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@faun shared a link, 4 months, 3 weeks ago
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From Big Data to Heavy Data: Rethinking the AI Stack

Savvy teams morph dense data into AI’s favorite meal: bite-sized chunks primed for action, indexed and ready to go. This trick spares everyone from slogging through the same info over and over. AI craves structured, context-filled data to keep it grounded and hallucination-free. Without structured p.. read more  

From Big Data to Heavy Data: Rethinking the AI Stack
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@faun shared a link, 4 months, 3 weeks ago
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Building tiny AI tools for developer productivity

Tiny AI scripts won't make you the next tech billionaire, but they're unbeatable for rescuing hours from the drudgery of repetitive tasks. Whether it's wrangling those dreadedGitHub rollupsor automating the minutiae, these little miracles grant engineers the luxury to actually think... read more  

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@faun shared a link, 4 months, 3 weeks ago
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The Portable Memory Wallet Fallacy: 4 Fundamental Problems

Portable AI memory pods hit a brick wall—vendors cling to data control, users resist micromanagement, and technical snarls persist.So, steer regulation towards automating privacy and clarifying transparency. Make AI interaction sync with how people actually live... read more  

The Portable Memory Wallet Fallacy: 4 Fundamental Problems
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@faun shared a link, 4 months, 3 weeks ago
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Supabase MCP can leak your entire SQL database

Supabase MCP'saccess can barge right past RLS,spilling SQL databaseswhen faced with sneaky inputs. It's a cautionary tale from the world ofLLM system trifecta attacks... read more  

Supabase MCP can leak your entire SQL database
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@faun shared a link, 4 months, 3 weeks ago
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LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide - Confident AI

Dump BLEU and ROUGE. Let LLM-as-a-judge tools like G-Eval propel you to pinpoint accuracy.The old scorers? They whiff on meaning, like a cat batting at a laser dot.DeepEval? It wrangles bleeding-edge metrics with five lines of neat code.Want a personal touch? G-Eval's got your back. DAG keeps benchm.. read more  

LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide - Confident AI
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@faun shared a link, 4 months, 3 weeks ago
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My Honest Advice for Aspiring Machine Learning Engineers

Becoming a machine learning engineer requires dedicatingat least 10 hours per weekto studying outside of everyday responsibilities. This can take a minimum of two years, even with an ideal background, due to the complexity of the required skills. Understanding core algorithms and mastering the funda.. read more  

My Honest Advice for Aspiring Machine Learning Engineers
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@faun shared a link, 4 months, 3 weeks ago
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Document Search with NLP: What Actually Works (and Why)

NLP document search trounces old-school keyword hunting. It taps into scalable*vector databasesandsemantic vectorsto grasp meaning, not just parrot words.* Pictureword vector arithmetic: "King - Man + Woman = Queen." It's magic. Searches become lightning-fast and drenched in context... read more  

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@faun shared a link, 4 months, 3 weeks ago
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A non-anthropomorphized view of LLMs

CallingLLMssentient or ethical? That's a stretch. Behind the curtain, they're just fancy algorithms dressed up as text wizards. Humans? They're a whole mess of complexity... read more  

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@faun shared a link, 4 months, 3 weeks ago
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Meta Hires OpenAI Researchers to Boost AI Capabilities

Metacranks up its AI antics. They've snagged former OpenAI whiz kids, snatched 49% ofScale AI, and roped in enough nuclear energy to keep their data hubs humming all night long... read more  

Meta Hires OpenAI Researchers to Boost AI Capabilities
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@faun shared a link, 4 months, 3 weeks ago
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Context Engineering for Agents

Context engineeringcranks an AI agent up to 11 by juggling memory like a slick OS. It writes, selects, compresses, and isolates—never missing a beat despite those pesky token limits. Nail the context, and you've got a dream team. Slip up, though, and you might trigger chaos, like when ChatGPT went r.. read more  

Context Engineering for Agents
FAUN.dev() is a developer-first platform built with a simple goal: help engineers stay sharp without wasting their time. It curates practical newsletters, thoughtful technical blogs, and useful developer tools that focus on signal over noise.

Created by engineers, for engineers, FAUN.dev() is where experienced developers turn to keep up with the fast-moving world of DevOps, Kubernetes, Cloud Native, AI, and modern programming. We handpick what matters and skip the fluff.

If it’s on FAUN.dev(), it’s worth your attention.

Beyond curation, we run a course marketplace (WIP) designed to keep developers current. These courses go deep into the subjects that shape real-world work—things like Kubernetes internals, modern DevOps workflows, cloud-native architecture, and using AI tools to build faster and smarter. It’s practical learning, taught by people who’ve done the work. Developers from companies like GitHub, Netflix, and Shopify already rely on FAUN.dev() to stay on top of their game. They trust us because we keep it real: no hype, no filler, just what you need to grow and do your best work. For sponsors and partners, FAUN.dev() offers access to a focused, engaged audience of technical professionals. This isn’t just another broad developer community—it’s a place where smart engineers go to get smarter. If you have something meaningful to offer them, you’ll be in good company. In short, FAUN.dev() is more than a content hub. It’s a place to grow, to learn, and to connect with what really matters in software today.