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@laura_garcia shared a post, 2 weeks, 5 days ago
Software Developer, RELIANOID

🔐🌱 Cybersecurity and industrial sustainability: a moment to reflect as the year comes to an end

We shared this article a few months ago, but year-end is the perfect time to revisit it and reflect on where the industry is heading in the year ahead. Cybersecurity and sustainability can no longer be treated as separate disciplines. They share a common goal: ensuring ethical, resilient, and respon..

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@eon01 shared a post, 3 weeks, 1 day ago
Founder, FAUN.dev

Enshittification is not a bug

Docker Helm Kubernetes

Bitnami charts are still high quality, but their public image distribution is going away. Instead of rewriting everything, many teams can keep the charts and switch the underlying images (for example, to Docker Hardened Images) to minimize disruption and maintain security.

Bitnami vs Docker Hardened Images
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@laura_garcia shared a post, 3 weeks, 4 days ago
Software Developer, RELIANOID

🚀 Deploy RELIANOID CE v7 on AWS with Terraform

Quickly deploy RELIANOID Community Edition v7 on AWS using the official Terraform module. ✔️ VPC, Subnet & Security Group ✔️ EC2 with RELIANOID AMI ✔️ SSH & Web GUI ready ✔️ Easy cleanup with terraform destroy ⚠️ AMI is region-specific (default: us-east-1) 🔐 Always secure your SSH private key #Terra..

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@viktoriiagolovtseva shared a post, 3 weeks, 4 days ago

How To Create a Jira Test Case Template To Boost Efficiency

Many agile teams prefer Jira for managing test cases. Even though it’s not a dedicated tool, it provides a straightforward way to organize the testing process, track progress, and share results with stakeholders. Additionally, it enhances collaboration between QA and development teams.

Using test case templates in Jira allows you to manage this process even more efficiently. These templates save time, promote standardization, and provide a structured foundation for test execution. In this short tutorial, I will show you how to create a Jira test case template and use it with automation to simplify your testing process.

Zrzut ekranu 2025-12-23 155342
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DeepSeekMath-V2 is a state-of-the-art mathematical reasoning model built on the DeepSeek-V3.2-Exp-Base architecture with 685 billion parameters. Unlike conventional math-focused language models that optimize only for correct final answers, DeepSeekMath-V2 introduces a self-verification framework where the model generates, inspects, and validates its own mathematical proofs.

This approach enables rigorous, step-by-step reasoning suitable for theorem proving, scientific research, and domains requiring high-integrity logic. The model is trained through a generation-verification loop involving a dedicated LLM-based verifier and reinforcement learning optimized for proof correctness rather than answer matching.

DeepSeekMath-V2 achieves gold-level scores on IMO 2025 and CMO 2024, along with a groundbreaking 118/120 on the Putnam 2024 contest. Released under the Apache 2.0 license and hosted on Hugging Face, it is fully open source for research and commercial use.