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@viktoriiagolovtseva shared a post, 1 month 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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🔐 RELIANOID & NIST Cybersecurity Framework Alignment

At RELIANOID, security is built into both our Load Balancer and our internal operations. We align our product and organizational practices with the NIST Cybersecurity Framework (CSF) across its five core functions: Identify, Protect, Detect, Respond, and Recover. ✔️ Consistent security controls acro..

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Goodbye Microservices

Twilio Segment collapsed 140+ destination-specific microservices into asingle monolith, one repo, one set of dependencies, one test harness. They leveled out version sprawl and builtTraffic Recorder, a homegrown yakbak-based HTTP playback tool. That killed off hours-long test runs, dropping them to.. read more  

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Why I Didn’t Sign the Resonant Computing Manifesto: The Foundations Need Work

A sharp critique of theResonant Computing Manifestopushes it past vague ideals. It calls for real governance scaffolding, not just poetic prose. Without that? The manifesto risks becoming just another glossy PDF for entrenched players to wave around while changing nothing. Under the hood:What’s real.. read more  

Why I Didn’t Sign the Resonant Computing Manifesto: The Foundations Need Work
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.