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  • Agentic AI SRE built for Engineering and DevOps teams. Icon
    Agentic AI SRE built for Engineering and DevOps teams.

    No More Time Lost to Troubleshooting

    NeuBird AI's agentic AI SRE delivers autonomous incident resolution, helping team cut MTTR up to 90% and reclaim engineering hours lost to troubleshooting.
    Learn More
  • CloudZero: The Cloud Cost Optimization Platform Icon
    CloudZero: The Cloud Cost Optimization Platform

    CloudZero automates the collection, allocation, and analysis of your infrastructure and AI spend to uncover waste and improve unit economics.

    CloudZero is the leader in proactive cloud cost efficiency. We enable engineers to build cost-efficient software without slowing down innovation. CloudZero's next-generation cloud cost optimization platform automates the collection, allocation, and analysis of cloud costs to uncover savings opportunities and improve unit economics. We are the only platform that enables companies to understand 100% of their operational cloud spend and take an engineering-led approach to optimizing that spend. CloudZero is used by industry leaders worldwide, such as Coinbase, Klaviyo, Miro, Nubank, and Rapid7.
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  • 1
    DVC

    DVC

    Data Version Control | Git for Data & Models

    DVC is built to make ML models shareable and reproducible. It is designed to handle large files, data sets, machine learning models, and metrics as well as code. Version control machine learning models, data sets and intermediate files. DVC connects them with code and uses Amazon S3, Microsoft Azure Blob Storage, Google Drive, Google Cloud Storage, Aliyun OSS, SSH/SFTP, HDFS, HTTP, network-attached storage, or disc to store file contents. Version control machine learning models, data sets, and intermediate files. ...
    Downloads: 0 This Week
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  • 2
    Albedo

    Albedo

    A recommender system for discovering GitHub repos

    Albedo is an open-source recommender system aimed at helping developers discover GitHub repositories by learning from activity signals. It treats repositories and developers as a graph of interactions and applies large-scale matrix factorization to model affinities, with Apache Spark providing the distributed data processing. The project focuses on implicit feedback—stars, watches, and other engagement metrics—so it can build useful recommendations without explicit ratings.
    Downloads: 0 This Week
    Last Update:
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