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@laura_garcia shared a post, 3 weeks, 5 days ago
Software Developer, RELIANOID

🔐 CISO Sydney 2026

📍 Sydney, Australia | 🗓 10–11 February 2026 CISO Sydney returns for its 5th edition, bringing together New South Wales’ most senior Information Security leaders to explore how cybersecurity can truly enable business growth. From AI-driven threats and shared risk responsibility to culture-first secur..

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@nelly96 added a new tool GPTHuman , 3 weeks, 5 days ago.
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@laura_garcia shared a post, 3 weeks, 6 days ago
Software Developer, RELIANOID

Want to deploy RELIANOID Load Balancer Enterprise Edition v8 on AWS using Terraform in a clean, automated way?

We’ve got you covered. In this step-by-step guide, you’ll learn how to: Use the official Terraform module from the Terraform Registry Automatically provision VPC, subnet, security groups, and EC2 Deploy the RELIANOID Enterprise Edition AMI Access the VM via SSH and Web GUI Easily destroy all resourc..

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@sancharini shared a post, 3 weeks, 6 days ago

Interpreting Software Testing Metrics Beyond Dashboards

Learn how to interpret software testing metrics beyond dashboards, turning raw data into actionable insights that improve release decisions and reduce risk.

Interpreting Software Testing Metrics Beyond Dashboards
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@idjuric660 shared a post, 1 month ago
Technical Content Writer, Mailtrap

5 Best Email API for Python Developers Tested & Compared

The best email APIs for Python developers are Mailtrap, Mailgun, SendGrid, Amazon SES, and Postmark. SDK quality & framework compatibility All five providers offerPythonSDKs and they’re compatible with popular frameworks. I tested each withDjango,Flask, and FastAPI to assess real-world integration. ..

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@jordanunix created an organization DevOpsDayLA , 1 month ago.
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@laura_garcia shared a post, 1 month ago
Software Developer, RELIANOID

💡 Third-Party Vendors: The Hidden Cybersecurity Risk

In today’s hyper-connected world, digital supply chains are only as secure as their weakest link. One single vendor can open the door to ransomware, outages, or worse. At RELIANOID, we take this risk seriously. 🔒 That’s why we apply: ✅ Continuous vendor risk assessments ✅ Real-time monitoring of thi..

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@varbear shared a link, 1 month ago
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Software engineering when machine writes the code

In 1968, computer scientists identified the "software crisis" - the existing methods of programming were struggling to handle the power of computers. Today, AI coding assistants are accelerating productivity, but concerns arise about understanding the code they generate, the implications for debuggi.. read more  

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@varbear shared a link, 1 month ago
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Unconventional PostgreSQL Optimizations

PostgreSQL 18 now supportsvirtual generated columns, indexable expressions without burning storage. Perfect for standardizing queries in analytics-heavy pipelines. Pair that withplanner constraint exclusion(constraint_exclusion=on), and Postgres can dodge irrelevant table scans based on constraints... read more  

Unconventional PostgreSQL Optimizations
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@varbear shared a link, 1 month ago
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How I Taught GitHub Copilot Code Review to Think Like a Maintainer

Vibe coding has made contributing to open source easier, but the high number of contributions to the AI agent framework goose has posed a challenge. An AI Code Review agent like Copilot can help review PRs, but tuning its feedback is crucial for reducing noise and increasing value. By providing clea.. read more  

Gemini 3 is Google’s third-generation large language model family, designed to power advanced reasoning, multimodal understanding, and long-running agent workflows across consumer and enterprise products. It represents a major step forward in factual reliability, long-context comprehension, and tool-driven autonomy.

At its core, Gemini 3 emphasizes low hallucination rates, deep synthesis across large information spaces, and multi-step reasoning. Models in the Gemini 3 family are trained with scaled reinforcement learning for search and planning, enabling them to autonomously formulate queries, evaluate results, identify gaps, and iterate toward higher-quality outputs.

Gemini 3 powers advanced agents such as Gemini Deep Research, where it excels at producing well-structured, citation-rich reports by combining web data, uploaded documents, and proprietary sources. The model supports very large context windows, multimodal inputs (text, images, documents), and structured outputs like JSON, making it suitable for research, finance, science, and enterprise knowledge work.

Gemini 3 is available through Google’s AI platforms and APIs, including the Interactions API, and is being integrated across products such as Google Search, NotebookLM, Google Finance, and the Gemini app. It is positioned as Google’s most factual and research-capable model generation to date.