kannthu

A few months ago, Cursor released a technical blog post describing how they implemented the instant apply feature. The post went deep into how and why it works so well. They even did a part 2 in collaboration with an LLM inference provider - https://fireworks.ai/blog/cursor.

However, when I checked today, the blog post had been deleted. If you go to the https://www.cursor.com/blog/instant-apply, you will get 404.

Are they scared of the competition? The PearAI started getting some traction recently.

BTW the blog post is still accessible on the Wayback[1] + it seems like somebody already implemented the instant apply and published it on Github[2].

[1] https://web.archive.org/web/20240823050616/https://www.cursor.com/blog/instant-apply

[2] https://github.com/llllvvuu/instant_apply

*TL;DR;*

Over one month ago I posted about a really hard problem that I "accidentally" solved (https://news.ycombinator.com/item?id=40460084).

The problem is to resolve cross-file references for multiple programming languages. I can generate a graph representation of the codebase.

*Why do you need to have a graph representation of the codebase?*

- To understand how code references other code

- Track how data is passed around

I generated references for repo https://github.com/dj-stripe/dj-stripe, here is a gist: https://gist.githubusercontent.com/kannthu/6e1bdd2781d2e0a6ded30844d61f089e/raw/f1fa4bc0f34891834ce13ac256eec12f6cc671e1/dj-stripe-references.json

The gist is a big JSON blob that contains definitions form the repository.

Definitions are:

- top-level functions

- classes

- methods and public properties

- top-level variables

- exports

Each definition contains:

- Snippet, path, and range within the file

- "references" - a list of places where the definition is used

- "expressions" - a list of resolved references (variables, functions, and classes) that are used within the body of the definition

*How this data can be useful?*

If you are building code generation, code intelligence, or code review products - your product needs to have an understanding of the codebase for many programming languages at once. The more accurate context you feed to LLM => the better output you will get, and doing it in-house is really expensive and resource-consuming.

Let me know if it is interesting for any of you.

How do you gain karma on Reddit when you constantly get blocked and filtered?

I am a cybersecurity expert and I have experience in writing decent technical content (like https://blog.vidocsecurity.com/blog/hacking-swagger-ui-from-xss-to-account-takeovers/ or https://www.moczadlo.com/2024/delightful-inputs-with-intellisense-and-syntax-highlighting)

It is almost impossible to translate it into Reddit karma - I tried posting content on some of the subreddits, but I simply can't cause of minimal karma requirements.

Reddit said that you need to comment before being allowed to post content on most of the subreddits, but for me, it is really hard to find interesting posts I could comment on.

It is far easier to create new engaging content, than finding ways of contributing to existing content. You need to filter through hundreds of bad posts to find a couple of interesting ones and be early enough to add value through comments.

How do you do it?