61–70 of 134 posts
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.
Anthropic is losing bigtime against Open AI in coding space. I was using Claude code upto last march. Not an enterprise customer, but a responsible AI user where i don't over spend and use basic plan to manage repository with 400Kloc all together. We sell to local government and a team of 3. Claude code was super slow, never able to fix issues properly.(Despite with proper test cases, observability, documentation and…
I've found Opus 4.8 pretty amazing, and Codex a bit meh. YMMV?
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)…
Most models’ reasoning abilities drops off significantly between the 256K-1M token ranges of the context window. There’s too much stuff to “pay attention to” at once.
...is this not a Travelling Salesman Problem?
Related, but not quite the same. The solution to this problem can have more than one loop, whereas TSP definitionally has only one. Not quite sure how that changes the difficulty or the tools that you’d use.
Or I think my PTSD on advanced algorithm course kicked in
Edit: looks like the closet reduction should be https://en.wikipedia.org/wiki/Ring_star_problem with bounded circuit length
I love that we have this on one hand and me cleaning up catastrophic CSS made by Sol on the other. Then again, maybe CSS is the ultimate benchmark.
I do not know the whole picture, but if you are asking for blind one-shot CSS, you might benefit from wiring the model to take screenshots of various end-browsers and discuss them as you iterate. Offering freelance estimates for CSS design changes before frameworks were around was a problem.
What is /goal?
Both Codex and Claude Code have it, but they work slightly differently.
Claude Code uses Haiku to read through the transcript and decide if the goal has been completed. If not, Haiku injects a prompt back to the main model to indicate what still needs to be done.
In Codex, instead it's a tool available to the main model, plus some part of the surrounding harness that will re-prompt it if the tool calls haven't yet indicated that the goal is complete.
The issue that they are trying to solve is that sometimes models will stop before they have actually fully completed whatever task they were given; attention isn't perfect, and someitmes they'll complete part of it but not the whole task. Rather than making the user come back and re-prompt to keep going, they add a way to automatically do a bit more nudging to try to get the model to finish the task.
/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…
It reminds me of this printing trick “until you are 95% confident”…
Would really be interested to know how this influences the work of the LLM. Also, how would it be if I write “until you certainly understand it”?
Claude 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...
My impression is that it is about as intelligent as 5.5, but they dialed up the relentlessness meter to eleven. This makes it more likely that it will accomplish the task you give it, which I think is the primary reason it looks competitive in benchmarks. However, it also makes it more likely that it will resort to... unconventional, weird or outright unsafe methods to do it. So I have to watch it like a hawk.
The other day it tried to read env variables from prod using a CLI command. The task it was working on did not necessitate doing that even remotely. I have the SSH keys for that particular CLI tool tied to my 1Password. So when the agent failed (because I never authenticated the SSH key access), it wanted to take over the computer, for which I got an OS prompt. At that point I stopped the agent and asked it why it did that. It said it wanted to dig around 1Password itself to see if it could get the key. I asked it why it needed prod env variables, and it thought for a bit and admitted it actually shouldn't. So as of yesterday I stopped using the "approve for me" mode and now use it only for simpler tweaks and bug fixes.
Fable is not only more intelligent, but also way more insightful. It can sniff out my intent far more effectively, and its "real world" knowledge allows it to act as a seasoned product manager with domain expertise. It can also think outside the box and make suggestions that I would not have thought of. With GPT 5.6 I have to be way more literal.