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@faun shared a link, 1 year, 2 months ago

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The rise and fall of the vector database infrastructure category

The explosion of embedding-based applications created a need for specialized infrastructure for vector operations, giving rise to the vector database category, with companies like Pinecone at the forefront. However, the industry has evolved towards a convergence where traditional search engines are .. read more

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@faun shared a link, 1 year, 2 months ago

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Challenges in Large-Scale DevOps Migration

Migrating40K reposfromGitLabtoGitHub? Picture a herd of cats. The GHEC tool choked. Suddenly, scripts became the unsung heroes, zapping100 MB+ filesand evicting unwanted tokens. Think retro hacks with a modern twist... read more

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@faun shared a link, 1 year, 2 months ago

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Stop Building Internal Tools Nobody Wants: A Platform Engineer’s Guide

Forge tools that tackle everyday quirks, yet welcome secret handshakes like function pointers or hooks for the rule-breakers among us. Think custom libraries, but on steroids. Embracesemantic versioningwith zeal. Minimize surprise explosions—AKA breaking changes. Flaunt yourrelease noteslike casual .. read more

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@faun shared a link, 1 year, 2 months ago

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Software Engineer Roadmap 2025: The Complete Guide

Zero in onAI tools,cloud services, andsystem design. Want a software engineering career that stands out? Ditch the fluff, snag the free roadmap!.. read more

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@faun shared a link, 1 year, 2 months ago

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Effectively implementing resource control policies in a multi-account environment

Resource control policies (RCPs)allow you to wrangle AWS access with ease, setting organization-wide guardrails that trim down policy chaos. They work wonders for broad restrictions, but for the nitty-gritty? Stick with resource and identity-based policies... read more

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Convert Linux to Windows

Winemight just be Linux's secret weapon. It untangles the compatibility mess by coaxing Windows apps to dance on Linux soil. Think of it as a friendly bridge guiding Windows users over to the wild side... read more

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Revenge of the junior developer

Vibe coding—a cheeky term from Dr. Andrej Karpathy—letsLLMstackle the drudgery, propelling coding's future asmanual codingfades into history. By 2025,coding agentsare poised to outshine chat-based tools, urging developers to swap their keyboards for AI irony hats. Efficiency gears will shift as thes.. read more

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Hacker Laws

Laws, Theories, Principles and Patterns that developers will find useful... read more

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@laura_garcia shared a post, 1 year, 2 months ago

Software Developer, RELIANOID

🔒 World Backup Day: Protect Your Digital Assets with Smart Backup Strategies 🔒

Today, onWorld Backup Day, we’re reminded of a simple but critical truth:data loss is not a matter of "if," but "when."Whether it's due to hardware failures, cyberattacks, or accidental deletions, losing critical data can be devastating. That’s why having arobust backup and recovery strategyis essen..

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@faun shared a link, 1 year, 2 months ago

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Google Cloud Announces Kubernetes History Inspector to Visualise Cluster Logs

Google Cloud introduces the Kubernetes History Inspector (KHI), a tool crafted to chronicle cluster logs in an orderly visual sequence, simplifying Kubernetes troubleshooting tasks. It utilizes Cloud Logging to fetch state details, displaying the information in a visual timeline. This lets users mon.. 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.