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@kaptain shared a link, 1 week, 2 days ago
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jsongrep is faster than {jq, jmespath, jsonpath-rust, jql}

This article introduces a tool called jsongrep, explains the internal search engine it uses, and outlines the benchmarking strategy used to compare its performance with other JSON path-like query tools. The tool parses the JSON document, constructs an NFA from the query, determinizes the NFA into a .. read more  

jsongrep is faster than {jq, jmespath, jsonpath-rust, jql}
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@kaptain shared a link, 1 week, 2 days ago
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Deploying Disaggregated LLM Inference Workloads on Kubernetes

In large language model (LLM) inference workloads, a single monolithic serving process can hit its limits due to different compute profiles for prefill and decode stages. Disaggregated serving splits the pipeline into distinct stages to better utilize GPU resources and scale more flexibly on Kuberne.. read more  

Deploying Disaggregated LLM Inference Workloads on Kubernetes
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@kala shared a link, 1 week, 2 days ago
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What 81,000 people want from AI

Anthropic used a version of Claude to interview 80,508 users across 159 countries and 70 languages - claiming the largest qualitative AI study ever conducted. The top ask wasn't productivity, it was time back for things that matter outside of work. The top fear was hallucinations and unreliability. .. read more  

What 81,000 people want from AI
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@kala shared a link, 1 week, 2 days ago
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Building a digital doorman

Larson runs a dual-agent system. A tiny public doorman,nullclaw, lives on a $7 VPS. A private host,ironclaw, runs over Tailscale. Nullclaw sandboxes repo cloning. It routes heavy work to ironclaw viaA2AJSON‑RPC. It enforcesUFW, Cloudflare proxying, and single‑gateway billing... read more  

Building a digital doorman
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@kala shared a link, 1 week, 2 days ago
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Multi-Agent AI Systems: Architecture Patterns for Enterprise Deployment

Last quarter, a mid-sized insurance company struggled to deploy an AI agent that collapsed in production due to cognitive overload. Enterprises are facing similar challenges when building single-agent AI systems and are moving towards multi-agent architectures to distribute responsibilities effectiv.. read more  

Multi-Agent AI Systems: Architecture Patterns for Enterprise Deployment
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@kala shared a link, 1 week, 2 days ago
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Inside our approach to the Model Spec

OpenAI introduces Model Spec, a formal framework defining behavioral rules for their AI models to follow, aiming for transparency, safety, and public insight. The Model Spec includes a Chain of Command to resolve instruction conflicts and interpretive aids for consistent gray area decisions, emphasi.. read more  

Inside our approach to the Model Spec
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@kala shared a link, 1 week, 2 days ago
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How OpenAI Codex Works

Engineering leaders report limited ROI from AI, often missing full lifecycle costs. OpenAI's Codex model for cloud-based coding required significant engineering work beyond the AI model itself. The system's orchestration layer ensures rich context for the model to execute tasks effectively... read more  

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@devopslinks shared a link, 1 week, 2 days ago
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Software engineer interviews for the age of AI

AI is becoming more prevalent in coding interviews, sparking interest from experienced candidates tired of traditional methods. Hiring great engineers is crucial for maintaining reliable services, especially in the era of AI-generated code. System design interviews help identify candidates with hand.. read more  

Software engineer interviews for the age of AI
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@devopslinks shared a link, 1 week, 2 days ago
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Why system architects now default to Arm in AI data centers

Architects rebase infrastructure torack-levelsystems. They anchor designs onArm NeoverseCPUs. Goal: balance energy, thermals, memory bandwidth, and sustained throughput. Benchmarks showGraviton4(Neoverse) outperforms comparableAMDandIntelEC2instances on price/performance for generative AI, DB, ML, a.. read more  

Why system architects now default to Arm in AI data centers
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@devopslinks shared a link, 1 week, 2 days ago
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5 Suggestions to Upgrade your OpenTofu/Terraform & AWS Development Experience

The article covers tools and scripts to reclaim focus and improve workflow for OpenTofu, Terraform, and AWS CLI users. Suggestions include tools for easily swapping between versions, summarizing plans, linting code, switching AWS profiles, and customizing prompts. Bonus recommendation includes Task .. read more  

5 Suggestions to Upgrade your OpenTofu/Terraform & AWS Development Experience
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.

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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.