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@laura_garcia shared a post, 9 months, 1 week ago
Software Developer, RELIANOID

Understanding Botnets & How to Defend Against Them

Botnets remain one of the biggest cybersecurity threats, enabling large-scale DDoS attacks, credential theft, and malware distribution. These networks of compromised devices operate silently, controlled by cybercriminals to exploit vulnerabilities. - How do botnets work? Infect devices via phishing,..

Blog2 Botnets Network Attacks RELIANOID protected
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@faun shared a link, 9 months, 1 week ago
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So you want to parse a PDF?

Out of 3,977 real-world PDFs, 0.5% broke during xref pointer parsing. Not a huge number—unless you're the one parsing them. The top culprit? Junk data before the start pointer. Classic. Other file weirdness: broken xref tables, bad object offsets, and inconsistent xref chains... read more  

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@faun shared a link, 9 months, 1 week ago
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My Functional Programming Awakening: Patterns I'd Been Using All Along

A dev takes functional programming from Python class to JavaScript land—with surprising wins. The usual suspects show up:closures,function composition, and some spicyparser combinators. But the real magic? Swapping out side-effect soup forpure functions,Result-based error handling, andhigher-order f.. read more  

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@faun shared a link, 9 months, 1 week ago
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Scaling Netflix's threat detection pipelines without streaming

Netflix’s “Psycho Pattern” stitched togetherSpark, Kafka, and Airflowinto a relentless micro-batch pipeline. It tracked high watermarks for near-real-time threat detection—fast enough, sharp enough. Then came the Flink switch. Lower latency? Sure. But it missed the mark. Signal quality stayed flat... read more  

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@faun shared a link, 9 months, 1 week ago
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2025 Stack Overflow Developer Survey

Visual Studio and VS Code continue to reign supreme, fending off AI IDEs in the Stack Overflow 2025 Developer Survey. AI-generated devs noted as time-consuming and lacking trust, while Microsoft tools still dominate in agentic AI with GitHub and ChatGPT. More to discover, as always, Stack Overflow D.. read more  

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@faun shared a link, 9 months, 1 week ago
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GitHub Copilot crosses 20M all-time users

GitHub Copilot just crossed20 million users. Five million joined last quarter alone. Enterprise usage? Up75%quarter-over-quarter. It’s now in the hands of90% of the Fortune 100, according to Microsoft. Here’s the kicker: Copilot’s AI coding biz is now bigger than all of GitHub’s revenue when Micros.. read more  

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@faun shared a link, 9 months, 1 week ago
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The many, many, many JavaScript runtimes of the last decade

JavaScript runtimes aren’t just multiplying—they’re splintering. Big engines likeV8,JavaScriptCore,QuickJS,Hermes, andSpiderMonkeynow sit at the core of purpose-built runtimes everywhere: cloud, edge, mobile, IoT, even smart TVs. Platforms likeCloudflare Workers,Deno Deploy,Bun,LLRT, andNativeScrip.. read more  

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@faun shared a link, 9 months, 1 week ago
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GenAI vs. Agentic AI: What Developers Need to Know

Docker’s getting serious about agent-based AI. It just rolled out tools tailor-made for building modular, goal-chasing LLM systems. Model Runnerlets devs spin up LLMs locally—zero cloud, zero wait.Offloadtaps cloud GPUs when local ones tap out. And theMCP Gatewaypipes in external tools without duct.. read more  

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@faun shared a link, 9 months, 1 week ago
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Building a Redis Clone from Scratch – In-Memory KV Store with TCP

A solo dev just spun up a public build of aRedis-style key-value store in Java—lean, thread-safe, and backed by a custom TCP server. Right now it handlesGET,SET, andDELETEover a socket-level protocol. No HTTP. No bloat. At its core: aConcurrentHashMapdoing the heavy lifting. Fast, in-memory, and de.. read more  

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@faun shared a link, 9 months, 1 week ago
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I used NotebookLM to learn a new programming language, and it actually worked

A CS student taught themselvesSwiftusingNotebookLM, Google’s AI that sticks to sources you feed it. They pulled in handpicked docs, YouTube transcripts, and visual mind maps—all dropped into a custom notebook. No generic guesses. No hallucinated trivia. Just clean, source-grounded answers on syntax .. read more  

LangChain is a modular framework designed to help developers build complex, production-grade applications that leverage large language models. It abstracts the underlying complexity of prompt management, context retrieval, and model orchestration into reusable components. At its core, LangChain introduces primitives like Chains, Agents, and Tools, allowing developers to sequence model calls, make decisions dynamically, and integrate real-world data or APIs into LLM workflows.

LangChain supports retrieval-augmented generation (RAG) pipelines through integrations with vector databases, enabling models to access and reason over large external knowledge bases efficiently. It also provides utilities for handling long-term context via memory management and supports multiple backends like OpenAI, Anthropic, and local models.

Technically, LangChain simplifies building LLM-driven architectures such as chatbots, document Q&A systems, and autonomous agents. Its ecosystem includes components for caching, tracing, evaluation, and deployment, allowing seamless movement from prototype to production. It serves as a foundational layer for developers who need tight control over how language models interact with data and external systems.