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

Software Developer, RELIANOID

CloudFest 2026 is calling

🚀 CloudFest 2026 is calling March 23–26 | Europa-Park 10,000+ minds. 80+ countries. One place where the future of the internet is built. From cutting-edge cloud innovation to legendary networking — this isn’t just an event, it’s the experience. 👉 Meet us there and discover how RELIANOID is powering ..

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@indonetgroup started using tool Juju , 1 week, 4 days ago.

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@indonetgroup started using tool Business Catalyst , 1 week, 4 days ago.

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@sanjayjoshi added a new tool Shadcn Space , 1 week, 4 days ago.

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@sanjayjoshi created an organization WrapPixel , 1 week, 4 days ago.

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@sanjayjoshi started using tool tailwindcss , 1 week, 4 days ago.

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@sanjayjoshi started using tool React , 1 week, 4 days ago.

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@sanjayjoshi started using tool Next.js , 1 week, 4 days ago.

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@himanshu shared a post, 1 week, 5 days ago

Software Testing Strategies: A Practical Guide for Modern Development

Software quality is a critical factor in modern application development. As development teams adopt Agile, DevOps, and CI/CD pipelines, testing must also evolve to ensure software remains reliable and secure. A well-defined testing plan helps teams identify bugs early, reduce risks, and deliver bett..

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@pramod_kumar_0820 shared a link, 1 week, 5 days ago

Software Engineer, Teknospire

Why Most Spring Boot Apps Fail in Production (7 Critical Mistakes)

Most Spring Boot applications run perfectly in development.

The APIs respond quickly, tests pass, and everything seems stable.

But once the application reaches production, things can change dramatically — slow responses, memory issues, and unexpected failures start appearing.

In many cases, the problem isn't Spring Boot itself.
It's a set of common mistakes developers unknowingly introduce into their applications.

In this article, we'll explore 7 critical mistakes that cause many Spring Boot apps to fail in production — and how to avoid them.

GPT-5.3-Codex is OpenAI’s advanced agentic coding model, designed to go beyond writing code and operate as a general-purpose collaborator on a computer. It builds on GPT-5.2-Codex by combining stronger coding performance with improved reasoning and professional knowledge, while running about 25% faster. The model is optimized for long-running tasks that involve research, tool use, and complex execution, and it performs at the top of industry benchmarks such as SWE-Bench Pro and Terminal-Bench.

Unlike earlier Codex models that focused primarily on code generation and review, GPT-5.3-Codex can reason, plan, and act across the full software lifecycle. It supports activities such as debugging, deploying, monitoring, writing product requirement documents, creating tests, and analyzing metrics. It can also autonomously build and iterate on complex applications and better interpret underspecified prompts, producing more complete and production-ready results by default.

A defining feature of GPT-5.3-Codex is its interactive, agentic workflow. Users can steer the model while it is working, receive progress updates, and adjust direction without losing context, making it feel more like a teammate than a batch automation tool. The model was even used internally to help debug its own training and deployment processes. GPT-5.3-Codex is available through paid ChatGPT plans in the Codex app, CLI, IDE extension, and web, with API access planned for the future.