“Michele Riva is a great Speaker and he is very knowledgeable. I had the pleasure to meet him recently and we are collaborating on some projects! Also, check out his YouTube Channel, He is doing an amazing job!!! ”
Informazioni
Co-founder of Orama.
Also: Google GDE, Microsoft MVP, Published Author, and International Speaker.
Esperienza
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Licenze e certificazioni
Pubblicazioni
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Real-World Next.js
Packt
Vedi pubblicazioneNext.js is a scalable and high-performance React.js framework for modern web development. It provides a large set of features out of the box, such as hybrid rendering, route prefetching, automatic image optimization, and internationalization. With this book, you’ll learn how to effectively use this exciting technology for a wide range of purposes, from creating an e-commerce website or a simple website to a blog without compromising on performance or user experience.
Starting from the…Next.js is a scalable and high-performance React.js framework for modern web development. It provides a large set of features out of the box, such as hybrid rendering, route prefetching, automatic image optimization, and internationalization. With this book, you’ll learn how to effectively use this exciting technology for a wide range of purposes, from creating an e-commerce website or a simple website to a blog without compromising on performance or user experience.
Starting from the basics of Next.js, you will understand how the framework can help you reach your development goals. You’ll also explore Next.js’s versatility by building real-world applications with the help of step-by-step explanations, understanding how to write unit tests and integration tests for an app, integrating different backends with the app, and more. Later, the book shows you how to choose the right rendering methodology for your website, how to secure it, and how to deploy it to different providers.
By the end of this Next.js book, you’ll have the skills you need to be able to design, build, and deploy modern architectures using Next.js with any headless CMS or data source.
Brevetti
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Operating a distributed search index in a content delivery network
Rilasciato 11921759
Vedi brevettoA facility for distributing a search index for a corpus of documents is described. The facility accesses multiple search index segments collectively making up the search index. Each of the segments is executable to traverse an index subtree embedded in the segment to find in the index subtree a node representing a query term specified in an argument. Each of the segments corresponds to a particular indexed document field that is indexed by the search index.
For each of the segments, the…A facility for distributing a search index for a corpus of documents is described. The facility accesses multiple search index segments collectively making up the search index. Each of the segments is executable to traverse an index subtree embedded in the segment to find in the index subtree a node representing a query term specified in an argument. Each of the segments corresponds to a particular indexed document field that is indexed by the search index.
For each of the segments, the index subtree embedded the segment has nodes representing query terms that all produce a particular hash result. The facility calls a programmatic publication interface for a content delivery network to publish the plurality of search index segments on the content delivery network. -
Operating in a content delivery network a distributed search index based on an automatically-selected set of documents properties
Depositato: 63/633,411
A facility is described for automatically selecting document properties to be included in an index on a corpus of documents. In some cases, the facility performs this automatic selection by subjecting a list of the document properties for which the documents have contents to an inference model. In some cases, the facility conducts this automatic selection by submitting to a large language model a prompt automatically constructed to include, for each of one or more documents, the document's…
A facility is described for automatically selecting document properties to be included in an index on a corpus of documents. In some cases, the facility performs this automatic selection by subjecting a list of the document properties for which the documents have contents to an inference model. In some cases, the facility conducts this automatic selection by submitting to a large language model a prompt automatically constructed to include, for each of one or more documents, the document's contents for each document property, in each case together with the document property's name.
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