twapi · 299 points · 173 comments · hier · Open original
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SUsuperasnhier
Recently I came across the /handoff skill, which I've been using a lot. I find it much better than /compact.
Basically:
- /handoff file creates a short document with the important context from your current session and maybe next steps as checklist.
- You can then start a fresh session with /continue file
- You can also hand the work from Claude to ChatGPT, or the other way around. Very useful at time of session limits.
- Plus your handoff files becomes a useful piece of project memory that you can reference later.
I find this much more useful than /compact or /clear because the context is saved in something portable instead of being tied to one session and i've seen better results doing this every 20 messages or so than running long sessions.
RHrhakswhier
> @-mention files instead of naming them
Love Claude, but the @ mention is broken in the desktop app. For the same project if I type the same query "@ephem" I get:
CLI: https://imgur.com/a/VZMUCOa (good, relevant results)
Desktop: https://imgur.com/a/QLSo4Ms (bad, irrelevant)
Opened issue for this and it was automatically closed:
https://github.com/anthropics/claude-code/issues/71421
I could have written the issue better (using CLI as comparison instead of VS Code). But, no doubt in my mind Claude could fix this itself in a minute.
GLGlyptodonhier
What I see is that I have to read a bunch of stuff and go through a bunch of hassle to save money when the root of it is that if I tell an AI to do work on a task while I'm busy with something else and come back later I've doubled my cost because the cache expires too quickly?
JNjnwatsonhier
Can anyone explain why the prefix cache is tied to effort?
I frequently run Fable at xhigh effort to run statistical modeling way above my undergraduate understanding. Claude Fable produces Masters-degree level output, and then I spend lots of round trips asking it to explain different parts to me.
The first part absolutely uses the extra effort, but the interrogation exercise is something a much simpler model, or the same model with much less effort, could answer.
MOmojubail y a 11 heures
This is all good to know, but funny how we are suddenly back to formal languages and commands. Aren't these things intelligent enough to figure these things out for us?
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Recently I came across the /handoff skill, which I've been using a lot. I find it much better than /compact. Basically: - /handoff file creates a short document with the important context from your current session and maybe next steps as checklist. - You can then start a fresh session with /continue file - You can also hand the work from Claude to ChatGPT, or the other way around. Very useful at time of session limits. - Plus your handoff files becomes a useful piece of project memory that you can reference later. I find this much more useful than /compact or /clear because the context is saved in something portable instead of being tied to one session and i've seen better results doing this every 20 messages or so than running long sessions.
> @-mention files instead of naming them Love Claude, but the @ mention is broken in the desktop app. For the same project if I type the same query "@ephem" I get: CLI: https://imgur.com/a/VZMUCOa (good, relevant results) Desktop: https://imgur.com/a/QLSo4Ms (bad, irrelevant) Opened issue for this and it was automatically closed: https://github.com/anthropics/claude-code/issues/71421 I could have written the issue better (using CLI as comparison instead of VS Code). But, no doubt in my mind Claude could fix this itself in a minute.
What I see is that I have to read a bunch of stuff and go through a bunch of hassle to save money when the root of it is that if I tell an AI to do work on a task while I'm busy with something else and come back later I've doubled my cost because the cache expires too quickly?
Can anyone explain why the prefix cache is tied to effort? I frequently run Fable at xhigh effort to run statistical modeling way above my undergraduate understanding. Claude Fable produces Masters-degree level output, and then I spend lots of round trips asking it to explain different parts to me. The first part absolutely uses the extra effort, but the interrogation exercise is something a much simpler model, or the same model with much less effort, could answer.
This is all good to know, but funny how we are suddenly back to formal languages and commands. Aren't these things intelligent enough to figure these things out for us?