Packages in Meteor

Talking about packages in regard of Meteor is different from other languages. Meteor uses five basic types of packages.

  • Meteor platform packages : The Meteor core itself is split into different Meteor packages. They are included in every Meteor App and you do not need to explicitly add these.
  • Regular Meteor packages : These are also known as isopacks or isomorphic packages and they work both on client and server. First-party packages such as accounts-ui or appcache are developed by Meteor core team comes bundled with Meteor.
  • Third-party packages : These are the isopacks developed by other users and uploaded on Meteor’s package server. You can browse them on Atmosphere or with meteor search command.
  • Local packages : These are the custom packages you create yourself and place them in /packages directory.
  • NPM packages (Node.js Packaged Modules) : These are Node.js packages. They do not work different with Meteor but these can be used by previous types of packages.

Posted

in

, ,

by

Recent Post

  • 12 Essential SaaS Metrics to Track Business Growth

    In the dynamic landscape of Software as a Service (SaaS), the ability to leverage data effectively is paramount for long-term success. As SaaS businesses grow, tracking the right SaaS metrics becomes essential for understanding performance, optimizing strategies, and fostering sustainable growth. This comprehensive guide explores 12 essential SaaS metrics that every SaaS business should track […]

  • Bagging vs Boosting: Understanding the Key Differences in Ensemble Learning

    In modern machine learning, achieving accurate predictions is critical for various applications. Two powerful ensemble learning techniques that help enhance model performance are Bagging and Boosting. These methods aim to combine multiple weak learners to build a stronger, more accurate model. However, they differ significantly in their approaches. In this comprehensive guide, we will dive […]

  • What Is Synthetic Data? Benefits, Techniques & Applications in AI & ML

    In today’s data-driven era, information is the cornerstone of technological advancement and business innovation. However, real-world data often presents challenges—such as scarcity, sensitivity, and high costs—especially when it comes to specific or restricted datasets. Synthetic data offers a transformative solution, providing businesses and researchers with a way to generate realistic and usable data without the […]

  • Federated vs Centralized Learning: The Battle for Privacy, Efficiency, and Scalability in AI

    The ever-expanding field of Artificial Intelligence (AI) and Machine Learning (ML) relies heavily on data to train models. Traditionally, this data is centralized, aggregated, and processed in one location. However, with the emergence of privacy concerns, the need for decentralized systems has grown significantly. This is where Federated Learning (FL) steps in as a compelling […]

  • Federated Learning’s Growing Role in Natural Language Processing (NLP)

    Federated learning is gaining traction in one of the most exciting areas: Natural Language Processing (NLP). Predictive text models on your phone and virtual assistants like Google Assistant and Siri constantly learn from how you interact with them. Traditionally, your interactions (i.e., your text messages or voice commands) would need to be sent back to […]

  • What is Knowledge Distillation? Simplifying Complex Models for Faster Inference

    As AI models grow increasingly complex, deploying them in real-time applications becomes challenging due to their computational demands. Knowledge Distillation (KD) offers a solution by transferring knowledge from a large, complex model (the “teacher”) to a smaller, more efficient model (the “student”). This technique allows for significant reductions in model size and computational load without […]

Click to Copy