#jupyter_notebook
DINOv2 is a powerful AI model from Meta AI that learns to understand images without needing labeled data, using self-supervised learning. It was trained on 142 million images and creates strong visual features that work well for many tasks like image classification, depth estimation, and segmentation without extra fine-tuning. You can use its pretrained models easily with simple classifiers, saving time and effort. DINOv2 is efficient, scalable, and performs better than many other models, making it great for building versatile computer vision applications quickly and accurately. It’s open-source and ready to use with PyTorch.
https://github.com/facebookresearch/dinov2
#java
The Model Context Protocol (MCP) Java SDK helps developers connect AI models with tools and data sources using a standardized interface. It supports both synchronous and asynchronous communication, making it flexible for different applications. The SDK includes features like tool management, logging, and multiple transport options, which simplify interactions between AI systems and external tools. This benefits users by providing a consistent way to integrate AI with various data sources, reducing the complexity of managing multiple connectors for different tools.
https://github.com/modelcontextprotocol/java-sdk
#java
BookLore is a self-hosted web app that helps you organize, manage, and read your personal book collection easily. You can sort books into libraries and shelves, automatically get book details from sources like Goodreads, and track your reading progress on PDFs and eBooks with a built-in reader. It supports multiple users with separate accounts and secure login options, so everyone can manage their own books without mixing collections. You can upload many books at once, share books by email (great for Kindle users), and browse books via compatible reading apps. This gives you full control over your digital library with a clean, modern interface and continuous updates[1][2][5].
https://github.com/adityachandelgit/BookLore
JSpecify — стандартизация Java-аннотаций для статического анализа кода и взаимодействия между языками JVM.
Если вы знакомы с Java или изучали исходный код, то одним из решений проблемы null является использование аннотаций nullability. Однако реализаций таких аннотаций много: JetBrains, Android Jetpack, Spring, Uber и другие создали свои версии.
Решений очень много, и возникла проблема выбора и поддержки. Хотелось бы иметь стандарт в Java, но договориться не удалось.
Консорциум компаний и команд из Google, JetBrains, Meta, Kotlin, Android, Spring, PMD, Sonar, EISOP и других объединился и создал единый стандарт, который обязуются поддерживать в своих решениях.
JSpecify 1.0 сосредоточен на nullability и содержит четыре аннотации: @NonNull, @Nullable, @NullMarked, @NullUnmarked.
Интеграция уже началась в библиотеки Jetpack Android и Kotlin.
#java
Java Backend
1 - dars. Kirish
- JVM, JRE, JDK
- Java qanday ishlaydi?
- O‘zgaruvchilar
- Maʼlumot turlari
- Kommentariyalar
- Chiqarish
Mentor : Hasan Po‘latov
#java
👉@ummat_uchun_dasturlash