Cool project, but just a side thought I was having about how do people have resources and the money to make things like this and make it avl for public, I mean it's fair to say they have their own GPUs or if they are using api keys for gpt or Gemini with enterprise subsidized inference
But still coming from a frugal background I still cannot wrap my head around this
This is cool! I am more interested in how you guys generated next edit training data from repos, seems like there are lots of caveats here. Would love your insights
Again amazing work! waiting for what you guys cook next
I liked the new approach but I don't like the pseudo security framing. I don't see how an LLM rewritten query is secure than just sending my raw query, in both cases I don't feel secure at all
I have given up on any external bash configurator a long time ago, instead I write my own bash prompts these days, they lack a lot of functionalities but I am much happy with them for now, also a shameless plug:
https://martianlantern.github.io/2025/11/updating-my-bash-pr...
Do mean hard to be used because of compute required to render it or because of setting up and understanding the training pipeline and related hyperparameters?
Do you know you can achieve the exact same functionality with the vscode's inline code execution in python files and can create jupyter like code cells with '# %%'?