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@viktoriiagolovtseva shared a post, 1 month, 1 week ago

How to Make Your Jira Sprint Planning Really Agile

You know the drill:build a product roadmap in Jira, create your product backlog, review it, update the user stories, come up with a sprint goal before the meeting, and finally, review every story to decide which ones need to be completed this sprint. Easier said than done, right? Well-planned sprint..

Zrzut ekranu 2026-04-29 152303
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@laura_garcia shared a post, 1 month, 1 week ago
Software Developer, RELIANOID

Not all 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝗰𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀 are created equal

🚨 Not all 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝗰𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀 are created equal. From 𝗗𝗩, 𝗢𝗩, 𝗘𝗩 𝘁𝗼 𝗺𝗧𝗟𝗦 𝗰𝗹𝗶𝗲𝗻𝘁 𝗰𝗲𝗿𝘁𝘀 and 𝗰𝗼𝗱𝗲 𝘀𝗶𝗴𝗻𝗶𝗻𝗴, each plays a different role in your security posture. 🔐 Encryption is just the beginning: → Identity validation → Trust chains (Root → Intermediate → Leaf) → Secure software delivery → Zero Trust..

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@sanjayjoshi shared a post, 1 month, 1 week ago

8+ Shadcn Combobox Components for React & Nextjs Projects

Shadcn combobox components are more than just UI elements they’re productivity boosters. They simplify complex selections, enhance usability, and make your applications feel fast and modern.

Shadcn Combobox Cover pic OG image
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@rafidbottler created an organization WPWeb Infotech , 1 month, 1 week ago.
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@cloudsignals shared a post, 1 month, 1 week ago
Director - Cloud Engineering, osttra

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

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 month, 1 week 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 month, 1 week 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 month, 1 week ago

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

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.

ChatGPT Image Apr 27, 2026, 02_56_29 PM
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@sancharini shared a post, 1 month, 1 week 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.

Software Development Tools in 2026
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@koukibadr shared a link, 1 month, 1 week ago
Mobile Developer, Nventive

Code Templating

Vertex AI is Google Cloud’s end-to-end machine learning and generative AI platform, designed to help teams build, deploy, and operate AI systems reliably at scale. It unifies data preparation, model training, evaluation, deployment, and monitoring into a single managed environment, reducing operational complexity while supporting advanced AI workloads.

Vertex AI supports both custom models and foundation models, including Google’s Gemini model family. It enables organizations to fine-tune models, run large-scale inference, orchestrate agentic workflows, and integrate AI into production systems with strong security, governance, and observability controls.

The platform includes tools for AutoML, custom training with TensorFlow and PyTorch, managed pipelines, feature stores, vector search, and online and batch prediction. For generative AI use cases, Vertex AI provides APIs for text, image, code, multimodal generation, embeddings, and agent-based systems, including support for Model Context Protocol (MCP) integrations.

Built for enterprise environments, Vertex AI integrates deeply with Google Cloud services such as BigQuery, Cloud Storage, IAM, and VPC, enabling secure data access and compliance. It is widely used across industries like finance, healthcare, retail, and science for applications ranging from recommendation systems and forecasting to autonomous research agents and AI-powered products.