An agent harness that compiles a model into one provably-correct, self-retargeting CUDA megakernel and self-tunes it past cuBLAS at batch-1 LLM decode, paper: https://arxiv.org/abs/2606.09682
First read
RightNow-AI/AutoMegaKernel is active enough to inspect further, but the surrounding proof is thinner. Use the source repository and nearby comparisons to decide whether this is rising substance or just short-term visibility.
157 public stars in the current GitStar snapshot.
Needs a narrower context read
Last commit Sep 18, 2026.
Active enough
Treat stars as discovery context until a linked package appears.
No linked package mapping
One or more key signals are partial, so GitStar keeps the interpretation conservative.
Partial snapshot
Snapshot facts
Compare lens
pytorch/pytorch and ray-project/ray are the closest comparison targets GitStar found. A side-by-side comparison usually tells you more than a single raw rank.
Signal trail
Read the recent motion first. This block is for deciding whether the repository still looks alive, compounding, or flattening before you trust stars alone.
Package reality
No linked npm or PyPI package is mapped for this repository yet, so the page leans more heavily on GitHub-visible popularity and should be read more conservatively.
GitStar expects a package signal here, but no npm or PyPI package is linked to this repository yet.
Validation note
GitStar can summarize public signals for RightNow-AI/AutoMegaKernel, but the GitHub repository is still the primary place to confirm release cadence, issue activity, and maintainer intent.
GitStar surfaces public popularity and package signals. These rankings are not endorsements, security reviews, or investment advice.
Why this rank
This repository stands out because it combines fresh update.
Reconstructed from current stars and cached daily/weekly/monthly deltas.
Bar lengths use a log scale so stars and package downloads stay comparable.
GitStar can see repository momentum, but it does not have a reliable linked package signal yet.
Treat stars and recent movement as discovery context only until npm or PyPI usage is available.
Shares the learning category footprint with RightNow-AI/AutoMegaKernel, so the comparison is closer to a same-problem decision than a same-language coincidence.
Shares the learning category footprint with RightNow-AI/AutoMegaKernel, so the comparison is closer to a same-problem decision than a same-language coincidence.
Shares the learning category footprint with RightNow-AI/AutoMegaKernel, so the comparison is closer to a same-problem decision than a same-language coincidence.
Shares the learning category footprint with RightNow-AI/AutoMegaKernel, so the comparison is closer to a same-problem decision than a same-language coincidence.
GitStar picked pytorch + ray as the closest next comparison from the related repository set.
[](https://gitstar.space/repo/RightNow-AI/AutoMegaKernel)<a href="https://gitstar.space/repo/RightNow-AI/AutoMegaKernel"><img src="https://gitstar.space/api/badge/RightNow-AI/AutoMegaKernel" alt="GitStar"></a>Tensors and Dynamic neural networks in Python with strong GPU acceleration
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Machine Learning Systems: Foundations, Scaling, Agentic AI, and Physical AI (Vols I–IV) • Harvard CS249r | https://mlsysbook.ai
A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
This page provides a quick overview of RightNow-AI/AutoMegaKernel based on GitStar's cached data. The signal chart reconstructs approximate checkpoints from current stars plus cached daily, weekly, and monthly star deltas, so it is best read as directional context rather than as a precise historical audit log.
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