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@laura_garcia shared a post, 1 week, 4 days ago

Software Developer, RELIANOID

𝗕𝗲𝘁𝘁 𝗕𝗿𝗮𝘀𝗶𝗹 𝟮𝟬𝟮𝟲

📍 São Paulo, Brazil 📅 May 5–8, 2026 𝗥𝗘𝗟𝗜𝗔𝗡𝗢𝗜𝗗 is heading to 𝗕𝗲𝘁𝘁 𝗕𝗿𝗮𝘀𝗶𝗹 𝟮𝟬𝟮𝟲 — 𝘵𝘩𝘦 𝘭𝘢𝘳𝘨𝘦𝘴𝘵 𝘌𝘥𝘛𝘦𝘤𝘩 𝘦𝘷𝘦𝘯𝘵 𝘪𝘯 𝘓𝘢𝘵𝘪𝘯 𝘈𝘮𝘦𝘳𝘪𝘤𝘢. 🚀 46,000+ professionals 💡 270+ companies 🌍 One shared goal: transforming education Let’s talk about secure, scalable, and high-performance digital learning. 👉 See you at Expo Cen..

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@koukibadr shared a link, 1 week, 4 days ago

Mobile Developer, Nventive

LiveData vs StateFlow

LiveData and StateFlow both stream data reactively, but differ in two key ways:

Initialization — LiveData needs no initial value; StateFlow requires one.

Lifecycle — LiveData is lifecycle-aware by default; StateFlow is not, so you need to wrap it in repeatOnLifecycle to avoid memory leaks.

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@pramod_kumar_0820 shared a post, 1 week, 4 days ago

Software Engineer, Teknospire

How To Crack Senior Java Interviews (6–10 YOE) In 4 Weeks

#system-... #Intervi... #spring ... #java #Microse...

A practical 4-week roadmap to crack Senior Java Developer interviews (6–10 YOE), covering Core Java, Spring Boot internals, Microservices, System Design, and real-world interview strategies.

Senior Java Interviews (6–10 YOE) In 4 Weeks

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@smh started using tool TypeScript , 1 week, 4 days ago.

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@smh started using tool New Relic , 1 week, 4 days ago.

AIStor is an enterprise-grade, high-performance object storage platform built for modern data workloads such as AI, machine learning, analytics, and large-scale data lakes. It is designed to handle massive datasets with predictable performance, operational simplicity, and hyperscale efficiency, while remaining fully compatible with the Amazon S3 API. AIStor is offered under a commercial license as a subscription-based product.

At its core, AIStor is a software-defined, distributed object store that runs on commodity hardware or in containerized environments like Kubernetes. Rather than being limited to traditional file or block interfaces, it exposes object storage semantics that scale from petabytes to exabytes within a single namespace, enabling consistent, flat addressing of vast datasets. It is engineered to sustain very high throughput and concurrency, with examples of multi-TiB/s read performance on optimized clusters.

AIStor is optimized specifically for AI and data-intensive workloads, where throughput, low latency, and horizontal scalability are critical. It integrates broadly with modern AI and analytics tools, including frameworks such as TensorFlow, PyTorch, Spark, and Iceberg-style table engines, making it suitable as the foundational storage layer for pipelines that demand both performance and consistency.

Security and enterprise readiness are central to AIStor’s design. It includes capabilities like encryption, replication, erasure coding, identity and access controls, immutability, lifecycle management, and operational observability, which are important for mission-critical deployments that must meet compliance and data protection requirements.

AIStor is positioned as a platform that unifies diverse data workloads — from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets — within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.