Based on my search results, I've found several excellent therapeutic listening tutorials and resources on YouTube specifically for parents of autistic children. Here's a comprehensive guide:\n\n## Therapeutic Listening YouTube Tutorials for Parents of Autistic Children\n\n### 1. Best YouTube Video for Starting Therapeutic Listening\n**"Ep 504 | Starting Therapeutic Listening for child diagnosed with Autism"** [citation:0] \nThis is the most comprehensive tutorial I found specifically addressing parents' questions about starting therapeutic listening.

Occupational therapist Reena Singh covers:\n- Can therapeutic listening be done in the car? \n- Does my child need to do it with a therapist or can it be done at home? \n- What if my child doesn't tolerate headphones? \n- Which headphones are best for therapeutic listening?\n- How to start if your child resists\n- Practical steps to integrate therapeutic listening into daily routines\n\n### 2. Key YouTube Channels for Parents\n\n**A. Awetism Insig

TOON, or Token-Oriented Object Notation, is a lightweight, human-readable data serialization format specifically designed for use with Large Language Models (LLMs). It acts as a more efficient alternative to JSON by reducing token consumption in prompts, making it ideal for passing structured data to AI systems without losing information. TOON is particularly effective for uniform tabular data, such as arrays of objects with consistent fields, where it can achieve 30-60% fewer tokens compared to JSON, based on benchmarks using common tokenizers like those in GPT models. This efficiency comes from stripping away redundant syntax like braces, brackets, and repeated keys, while relying on indentation and length markers to maintain structure.

The main goal of TOON is to optimize for LLM contexts, where token limits and costs are critical. JSON's verbosity can inflate prompts unnecessarily, especially with large datasets, but TOON minimizes this by:

• Using indentation for nesting (simila

LLM-as-a-Judge (also called model-based evaluation) is a scalable way to use one LLM to automatically score and reason about the outputs of another LLM or agent. In the context of your CanvassAssistant (backed by OpenAI's Assistant API via Azure), this is ideal for tuning prompts and fixing issues like repetitive questions. For example, you can evaluate thread histories for redundancy, coherence, or completeness, generating scores (e.g., 0-1) with chain-of-thought explanations.

Langfuse provides managed LLM-as-a-Judge evaluators through its UI, with a catalog of pre-built templates (e.g., for Hallucination, Helpfulness, Toxicity) and support for customization. It integrates seamlessly with your traced runs from the previous setup (using @langfuse/openai or manual observations). Evaluations can run on production traces (e.g., your callEndpoint executions) or offline datasets.

If you need fully custom logic beyond the UI (e.g., specialized scoring for repetitiveness),

Tutorial: Using Azure Cache for Redis with TypeScript This tutorial guides you through setting up and using Azure Cache for Redis in a TypeScript application, implementing the cache-aside pattern. We'll use the ioredis library for Redis connectivity and demonstrate basic caching operations. Prerequisites

An active Azure subscription Node.js (v16 or later) and npm installed An Azure Cache for Redis instance (Basic, Standard, Premium, or Enterprise tier) TypeScript installed (npm install -g typescript)

Step 1: Create an Azure Cache for Redis Instance

To formalize the last step of your proof, let’s carefully work through the problem of showing that any linear permutation-equivariant function ( F: \mathbb{R}^n \to \mathbb{R}^n ) can be written as ( F(X) = aI X + b 11^T X ), where ( I ) is the identity matrix, ( 11^T ) is the matrix corresponding to the average function, and ( a, b \in \mathbb{R} ). The key property is that ( F ) is linear and permutation-equivariant, meaning ( FPX = PFX ) for any permutation matrix ( P ). Your insight about setting ( X = 11^T ) is a good starting point, and we’ll use it to derive the result.

Since ( F ) is a linear function from ( \mathbb{R}^n \to \mathbb{R}^n ), it can be represented by an ( n \times n ) matrix, say ( A ), such that ( F(X) = AX ). The permutation-equivariance condition ( FPX = PFX ) translates to:

[ A(PX) = P(AX) ]

When deploying a TypeScript Azure Function App with multiple files (e.g., a handler and a library) to Azure, the issue of functions not appearing in the Azure Portal often stems from incorrect project structure, configuration, or deployment settings. Below, I'll guide you through the steps to properly deploy a TypeScript Azure Function App with multiple files, ensuring the functions are visible in the Azure Portal.

The problem you're facing—functions not appearing in the Azure Portal—can occur due to:

1. Incorrect project structure: Azure Functions expects a specific folder structure, especially for TypeScript projects where compiled JavaScript files are used.
2. Missing or incorrect function.json files: For the v3 programming model, these files define the function's bindings and entry points.
3. Improper compilation or deployment: TypeScript files need to be compiled to JavaScript, and all

Applying differential and computational geometry to read letters on a warped piece of paper is a fascinating problem that involves modeling the paper’s deformation, reconstructing its 3D shape, and extracting text from a distorted surface. This task has applications in document analysis, historical manuscript restoration, and computer vision. Below, I’ll outline the key concepts, existing work, and related projects in this domain, focusing on differential and computational geometry approaches. I’ll keep the explanation concise yet comprehensive, as you’ve expressed curiosity without specifying a desired length.

1. Differential Geometry: This field provides tools to describe the geometry of surfaces, such as a warped piece of paper, using concepts like curvature, tangent planes, and geodesics. For a warped paper, differential geometry helps model the surface as a smooth, non-Euclidean manifold, allowing us to understand its deformation mathematically.
2. Computational Geometry: This i

Both Aider and OpenHands are powerful open-source AI coding agents designed to assist developers, but they cater to slightly different workflows and preferences. Below is a detailed comparison of their pros and cons based on available information and community feedback, focusing on their features, usability, and performance as of July 2025.

Aider is a command-line-based AI coding assistant that enables pair programming with large language models (LLMs) to edit code in your local Git repository. It is designed for simplicity, speed, and integration with existing codebases.

1. Ease of Setup and Use:

• Installs quickly via pip install aider-chat and requires only an LLM API key to start.[](https://dev.to/composiodev/9-open-source-ai-coding-tools-that-