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@kaptain shared a link, 5 months, 3 weeks ago
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The Grafana trust problem

Grafana’s been busy clearing the shelves.Grafana Agent,Agent Flow, andOnCall? All deprecated. The replacement:Grafana Alloy- a one-stop observability agent that handles logs, metrics, traces, and OTEL without flinching. Meanwhile,Mimir 3.0ships with a Kafka-powered ingestion pipeline. More scalabili.. read more  

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@kaptain shared a link, 5 months, 3 weeks ago
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Kubernetes Configuration Good Practices

Stripped down and sharp, the blog lays out Kubernetes config best practices: keep YAML manifests in version control, use Deployments (not raw Pods), and label like you mean it - semantically, not just alphabet soup. It digs into sneaky pain points too, like how YAML mangles booleans (yes≠true), and .. read more  

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@kala shared a link, 5 months, 3 weeks ago
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How I Built a 100% Offline “Second Brain” for Engineering Docs using Docker & Llama 3 (No OpenAI)

Senior Automation Engineer built an offline RAG system for technical documents using Ollama, Llama 3, and ChromaDB in a Dockerized microservices architecture. The system enables efficient retrieval and generation of information from PDFs with a streamlined UI. The deployment package, including compl.. read more  

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@kala shared a link, 5 months, 3 weeks ago
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How to Evaluate LLMs Without Opening Your Wallet

A new mock-based framework lets QA and automation folks stress-test LLM outputs - no API calls, no surprise charges. It runs entirely local, usingpytest fixtures, structured test flows, and JSON schema checks to keep things tight. Test logic stays modular. Cross-validation’s baked in. And if you nee.. read more  

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@kala shared a link, 5 months, 3 weeks ago
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I tested ChatGPT’s backend API using RENTGEN, and found more issues than expected

A closer look at OpenAI’s API uncovers some shaky ground: misconfiguredCORS headers, missingX-Frame-Options, noinput validation, and borkedHTTP status handling. Large uploads? Boom..crash!CORS preflightrequests? Straight-up denied. So much for smooth browser support... read more  

I tested ChatGPT’s backend API using RENTGEN, and found more issues than expected
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@kala shared a link, 5 months, 3 weeks ago
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Writing a good CLAUDE.md

Anthropic’s Claude Code now deprioritizes parts of the root context file it sees as irrelevant. It still reads the file every session, but won’t waste cycles on side quests. The message to devs: stop stuffing it with catch-all instructions. Instead, use modular context that unfolds as needed - think.. read more  

Writing a good CLAUDE.md
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@kala shared a link, 5 months, 3 weeks ago
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1,500+ PRs Later: Spotify’s Journey with Our Background Coding Agent

Spotify just gave its internal Fleet Management tooling a serious brain upgrade. They've wired inAI coding agentsthat now handle source-to-source transformations across repos - automatically. So far? Over 1,500 AI-generated PRs pushed. Not just lint fixes - these include heavy-duty migrations. They'.. read more  

1,500+ PRs Later: Spotify’s Journey with Our Background Coding Agent
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@kala shared a link, 5 months, 3 weeks ago
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AI and QE: Patterns and Anti-Patterns

The author shared insights on how AI can be leveraged as a QE and highlighted potential dangers to watch out for, drawing parallels with misuse of positive behaviors or characteristics taken out of context. The post outlined anti-patterns related to automating tasks, stimulating thinking, and tailor.. read more  

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@kala shared a link, 5 months, 3 weeks ago
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Cato CTRL™ Threat Research: HashJack - Novel Indirect Prompt Injection Against AI Browser Assistants

A new attack method -HashJack- shows how AI browsers can be tricked with nothing more than a URL fragment. It works like this: drop malicious instructions after the#in a link, and AI copilots likeComet,Copilot for Edge, andGemini for Chromemight swallow them whole. No need to hack the site. The LLM .. read more  

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@devopslinks shared a link, 5 months, 3 weeks ago
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How when AWS was down, we were not

During the AWS us-east-1 meltdown - when DynamoDB, IAM, and other key services went dark - Authress kept the lights on. Their trick? A ruthless edge-first, multi-region setup built for failure. They didn’t hope DNS would save them. They wired in automated failover, rolled their own health checks, an.. read more  

How when AWS was down, we were not
vLLM is an advanced open-source framework for serving and running large language models efficiently at scale. Developed by researchers and engineers from UC Berkeley and adopted widely across the AI industry, vLLM focuses on optimizing inference performance through its innovative PagedAttention mechanism — a memory management system that enables near-zero waste in GPU memory utilization. It supports model parallelism, continuous batching, tensor parallelism, and dynamic batching across GPUs, making it ideal for real-world deployment of foundation models. vLLM integrates seamlessly with Hugging Face Transformers, OpenAI-compatible APIs, and popular orchestration tools like Ray Serve and Kubernetes. Its design allows developers and enterprises to host LLMs with reduced latency, lower hardware costs, and increased throughput, powering everything from chatbots to enterprise-scale AI services.