking at an eval feature building right now, it’s 20ish build plans, each with zero to five or so /clear moments.

But maybe that’s the key thing… I don’t iteratively prompt ad hoc software writing. I do iterate on requirements, but if those are solid enough there is no “now write this function, now write that module”.

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