What is /goal?
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Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?
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Re: Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?
#22Claude seems to forget what you tell it in very long work sessions (things that take weeks to develop), no matter how many times you tell it which part is extra important. I dont use goal (I guess I should), but presumably it makes it actually remember the most important instruction. I believe this here is about shorter sessions where the issue doesn't crop up as much.
Stop using compaction. Stop treating sessions as something you keep alive for weeks. Set up a proper workflow for handing off work and start handing off well before maximum context is hit. It's less efficient, slower and more costly to constantly be sitting at high context and compaction is just not good.
Re: Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?
#23/goal has replaced plan mode for me. This is the pattern I use for 95% of my AI work now: 1. Read X feature of Y and tell me when you fully understand it (if there's any detail missing in the summary, repeat until the context is primed) 2. What time is it? 3. /goal Spend X minutes from $time writing a technical design doc on $feature. There must not be any vague language or ambiguity in the document. Read carry_forwa…
The hypothesis generation phase is looking like the most critical part of having an agent reliably hit your targets. Simply starting in the correct part of the search space is probably the biggest predictor of success. Forcing one big loop to fight its way through all the hypotheticals from zero looks like a dead end for many practical scenarios, regardless of how powerful the model is. I think you could draw some an…
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Re: Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?
#24Claude seems to forget what you tell it in very long work sessions (things that take weeks to develop), no matter how many times you tell it which part is extra important. I dont use goal (I guess I should), but presumably it makes it actually remember the most important instruction. I believe this here is about shorter sessions where the issue doesn't crop up as much.
This is one of the advantages of pi. I made a /protect command that protects the message from compaction. I also protect skills automatically.
So for long running tasks I'll do
/protect your goal is...Re: Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?
#25/goal has replaced plan mode for me. This is the pattern I use for 95% of my AI work now: 1. Read X feature of Y and tell me when you fully understand it (if there's any detail missing in the summary, repeat until the context is primed) 2. What time is it? 3. /goal Spend X minutes from $time writing a technical design doc on $feature. There must not be any vague language or ambiguity in the document. Read carry_forwa…
> 3. /goal Spend X minutes from $time writing a technical design doc on $feature. Hmm, I feel like this is akin to making a recursive function have a exit condition not based on what it actually did/found, but based on how long time it took. I'm always using /goal with explicit goals that the agent needs to achieve. Time-bounding them wouldn't make sense, I want something specific done regardless of how long time it…
Leaving to run unhindered makes sense when the agents understand both the complete domain context and can be supplied with a strong exit criteria that they understand well.
Otherwise time boxing is both going to help stop entities from wandering off into the weeds. And also communicate expectations from the commissioner about the expected effort levels and output quality requirements.
Empirically in human world, get very different results when an employee, particularly a junior, is asked to spend 1/2 a day on a work package, a week or are left completely to their own devices.
Re: Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?
#26Claude seems to forget what you tell it in very long work sessions (things that take weeks to develop), no matter how many times you tell it which part is extra important. I dont use goal (I guess I should), but presumably it makes it actually remember the most important instruction. I believe this here is about shorter sessions where the issue doesn't crop up as much.
Unfortunately I’ve used Claude and GPT models for a long time in a variety of harnesses and I agree with you and I think it’s the compaction. For some reason, codex compaction is like black magic. I’ve never felt like I can just one one continuous thread with other models, Claude I carefully curate when I compact
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) doing the compaction.
Re: Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?
#27If you're curious what the actual optimal Paris cost is, I suggest formulating the problem as an integer linear program and submitting it to Gurobi on NEOS [0]. Gurobi is arguably the strongest commercial ILP solver; big companies pay big dollars to use it to optimise schedules, industrial processes, etc. I'm not sure it could solve this problem to optimality in the 8 hours NEOS provides you, but it might -- KIRO has some similarities to the Vehicle Routing Problem, variants of which are very commercially important. In any case, Gurobi is a monster, and even if you don't get an exact solution, it will give you a lower bound (which may not be tight, but it's nonetheless interesting).
Re: Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?
#28What is /goal?
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 found a good way to solve this other than:
1. Produce an initial PR fulfilling all the requirements I knew at the start
2. Chat with the model about any weird snippets I notice and talk through alternatives
3. Simplify anything that I think is overengineered or plain unncessary
Sometimes I restart all over with more precise requirements but then it sometimes makes different mistakes/takes different shortcuts.
In practice the earlier I review the better the end result imo, so /goal seems very unproductive to me?
Re: Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?
#29Both are entirely useless on complex problem, because they have a bias in training data they can only partially detect in their own output. The answers are getting worse and worse as you dive deeper into the topic you are working on. I thought I could sharpen one of the documents I worked on using Opus 4.8 and GPT-5.5 together. Fable 5 and GPT-5.6 completely destroyed it. Not only it is not human readable anymore, but also doesn't make sense.
Re: Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?
#30Claude seems to forget what you tell it in very long work sessions (things that take weeks to develop), no matter how many times you tell it which part is extra important. I dont use goal (I guess I should), but presumably it makes it actually remember the most important instruction. I believe this here is about shorter sessions where the issue doesn't crop up as much.
Quality degrades long before you hit compaction, compaction is the "Next gas station, 100 miles" sign but you're already in the middle of nowhere.
You don't need to go down the rabbit hole of crazy workflows, but to avoid slop:
- Break down the work into tasks
- New context. Create a plan for one task.
- New context. Implement the plan.
- New context. /code-review the implementation
- New context. Fix the review findings.
- Repeat for next task.
I do this with Fable 5, and the quality is consistently quite good. If the context goes over 50%, the quality will become crap and you end up with 4 duplicates of the same thing across the codebase. Letting a current session review its own work is like asking a student to grade their own paper.