bonsai VS qdrant

The engine's completely bespoke; I wrote everything from the memory allocators and the font rasterizer to the renderer and level editor.

Source code is available online, and there's a discord for those interested.

Github : https://github.com/scallyw4g/bonsai

Discord : https://discord.gg/hv8NWrGU

Project Repo : https://github.com/scallyw4g/bonsai

| Bonsai | Realtime 3D Voxel Engine | Seeking Volunteers/Contributors | Remote, Global

Last month I posted a 'real' hiring ad (with an actual salary). I got such an overwhelming response from Intern level candidates that I though I'd put the word out; I'm looking for contributors for an OSS project I work on. If this is an inappropriate venue for this ad, I'm happy to take this down.

Bonsai is a long-running passion project of mine that's recently reached a level of stability with which I'm comfortable including an additional contributor or two. The project is largely inspired by Handmade Hero, and the rules are the same. With very few exceptions, every line of code included in the project has been written by me. This includes everything from the standard library to the font rasterizer, the collision detection, and even a C metaprogramming language.

https://github.com/scallyw4g/bonsai

As you can imagine, there's a great number of things that could be worked on, and I'm very open to suggestions. That said, a few tasks relatively high on the to-do list are as follows:

* Make some games!

* Implement transparency in the renderer based on this paper : https://www.jcgt.org/published/0002/02/09/paper.pdf

* Implement an MP3 decoder

* Using the metaprogramming language I wrote, implement a GUI tool to introspect the engine data at runtime.

A few more .. ;)

I'm looking for anyone who is passionate about (or passionately learning about) low-level programming, graphics and/or compilers. If you're just starting out with these topics, I'm happy to be a mentor and help you stumble blindly down the path of becoming an excellent engineer. Working on Bonsai has been an absolutely amazing experience for me; it's literally changed my entire life, and I'd love to share it with other folks. If you're looking for a cool new project to work on, and don't need income from it, let's connect.

[email protected]

Obligatory buzzwords : C, C++, OpenGL, WebGL, WASM, 3D graphics, multi-threading

Anyhow, if it sounds like what you're after you can take a look: https://github.com/scallyw4g/bonsai

Code available : https://github.com/scallyw4g/bonsai/blob/master/src/engine/voxel_synthesis.cpp

I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours.

I'm currently looking to implement locally, using QDrant [1] for instance.

I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2].

[1]. https://qdrant.tech/

Also compare with qdrant's Rust implementation; they tout their performance. https://github.com/qdrant/qdrant/tree/master/lib/segment/src...

There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb

For more information, see our release notes. Qdrant is an open source project. We welcome your contributions; raise issues, or contribute via pull requests!

Initialize the Qdrant Client with in-memory storage. The collection name will be “imagebind_data” and we will be using cosine distance.

Qdrant is an open-source vector search engine optimized for performance and flexibility. It supports both exact and approximate nearest neighbor search, providing a balance between accuracy and speed for various AI and ML applications.

Qdrant serves as a vector database, optimized for handling high-dimensional data typically found in AI and ML applications. It's designed for efficient storage and retrieval of vectors, making it an ideal solution for managing the data produced and consumed by AI models like Mistral 7B. In our setup, Qdrant handles the storage of vectors generated by the language model, facilitating quick and accurate retrievals.