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product #tooling 📝 BlogAnalyzed: Jan 4, 2026 09:48

Reverse Engineering reviw CLI's Browser UI: A Deep Dive

Published:Jan 4, 2026 01:43

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1 min read

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Zenn Claude

Analysis

This article provides a valuable look into the implementation details of reviw CLI's browser UI, focusing on its use of Node.js, Beacon API, and SSE for facilitating AI code review. Understanding these architectural choices offers insights into building similar interactive tools for AI development workflows. The article's value lies in its practical approach to dissecting a real-world application.

Key Takeaways

•reviw CLI utilizes a Node.js HTTP server to serve the browser UI.
•The browser UI leverages Beacon API for sending data.
•Server-Sent Events (SSE) are used for real-time communication.

Reference

“特に面白いのが、ブラウザで Markdown や Diff を表示し、行単位でコメントを付けて、それを YAML 形式で Claude Code に返すという仕組み。”

Permalink Zenn Claude

Technology #Prompt Engineering 📝 BlogAnalyzed: Jan 3, 2026 06:07

Introduction to Prompt Design: How to Effectively Use YAML, Markdown, and JSON and Avoid Template Failures

Published:Jan 2, 2026 03:32

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1 min read

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Zenn GPT

Analysis

This article targets beginners using ChatGPT who are unsure how to write prompts effectively. It aims to clarify the use of YAML, Markdown, and JSON for prompt engineering. The article's structure suggests a practical, beginner-friendly approach to improving prompt quality and consistency.

Key Takeaways

•The article focuses on practical application for beginners.
•It addresses the confusion surrounding YAML, Markdown, and JSON in the context of prompt engineering.
•The title suggests a focus on avoiding common pitfalls in prompt design.

Reference

“The article's introduction clearly defines its target audience and learning objectives, setting expectations for readers.”

Permalink Zenn GPT

Technology #AI Tools 📝 BlogAnalyzed: Jan 3, 2026 06:12

Tuning Slides Created with NotebookLM Using Nano Banana Pro

Published:Dec 29, 2025 22:59

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1 min read

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Zenn Gemini

Analysis

This article describes how to refine slides created with NotebookLM using Nano Banana Pro. It addresses practical issues like design mismatches and background transparency, providing prompts for solutions. The article is a follow-up to a previous one on quickly building slide structures and designs using NotebookLM and YAML files.

Key Takeaways

•The article is a follow-up to a previous one on using NotebookLM and YAML for slide creation.
•It focuses on using Nano Banana Pro to improve the quality of slides.
•Addresses practical design and usability issues.
•Provides specific prompts for solutions.

Reference

“The article focuses on how to solve problems encountered in practice, such as "I like the slide composition and layout, but the design doesn't fit" and "I want to make the background transparent so it's easy to use as a material."”

Permalink Zenn Gemini

Robotics #Robot Description Formats 🔬 ResearchAnalyzed: Jan 3, 2026 19:10

URDD: A Unified Robot Description for Enhanced Robotics Applications

Published:Dec 29, 2025 01:51

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1 min read

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ArXiv

Analysis

This paper introduces the Universal Robot Description Directory (URDD) as a solution to the limitations of existing robot description formats like URDF. By organizing derived robot information into structured JSON and YAML modules, URDD aims to reduce redundant computations, improve standardization, and facilitate the construction of core robotics subroutines. The open-source toolkit and visualization tools further enhance its practicality and accessibility.

Key Takeaways

•URDD is a modular representation for robot information, using JSON and YAML.
•It aims to reduce redundant computations and improve standardization in robotics.
•An open-source toolkit and visualization tools are provided.
•URDDs can encapsulate richer information than standard specification files.

Reference

“URDD provides a unified, extensible resource for reducing redundancy and establishing shared standards across robotics frameworks.”

Permalink ArXiv

Software #llm 📝 BlogAnalyzed: Dec 28, 2025 14:02

Debugging MCP servers is painful. I built a CLI to make it testable.

Published:Dec 28, 2025 13:18

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1 min read

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r/ArtificialInteligence

Analysis

This article discusses the challenges of debugging MCP (likely referring to Multi-Chain Processing or a similar concept in LLM orchestration) servers and introduces Syrin, a CLI tool designed to address these issues. The tool aims to provide better visibility into LLM tool selection, prevent looping or silent failures, and enable deterministic testing of MCP behavior. Syrin supports multiple LLMs, offers safe execution with event tracing, and uses YAML configuration. The author is actively developing features for deterministic unit tests and workflow testing. This project highlights the growing need for robust debugging and testing tools in the development of complex LLM-powered applications.

Key Takeaways

•Syrin is a CLI tool for debugging and testing MCP servers.
•It addresses issues like lack of visibility into LLM tool selection and non-deterministic testing.
•The tool supports multiple LLMs and offers safe execution with event tracing.

Reference

“No visibility into why an LLM picked a tool”

Permalink r/ArtificialInteligence

Research #llm 📝 BlogAnalyzed: Dec 28, 2025 11:00

Image Generation AI: Which is better for prompt instructions, Markdown or YAML? Explanation of conclusions and how to use them

Published:Dec 28, 2025 10:45

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1 min read

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Qiita AI

Analysis

This article from Qiita AI discusses the best way to format prompts for image generation AIs like Midjourney and ChatGPT, focusing on Markdown and YAML. It likely compares the readability, ease of use, and suitability of each format for complex prompts. The article probably provides practical examples and recommendations for when to use each format based on the complexity and structure of the desired image. It's a useful guide for users who want to improve their prompt engineering skills and streamline their workflow when working with image generation AIs. The article's value lies in its practical advice and comparison of two popular formatting options.

Key Takeaways

•Markdown and YAML are both viable options for formatting AI prompts.
•The best choice depends on the complexity and structure of the prompt.
•The article provides guidance on when to use each format.

Reference

“The article discusses the advantages and disadvantages of using Markdown and YAML for prompt instructions.”

Permalink Qiita AI

product #prompt 📝 BlogAnalyzed: Jan 5, 2026 09:13

Desktop App for YAML-Structured Management of Image Generation AI Prompts

Published:Dec 28, 2025 04:35

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1 min read

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Zenn GenAI

Analysis

This article discusses the development of a desktop application for managing image generation AI prompts using YAML, addressing the challenge of organizing and versioning complex prompt structures. The focus on YAML suggests a technical audience familiar with configuration management and a need for reproducible image generation workflows. The business value lies in improved efficiency and consistency in AI-driven content creation.

Key Takeaways

•The article introduces a desktop application for managing image generation AI prompts.
•The application utilizes YAML for structured prompt management.
•The author highlights the importance of models, prompts, and control techniques in image generation.

Reference

“自分は2023年の前半くらいからStable Diffusion WebUI（A1111）を触りはじめた”

Permalink Zenn GenAI

Infrastructure #ML Engineering 👥 CommunityAnalyzed: Jan 10, 2026 16:42

YAML vs. Notebooks: Streamlining ML Engineering Workflows

Published:Apr 9, 2020 14:52

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1 min read

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Hacker News

Analysis

This article likely discusses the advantages of using YAML for machine learning pipelines over the traditional notebook approach, potentially focusing on reproducibility and maintainability. Analyzing the Hacker News discussion provides a valuable look at practical industry preferences and the evolution of ML engineering practices.

Key Takeaways

•YAML likely offers benefits such as version control and easier configuration for ML pipelines compared to notebooks.
•The article might highlight challenges of managing notebooks in production environments.
•The Hacker News context suggests industry trends and practical considerations for ML engineers.

Reference

“The article's core argument revolves around a preference for YAML in machine learning engineering, replacing the notebook paradigm.”

Permalink Hacker News

Reverse Engineering reviw CLI's Browser UI: A Deep Dive

Analysis

Key Takeaways

Introduction to Prompt Design: How to Effectively Use YAML, Markdown, and JSON and Avoid Template Failures

Analysis

Key Takeaways

Tuning Slides Created with NotebookLM Using Nano Banana Pro

Analysis

Key Takeaways

URDD: A Unified Robot Description for Enhanced Robotics Applications

Analysis

Key Takeaways

Debugging MCP servers is painful. I built a CLI to make it testable.

Analysis

Key Takeaways

Image Generation AI: Which is better for prompt instructions, Markdown or YAML? Explanation of conclusions and how to use them

Analysis

Key Takeaways

Desktop App for YAML-Structured Management of Image Generation AI Prompts

Analysis

Key Takeaways

YAML vs. Notebooks: Streamlining ML Engineering Workflows

Analysis

Key Takeaways

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