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

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, 7 months, 3 weeks ago
FAUN.dev()

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, 7 months, 3 weeks ago
FAUN.dev()

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, 7 months, 3 weeks ago
FAUN.dev()

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, 7 months, 3 weeks ago
FAUN.dev()

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, 7 months, 3 weeks ago
FAUN.dev()

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, 7 months, 3 weeks ago
FAUN.dev()

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  

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@faun shared a link, 7 months, 3 weeks ago
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How we discovered, and recovered from, Postgres corruption on the homeserver

PostgreSQL index corruption silently broke the matrix.org homeserver. State groups were corrupted, active data was deleted, and restoring consistency took a week of forensic debugging and reindexing. The root cause? Unclear. Hardware, maybe. But not Postgres or Synapse. The team’s fix involved disab.. read more  

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

📌 New: netstat Command Cheatsheet

Need to check active connections, monitor listening ports, or debug network issues? The Linux netstat command remains a go-to tool for quick and effective diagnostics. We’ve created a clear, quick-reference cheatsheet with: 🔍 Essential command flags 📊 Real-world use cases ⚙️ Integration tips for REL..

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

Building Reproducible ML Systems with Apache Iceberg and SparkSQL

Apache Iceberg +SparkSQLbringsACID transactions,schema evolution, andtime travelto data lakes. That means ML pipelines finally get reproducibility and consistency without the hacks. Iceberg’s snapshot-based guts track every version, handle parallel writes without stepping on toes, and keep training .. read more  

Building Reproducible ML Systems with Apache Iceberg and SparkSQL
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