skp1995

yup you can compile, we tested and made sure all the features work before posting

The state of its Windows support is unclear to me.

yeah this one is a bit weird, you can run the linux binary using WSL and that should work. We have a window flavor build but its not as heavily tested yet (we are figuring out a better testing flow for windows)

yes! we wanted to make sure that the binary works in your editors or via `-p`, no point in standing guard to how you would want to use it.

hey I am the engineer who worked on the TUI and harness and happy to answer your questions.

We worked quite heavily on the TUI, its written in ratatui and we did try to go the extra mile to make sure that there won't be any regressions from moving to an alt-screen rendering. Lots and lots of small details to manage and get correct, we also tried to get vim keybindings correct while keeping it mouse friendly.

I does support o3-mini-high already, we use it for a few flows in the agent.

What kind of SDK support are you looking for?

Hey I am the coredev on sidecar. The reason you see autogenerated PRs is cause I am using our agents to write the code for the agent lol

The big difference is the complete loop, each PR gets its own VM with the tool chains installed so the agent can run cargo check or cargo tests etc.

We do find the LLMs of today are not the best elite engineers but very very competent junior engineers. It's been a weird but eye opening workflow to use.

Hey! One of the creators of Aide here.

ngl the total expenditure was around $10k, in terms of test-time compute we ran upto 20X agents on the same problem to first understand if the bitter lesson paradigm of "scale is the answer" really holds true.

The final submission which we did ran 5X agents and the decider was based on mean average score of the rewards, per problem the cost was around $20

We are going to push this scaling paradigm a bit more, my honest gut feeling is that swe-bench as a benchmark is prime for saturation real soon

1. These problem statements are in the training data for the LLMs

2. Brute-forcing the answer the way we are doing works and we just proved it, so someone is going to take a better stab at it real soon

I do have to ask, I have worked in codebases which used lifetimes and didn't lean into Rc/Arc and vice-versa.

I used to think Arc/Rc was a shortcut to avoiding the borrow checker shenanigans, but have evolved that thinking over time.

You do mention it in your comment so wondering if you have anything to share about it

will keep that in mind going forward! The most recent ones which I have been hitting are around "higher-ranked lifetime error"

I know my way around this now, which is to literally binary search over the timeline of my edits (commenting out code and then reintroducing it) to see what causes the compiler to trip over (there might be better ways to debug this, and I am all ears)

Most of the times this error is several layers deep in my application so even tho I want to ticket it up, not being able to create a minimal repo for anyone to iterate against feels like a bit of wasted energy on all sides, do let me know if I should change this way of thinking and I can promise myself to start being more proactive.

Rust can be hard to get right because of the borrow checker. I had a similar situation happen to me where I went about refactoring the code to make borrow checker happy ... until the last bit when things stopped compiling and I realized my approach was completely wrong (in the rust world, I had a self-reference in the structs)

Having said this, the benefits of borrow checker out weight the shortcomings. I can feel myself writing better code in other languages (I tend to think about the layout and the mutability and lifetimes upfront more now)

My rust code now is very functional, which seems to work best with lifetimes.

I would love to know more about the authors pain, I do hope rustc gets better at lifetime compilation errors cause some of them can be very very gnarly.

I am missing the link to the thread, but diffusion models also give a very consistent output when prompted with `IMG_{number}` part of the reason could be the training data distribution

Thats a fair point, a significant part of our 4 person team had to skill up on the VSCode codebase to be able to meaningfully make changes to it.

I would love to know your workflow, you mention CLI tool or VSCode plugin, which one of them work for you? Whats missing from them where Aide can fill in the gap

There is pinned context on the very top where you can pin the files which you frequently use.

We will start including the open file by default in the context very soon (one of the gotchas here, is that the open file could not be related to the question you have)

nothing https://github.com/codestoryai/ide/blob/0eb311b7e4d7d63676ad... we get back an undefined PosthogClient instead of populating it

ugh.. in which case our platform detection code is not working as expected. We will look into that

just a name resemblance, no relationship otherwise

It supports all platforms, we took inspiration from the macos spotlight search widget for inline editing.

that's a good metric to aim for... creating a full local index for 600k lines is pretty expensive but there are a bunch of huristics which can take us pretty far

- looking at git commits - making use of recently accesses files - keyword search

If I set these constraints and allow for maybe around 2 LLM round trips we can get pretty far in terms of performance.

JetBrains is very interesting, what are the best performing extensions out there for it?

I do wonder what api level access do we get over there as well. For sidecar to run, we need LSP + a web/panel for the ux part (deeper editor layer like undo and redo stack access will also be cool but not totally necessary)

ohh inserting.. I tried it on couple of big repos and it was a bit of a miss to me. How large are the codebases on which you work? I want to get a sense check on where the behavior detoriates with embedding + gpt3.5 based reranker search (not sure if they are doing more now!)

how do you use the computer usage.. I do find it a very interesting API layer to play around with

yeah bad edits are especially worse when we keep building on top of it.

Our first take on solving this is with rollbacks .. which allows you to delete edits up until a point in the conversation.. so if you do notice a bad edit you can do that.

after this, there is the proactive agent ..which checks it's work again and suggests more changes which it needs to do .. you can give feedback and guide it.

With llms we do loose a bit of control but I think the editor should work to solve this

thats a great way to build a tool which solves your need.

In Aide as well, we realised that the major missing loop was the self-correction one, it needs to iteratively expand and do more

Our proactive agent is our first stab at that, and we also realised that the flow from chat -> edit needs to be very free form and the edits are a bit more high level.

I do think you will find value in Aide, do let me know if you got a chance to try it out

hmmm you are right, the ergonomics of providing context are more powerful in zed. Feedback taken, we will work on it.

We implicitly take in all the diagnostics on the files https://github.com/codestoryai/sidecar/blob/e5408782a3bfa461...

reading the code for helix-gpt over here https://github.com/leona/helix-gpt/blob/2a047347968e63ca55e2... looks like the architecture for the extension is based around getting the diagnostic event and then passing that along to the chat.

The readme also talks about how LSP services are not exposed properly yet, my takeaway is that its not complete yet..but surely doable

yeah I am also getting the sense that people want tooling which meets them in their preferred environment.

Do you use any of the AI features which go for editing multiple files or doing a lot more in the same instruction?

Its great that cursor is working for you. I do think LLMs in general are far far better on Typescript and Python compared to other languages (reflects from the training data)

What features of cursor were the most compelling to you? I know their autocomplete experience is elite but wondering if there are other features which you use often!

In my previous life at Facebook, I worked on the infra team and worked on a cluster manager similar to kubernetes, thats where I first heard the term sidecar. Something about the concept of a binary running alongside the pod powering other related things felt strong. In most parts this is the inspiration for naming the AI brain: sidecar

yeah I am learning that on neovim you can own a buffer region and instead use that for ai back and forth.. it's a very interesting space

we don't .. in your user settings, type in disable all telemetry and we won't see a thing