Hmm, now that we have the prompts, would it be possible to reimplement Cursor servers and have a fully local ( ahem pirated) version?
Reverse Engineering Cursor's LLM Client
21–30 of 38 posts
Re: Reverse Engineering Cursor's LLM Client
#22Hmm, now that we have the prompts, would it be possible to reimplement Cursor servers and have a fully local ( ahem pirated) version?
Re: Reverse Engineering Cursor's LLM Client
#23Hmm, now that we have the prompts, would it be possible to reimplement Cursor servers and have a fully local ( ahem pirated) version?
I wonder how hard it would be to build a local apply model/surely that would be faster on a macbook
Re: Reverse Engineering Cursor's LLM Client
#24Soooo.... wireshark is no longer available or something?
The article literally says at the end this was just the first post about looking before getting into actually changing the responses. (that being said, mitmproxy has gotten pretty good for just looking lately https://docs.mitmproxy.org/stable/concepts/modes/#local-capt... )
But I also like you landed on mitmproxy as well, after starting with tcpdump/wireshark. I recently started building a tiny streaming textual gradient based optimizer (similar to what adalflow is doing) by parsing the mitmproxy outputs in realtime. Having a turnkey solution for this sort of thing will definitely be valuable at least in the near to mid term.
Re: Reverse Engineering Cursor's LLM Client
#25Earlier quoted context omitted.
The article literally says at the end this was just the first post about looking before getting into actually changing the responses. (that being said, mitmproxy has gotten pretty good for just looking lately https://docs.mitmproxy.org/stable/concepts/modes/#local-capt... )
Yea the proxying/observability is without question the simplest part of this whole problem space. Once you get into the weeds of automating all the eval and prompt optimizing, you realize how irrelevant wireshark actually is in the feedback loop. But I also like you landed on mitmproxy as well, after starting with tcpdump/wireshark. I recently started building a tiny streaming textual gradient based optimizer (simila…
Re: Reverse Engineering Cursor's LLM Client
#26Earlier quoted context omitted.
Yea the proxying/observability is without question the simplest part of this whole problem space. Once you get into the weeds of automating all the eval and prompt optimizing, you realize how irrelevant wireshark actually is in the feedback loop. But I also like you landed on mitmproxy as well, after starting with tcpdump/wireshark. I recently started building a tiny streaming textual gradient based optimizer (simila…
if you haven't check out our repo -- it's free, fully self-hosted, production-grade, and designed for precisely this application :) https://github.com/TensorZero/tensorzero
Looking great so far though!
Re: Reverse Engineering Cursor's LLM Client
#27Emailed them multiple times over weeks about billing questions -- not a single response. These weren't like VS code questions , either -- they needed Cursor staff intervention.
No problem getting promo emails though!
The quicker their 'value' can be spread to other services the better, imo. Maybe the next group will answer emails.
Re: Reverse Engineering Cursor's LLM Client
#28Re: Reverse Engineering Cursor's LLM Client
#29Hmm, now that we have the prompts, would it be possible to reimplement Cursor servers and have a fully local ( ahem pirated) version?
Re: Reverse Engineering Cursor's LLM Client
#30Earlier quoted context omitted.
Maybe there is some optimization logic that only appends tool details that are required for the user’s query? I’m sure they are trying to slash tokens where they can, and removing potentially irrelevant tool descriptors seems like low-hanging fruit to reduce token consumption.
Yes this is one of the techniques apps can use. You vectorize the tool description and then do a lookup based on the users query to select the most relevant tools, this is called pre-computed semantic profiles. You can even hash queries themselves and cache tools that were used and then do similarity lookups by query.