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@cloudsignals shared a post, 1 week, 6 days ago

Director - Cloud Engineering, osttra

From Hunters to Algorithms: How AI Is Rewriting the Rules of Vulnerability Discovery

#AI vuln... #AI bug ... #AI cybe... #DevSecO... #automat...

Security has entered its algorithmic era. AI is rapidly transforming vulnerability discovery by scanning code at scale, uncovering hidden patterns, and accelerating detection beyond human limits. For maintainers, this means shifting from reactive patching to intelligent triage and secure-by-design systems. For bug hunters, success now lies in combining AI speed with human creativity to uncover deeper, context-driven flaws. The future of security isn’t human vs machine—it’s human amplified by machine.

AI-driven vulnerability discovery concept showing a split human and artificial intelligence face analyzing cybersecurity threats, with dashboards displaying SQL injection detection, risk score, and automated code analysis in a futuristic interface.

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@koukibadr shared a post, 1 week, 6 days ago

Mobile Developer, Nventive

Optimizing Performance in Android Apps with Kotlin

Optimizing the performance of your Android applications is crucial for providing a smooth and responsive user experience. Kotlin, with its concise syntax and powerful features, offers several ways to enhance the performance of your apps. In this article, we'll explore various techniques and best pra..

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@laura_garcia shared a post, 1 week, 6 days ago

Software Developer, RELIANOID

𝗖𝗵𝗶𝗹𝗲 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲𝘀 𝗶𝘁𝘀 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗹𝗲𝗮𝗽

𝗖𝗵𝗶𝗹𝗲 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲𝘀 𝗶𝘁𝘀 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗹𝗲𝗮𝗽🚀 More than 𝟳𝟬% 𝙤𝙛 𝘾𝙝𝙞𝙡𝙚𝙖𝙣 𝙤𝙧𝙜𝙖𝙣𝙞𝙯𝙖𝙩𝙞𝙤𝙣𝙨 are actively driving projects around 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲, 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗰𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗻𝗲𝘅𝘁-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝘃𝗶𝘁𝘆. This rapid transformation—supported by strong public policies, 5G deployment, and long-..

Blog_Chile’s Technological Acceleration_RELIANOID

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@alok00k shared a post, 1 week, 6 days ago

Integration Testing: The Bridge Between Unit Tests and Real-World Software Reliability

#Testing #integra... #sdlc #keploy #automat...

Integration testing verifies that different parts of an application—such as APIs, databases, and services—work correctly together. It helps catch real-world issues that unit tests miss, like broken data flow or failed service communication. Essential for modern apps, especially microservices, it improves reliability, reduces production bugs, and should be automated in CI/CD pipelines.

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@sancharini shared a post, 1 week, 6 days ago

How Software Development Tools Influence Code Quality Over Time?

Learn how software development tools shape code quality over time by enforcing standards, automating testing, and improving developer workflows. Discover key factors that impact long-term software reliability.

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.