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Research#llm📝 BlogAnalyzed: Dec 26, 2025 10:23

Creating a "Development Runs with One Ticket Number Input" Mechanism with AI x MCP: Backlog MCP x Figma MCP (Codex Optimization Edition)

Published:Dec 26, 2025 10:20
1 min read
Qiita AI

Analysis

This article discusses the creation of a system that streamlines the development process by automating several initial steps based on a single ticket number input. It leverages AI, specifically Codex optimization, in conjunction with Backlog MCP and Figma MCP to automate tasks such as issue retrieval, summarization, task breakdown, and generating work procedures. The article is a continuation of a previous one, suggesting a series of improvements and iterations on the system. The focus is on reducing the manual effort involved in the early stages of development, thereby increasing efficiency and potentially reducing errors. The use of AI to automate these tasks highlights the potential for AI to improve developer workflows.
Reference

“本稿は 現状共有編の続編 です。”

PermalinkQiita AI
Research#llm📝 BlogAnalyzed: Dec 25, 2025 06:10

Creating a "Mechanism to Start Development with One Ticket Number Input" using AI x MCP: Backlog MCP x Figma MCP (Current Status Sharing Edition)

Published:Dec 25, 2025 06:07
1 min read
Qiita AI

Analysis

This article discusses automating the initial steps of software development using AI and MCP (presumably a custom platform). The author, a front-end developer, aims to streamline the process of reading tasks, creating branches, finding designs, and drafting pull requests. By automating these steps with a single ticket number input, the author seeks to save time and improve focus. The article likely details the specific tools and techniques used to achieve this automation, potentially including integrations between Backlog, Figma, and the custom MCP. It highlights a practical application of AI in improving developer workflow and productivity. The "Current Status Sharing Edition" suggests this is part of a series, indicating ongoing development and refinement of the system.
Reference

“"I usually do front-end development, but I was spending a considerable amount of time and concentration on this 'pre-development ritual' of reading tasks, creating branches, finding designs, and drafting PRs."”

PermalinkQiita AI
Data Science#Non-Stationary Data, Prioritization, Binary Classification📝 BlogAnalyzed: Jan 3, 2026 07:01

Non-Stationary Categorical Data Prioritization

Published:Dec 23, 2025 09:23
1 min read
r/datascience

Analysis

The article describes a real-world problem of prioritizing items in a backlog where the features are categorical, the target is binary, and the scores evolve over time as more information becomes available. The core challenge is that the data is non-stationary, meaning the relationship between features and the target changes over time. The author is seeking advice on the appropriate modeling approach and how to handle training and testing to reflect the inference process. The problem is well-defined and highlights the complexities of using machine learning in dynamic environments.
Reference

“The important part is that the model is not trying to predict how the item evolves over time. Each score is meant to answer a static question: “Given everything we know right now, how should this item be prioritized relative to the others?””

Permalinkr/datascience
Software Engineering#RAG, GitHub, LLM👥 CommunityAnalyzed: Jan 3, 2026 16:45

Using Retrieval Augmented Generation (RAG) to clear our GitHub backlog

Published:Aug 10, 2023 20:07
1 min read
Hacker News

Analysis

The article's focus is on applying Retrieval Augmented Generation (RAG) to a practical problem: managing a GitHub backlog. This suggests a practical application of LLMs. The title is clear and concise, indicating the core topic.

Key Takeaways

Reference

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PermalinkHacker News