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@laura_garcia shared a post, 2 months, 3 weeks 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, 2 months, 3 weeks 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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@laura_garcia shared a post, 2 months, 3 weeks ago
Software Developer, RELIANOID

🔐 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..

NIST Cybersecurity Framework RELIANOID compliance
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@varbear shared a link, 2 months, 3 weeks ago
FAUN.dev()

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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@varbear shared a link, 2 months, 3 weeks ago
FAUN.dev()

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
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@varbear shared a link, 2 months, 3 weeks ago
FAUN.dev()

Rust unit testing: file writing

To test file writes without hitting the disk, the author swaps in a closure that takes a file handle. That handle’s a test double, so after the code runs, you can crack it open and inspect what got written... read more  

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@varbear shared a link, 2 months, 3 weeks ago
FAUN.dev()

Full Unicode Search at 50× ICU Speed with AVX‑512

StringZilla v4.5drops a major speed bomb on Unicode text processing. Think10× faster tokenization and case folding. Up to150× faster for case-insensitive substring search. It leaves ICU and PCRE2 wheezing in the dust. Under the hood: SIMD all the way, AVX-512 on newer chips, plus script-aware SIMD k.. read more  

Full Unicode Search at 50× ICU Speed with AVX‑512
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@varbear shared a link, 2 months, 3 weeks ago
FAUN.dev()

pqr.sql: Generate QR Codes with Pure SQL in PostgreSQL

A developer jammed out aQR code generator in pure SQL, just PostgreSQL, no extensions or libraries. One gnarly single-statement query. It even runs faster onPostgreSQL 17than on 16, thanks to engine tweaks... read more  

pqr.sql: Generate QR Codes with Pure SQL in PostgreSQL
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@varbear shared a link, 2 months, 3 weeks ago
FAUN.dev()

5 engineering dogmas it's time to retire

Dependencies are risky, especially in smaller companies - avoid unnecessary packages to prevent security incidents and maintain code simplicity. Feature flags can become overwhelming if abused, leading to complex codebases and false sense of security - use them wisely. Commenting code is a balance -.. read more  

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@kaptain shared a link, 2 months, 3 weeks ago
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Dapr Deployment Models

Daprstarted as a humble Kubernetes sidecar. Now? It's a full-blownmulti-mode runtimethat runs wherever you need it,edge,VM, orserverless APIs. Diagrid’sCatalysttakes that further. It wraps Dapr in a fully managed API layer that’s detached from your app’s lifecycle. No infra lock-in, just token-based.. read more  

Dapr Deployment Models
GPT-5.4 is OpenAI’s latest frontier AI model designed to perform complex professional and technical work more reliably. It combines advances in reasoning, coding, tool use, and long-context understanding into a single system capable of handling multi-step workflows across software environments. The model builds on earlier GPT-5 releases while integrating the strong coding capabilities previously introduced with GPT-5.3-Codex.

One of the defining features of GPT-5.4 is its ability to operate as part of agent-style workflows. The model can interact with tools, APIs, and external systems to complete tasks that extend beyond simple text generation. It also introduces native computer-use capabilities, allowing AI agents to operate applications using keyboard and mouse commands, screenshots, and browser automation frameworks such as Playwright.

GPT-5.4 supports context windows of up to one million tokens, enabling it to process and reason over very large documents, long conversations, or complex project contexts. This makes it suitable for tasks such as analyzing codebases, generating technical documentation, working with large spreadsheets, or coordinating long-running workflows. The model also introduces a feature called tool search, which allows it to dynamically retrieve tool definitions only when needed. This reduces token usage and makes it more efficient to work with large ecosystems of tools, including environments with dozens of APIs or MCP servers.

In addition to improved reasoning and automation capabilities, GPT-5.4 focuses on real-world productivity tasks. It performs better at generating and editing spreadsheets, presentations, and documents, and it is designed to maintain stronger context across longer reasoning processes. The model also improves factual accuracy and reduces hallucinations compared with previous versions.

GPT-5.4 is available across OpenAI’s ecosystem, including ChatGPT, the OpenAI API, and Codex. A higher-performance variant, GPT-5.4 Pro, is also available for users and developers who require maximum performance for complex tasks such as advanced research, large-scale automation, and demanding engineering workflows. Together, these capabilities position GPT-5.4 as a model aimed not just at conversation, but at executing real work across software systems.