| GitModel | clrs | |
|---|---|---|
| 6 | 4 | |
| 60 | 379 | |
| - | 6.3% | |
| 6.8 | 4.7 | |
| 11 months ago | 2 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
GitModel
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[P] I built a chatbot that lets you talk to any Github repository
That's exactly what we've been exploring. Here's another approach I've seen to this problem of building a summary tree representation of a codebase: https://github.com/danielpatrickhug/GitModel/
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[D] Modern Topic Modeling/Discovery
https://github.com/danielpatrickhug/GitModel/blob/main/src/system_prompts/format_system_prompts.py the basic components of the self instruction prompting are format_system_prompts which has the base prompt and extract_questions which just uses regex to extract the questions from the output.
- GITModel: decompose Git repositories and generate coherent topic trees
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[P] GITModel: Dynamically generate high-quality hierarchical topic tree representations of GitHub repositories using customizable GNN message passing layers, chatgpt, and topic modeling.
Decompose Python libraries and generate Coherent hierarchical topic models of the repository. https://github.com/danielpatrickhug/GitModel
clrs
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[P] GITModel: Dynamically generate high-quality hierarchical topic tree representations of GitHub repositories using customizable GNN message passing layers, chatgpt, and topic modeling.
Example: https://github.com/deepmind/clrs
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[D] Is there any research into using neural networks to discover classical algorithms?
Bonus, there is this CLRS benchmark that might be useful for evaluation for your task.
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[R] A Generalist Neural Algorithmic Learner
The baseline code for the CLRS benchmark (which we use in the paper) has been open-sourced for a while now: https://github.com/deepmind/clrs
- GitHub - deepmind/clrs
What are some alternatives?
textbeat - 🎹 plaintext music sequencer and midi shell, with vim playback and the powers of music theory 🥁
zingg - Scalable identity resolution, entity resolution, data mastering and deduplication using ML
HVM - A massively parallel, optimal functional runtime in Rust
LeetCode-Solutions - A compilation of all the Leetcode solutions.
hummingbot - Open source software that helps you create and deploy high-frequency crypto trading bots
algorithms - CLRS study. Codes are written with golang.
tuninglib - A C++ Class and Template Library for Performance Critical Applications
Crack-Your-Placement - 🎯 Repo for placement-preparation, DSA, Problem-Solving, Competitive Programming, CS-Fundamentals
cp-algorithms - Algorithm and data structure articles for https://cp-algorithms.com (based on http://e-maxx.ru)
callofcode - Coding exercises for beginners. Want to contribute, check out the issues.
algo-ds-101 - algo-ds-101
LeetCode-Go - ✅ Solutions to LeetCode by Go, 100% test coverage, runtime beats 100% / LeetCode 题解