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@devopslinks shared an update, 7 months, 3 weeks ago
FAUN.dev()

Qovery Secures $13M Series A to Boost DevOps Automation Platform

Kubernetes

Qovery raises $13M Series A to enhance its DevOps automation platform, addressing the DevOps engineer shortage and supporting regional expansion and AI-driven development.

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@laura_garcia shared a post, 7 months, 3 weeks ago
Software Developer, RELIANOID

Japan’s new Active Cyberdefence Law

Japan’s new Active Cyberdefence Law (ACD) is redefining how the nation tackles cyber threats — shifting from a defensive stance to a proactive cybersecurity strategy. Key measures include: ⚙️ Authority to neutralize hostile servers 🤝 Closer public–private collaboration 📢 Mandatory breach reporting A..

Japan's Active Cyberdefence Law
Story
@laura_garcia shared a post, 7 months, 3 weeks ago
Software Developer, RELIANOID

Asia Hits 50% IPv6 Capability — A Global Milestone

- Asia has reached a major internet milestone: 50% of its systems are now IPv6 capable, positioning the region as a global leader in IPv6 user adoption. - Why this matters: - India (78.1%) and China (810M users) are driving this growth. - Historical IPv4 scarcity in Asia helped fuel early IPv6 inves..

Blog Asia reaches 50 percent IPv6 capability
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@laura_garcia shared a post, 7 months, 3 weeks ago
Software Developer, RELIANOID

🚀 RELIANOID is heading to it-sa Expo&Congress 2025!

📍 Nuremberg, Germany | October 7–9, 2025 🔒 Europe’s largest IT security event with 900+ exhibitors, expert talks & global networking. We’ll be there to showcase how RELIANOID helps businesses stay ahead of evolving cyber threats. 👉 See you in Nuremberg! Send us a DM to make an appointment. #itSa2025..

itsa nuremberg
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@faun shared a link, 7 months, 3 weeks ago
FAUN.dev()

Uncommon Uses of Common Python Standard Library Functions

A fresh guide gives old Python friends a second look—turns out, tools like **itertools.groupby**, **zip**, **bisect**, and **heapq** aren’t just standard; they’re slick solutions to real problems. Think run-length encoding, matrix transposes, or fast, sorted inserts without bringing in another depen.. read more  

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@faun shared a link, 7 months, 3 weeks ago
FAUN.dev()

Organize your Slack channels by “How Often”, not “What” - Aggressively Paraphrasing Me

One dev rewired their Slack setup by **engagement frequency**—not subject. Channels got sorted into tiers like “Read Now” and “Read Hourly,” cutting through noise and saving brainpower. It riffs off the **Eisenhower Matrix**, letting priorities shift with projects, not burn people out... read more  

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@faun shared a link, 7 months, 3 weeks ago
FAUN.dev()

Writing Load Balancer From Scratch In 250 Line of Code

A developer rolled out a fully working **Go load balancer** with a clean **Round Robin** setup—and hooks for dropping in smarter strategies like **Least Connection** or **IP Hash**. Backend servers live in a custom server pool. Swapping balancing logic? Just plug into the interface... read more  

Writing Load Balancer From Scratch In 250 Line of Code
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@faun shared a link, 7 months, 3 weeks ago
FAUN.dev()

Building a Resilient Data Platform with Write-Ahead Log at Netflix

Netflix faced challenges like data loss, system entropy, updates across partitions, and reliable retries. To address these, they built a generic Write-Ahead Log (WAL) system serving a variety of use cases like delayed queues, generic cross-region replication, and multi-partition mutations. WAL abstr.. read more  

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@faun shared a link, 7 months, 3 weeks ago
FAUN.dev()

Users Only Care About 20% of Your Application

Modern apps burst with features most people never touch. Users stick to their favorite 20%. The rest? Frustration, bloat, ignored edge cases. Tools like **VS Code**, **Slack**, and **Notion** nail it by staying lean at the core and letting users stack what they need. Extensions, plug-ins, integrati.. read more  

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@faun shared a link, 7 months, 3 weeks ago
FAUN.dev()

Authentication Explained: When to Use Basic, Bearer, OAuth2, JWT & SSO

Modern apps don’t just check passwords—they rely on **API tokens**, **OAuth**, and **Single Sign-On (SSO)** to know who’s knocking before they open the door... 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.