Why Synthetic RAG Index Lite? In the fast-moving landscape of large language models (LLMs) and retrieval-augmented generation (RAG), it’s essential to have a straightforward yet powerful tool. Microsoft’s Synthetic RAG Index is a robust solution for indexing and compressing large amounts of data, but sometimes you just need core functionalities without a full-stack deployment. That’s where Synthetic RAG Index Lite steps in. Key Goals: Lightweight Implementation: Keep the essential steps - extract, synthesize, and index - without the overhead of more advanced serverless architecture. Multi-Provider Support: Integrate easily with multiple LLM providers using LiteLLM to choose the best model for your use case. User-Friendliness: Provide clear commands, environment configurations, and minimal friction for setup. This Lite version preserves the spirit and core ideas from Microsoft’s original Synthetic RAG Index, while introducing simpler structures for smaller-scale or quick-turnaround projects. It respects the seminal work that inspired it, yet provides a tailored alternative for those seeking a direct, minimal solution. ...

TL;DR Ollama Minions is a framework that orchestrates hybrid local-cloud inference for large language models. Instead of sending an entire, possibly massive document to the cloud model for processing (which can be prohibitively expensive and raises privacy concerns), Minions enables your local LM - for instance, Llama 3.2 running on your own machine - to handle most of the input. The cloud model (such as GPT-4o) is called upon only when necessary for advanced reasoning, ensuring minimal API usage and associated costs. ...

Introduction: Why Azure Resource Inventory Matters With Azure’s vast ecosystem of virtual machines, storage accounts, databases, networks, and countless other services, keeping track of all deployed resources can be challenging. Azure Resource Inventory (ARI) addresses this problem head-on, collecting details from multiple subscriptions and generating an Excel workbook that breaks down each resource type - complete with security recommendations, cost insights, and more. This single consolidated report is a game-changer for cloud administrators, helping them govern infrastructure, optimize costs, and streamline audits. ...

Functionality and Architecture Browser-use is an open-source AI-powered browser automation framework that lets AI agents control a web browser via natural language instructions. Under the hood, it combines large language models (LLMs) with a headless browser automation engine (built on Playwright/Chromium) to interpret tasks and perform web interactions autonomously . When given a task, the Browser-use Agent uses an LLM (like GPT-4o or Claude) to analyze the goal and the current webpage, then issues browser commands (clicks, form fills, navigation, etc.) to achieve that goal. ...

Summary The EU Cyber Resilience Act (CRA) is a groundbreaking regulation establishing mandatory cybersecurity standards for most hardware and software products with digital elements in the EU. It aims to embed cybersecurity “by design and by default” across the product lifecycle – from development through post-sales support – to safeguard consumers and businesses from insecure technology (Cyber Resilience Act | Shaping Europe’s digital future) (The EU Cyber Resilience Act: Implications for Companies). The CRA applies broadly to connected products (including IoT devices, software, and hardware) offered on the EU market, imposing obligations on manufacturers (and other supply-chain operators) to ensure ongoing product security and swift vulnerability management. Key principles include secure product design, regular security updates for at least 5 years, incident and vulnerability reporting within 24 hours, and increased accountability via CE marking and documentation. Non-compliance can trigger fines up to €15 million or 2.5% of global turnover (Council of the European Union Adopts the Cyber Resilience Act). The CRA complements existing EU laws like GDPR, NIS2, and the proposed AI Act, closing gaps and creating a more cohesive cybersecurity framework. This report provides both a high-level overview and an in-depth breakdown of CRA’s scope, requirements, and implications, with tailored guidance for tech companies, IoT manufacturers, and financial institutions on achieving compliance in a cost-effective, phased manner. ...

TL;DR Just parse JSON into a JToken, somehow try to match property names. Wherever there’s a match, you can remove the property from its parent object. RemoveFromLowestPossibleParent Use method RemoveFromLowestPossibleParent from nice JsonExtensions class from Brian Rogers stackoverflow answer Code public static partial class JsonExtensions { public static TJToken RemoveFromLowestPossibleParent<TJToken>(this TJToken node) where TJToken : JToken { if (node == null) return null; JToken toRemove; var property = node.Parent as JProperty; if (property != null) { // Also detach the node from its immediate containing property -- Remove() does not do this even though it seems like it should toRemove = property; property.Value = null; } else { toRemove = node; } if (toRemove.Parent != null) toRemove.Remove(); return node; } public static IEnumerable<TJToken> RemoveFromLowestPossibleParents<TJToken>(this IEnumerable<TJToken> nodes) where TJToken : JToken { var list = nodes.ToList(); foreach (var node in list) node.RemoveFromLowestPossibleParent(); return list; } } Json field name from lambda expression Using PropertyHelper<YourClass>.GetJsonName(c => c.Id) on property with JsonPropertyAttribute attribute will return you json field name for this property. ...

TL;DR Shutdown all VMs than run modprobe -r kvm_intel and modprobe kvm_intel nested=1 in unraid terminal. After that add <feature policy='require' name='vmx'/> to the CPU Section of target VM. VM Make sure that all VM are stopped. Wait a little bit after stopping. Open the Unraid Terminal Run the following via a terminal in unraid (SpaceInvader’s thank you!) #!/bin/bash modprobe -r kvm_amd modprobe kvm_amd nested=1 echo "Nested vms are enabled for AMD cpus" sleep 10 exit Open VM Settings -> Toggle XML View Add feature to to the CPU Section ...

TL;DR Using SpaceinvaderOne’s docker app named Macinabox install MacOS, then install Splashtop Streamer and NVIDIA WebDriver for GPU and then update XML config Macinbox Start from this SpaceinvaderOne’s video about Macinbox - you will get all information Then install Macinbox - it will download all needed stuff for you and install MacOS Apps After that login to VM and install two apps: webdriver.sh - Bash script for managing NVIDIA’s web drivers on macOS High Sierra and later with an option to set the required build number in NVDAStartupWeb.kext and NVDAEGPUSupport.kext. Splashtop Personal - Remote Desktop gives you high-performance remote access to your PC or Mac from your Windows tablet. Splashtop is the ONLY remote access product capable of delivering full audio and HD video streaming and even interactive 3D gaming. (Access across the Internet requires purchase of the optional Anywhere Access Pack.) webdriver.sh Install webdriver.sh with Homebrew ...

TL;DR perf_hooks + wrapper which will redefine the original method to new method who will call the original method + publish information to Prometheus. Define a Histogram from prom-client library: import { Histogram } from 'prom-client' const requestDuration = new Histogram({ name: 'request_duration_ms', help: 'Duration of requests', labelNames: ['method'], buckets: [ 0.1, 50, 100, 200, 300, 400, 500, 1000 ] // buckets for response time from 0.1ms to 1000ms }) The decorator for non-async methods will be simple - redefine the method to this new method who will call the original method. Make sure that you use the function’s this context instead of the value of this when it is called (no arrow function) ...