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research#ml📝 BlogAnalyzed: Jan 18, 2026 13:15

Demystifying Machine Learning: Predicting Housing Prices!

Published:Jan 18, 2026 13:10
1 min read
Qiita ML

Analysis

This article offers a fantastic, hands-on introduction to multiple linear regression using a simple dataset! It's an excellent resource for beginners, guiding them through the entire process, from data upload to model evaluation, making complex concepts accessible and fun.
Reference

This article will guide you through the basic steps, from uploading data to model training, evaluation, and actual inference.

research#agent📝 BlogAnalyzed: Jan 18, 2026 11:45

Action-Predicting AI: A Qiita Roundup of Innovative Development!

Published:Jan 18, 2026 11:38
1 min read
Qiita ML

Analysis

This Qiita compilation showcases an exciting project: an AI that analyzes game footage to predict optimal next actions! It's an inspiring example of practical AI implementation, offering a glimpse into how AI can revolutionize gameplay and strategic decision-making in real-time. This initiative highlights the potential for AI to enhance our understanding of complex systems.
Reference

This is a collection of articles from Qiita demonstrating the construction of an AI that takes gameplay footage (video) as input, estimates the game state, and proposes the next action.

research#llm📝 BlogAnalyzed: Jan 17, 2026 10:45

Optimizing F1 Score: A Fresh Perspective on Binary Classification with LLMs

Published:Jan 17, 2026 10:40
1 min read
Qiita AI

Analysis

This article beautifully leverages the power of Large Language Models (LLMs) to explore the nuances of F1 score optimization in binary classification problems! It's an exciting exploration into how to navigate class imbalances, a crucial consideration in real-world applications. The use of LLMs to derive a theoretical framework is a particularly innovative approach.
Reference

The article uses the power of LLMs to provide a theoretical explanation for optimizing F1 score.

research#llm📝 BlogAnalyzed: Jan 16, 2026 01:16

Boosting AI Efficiency: Optimizing Claude Code Skills for Targeted Tasks

Published:Jan 15, 2026 23:47
1 min read
Qiita LLM

Analysis

This article provides a fantastic roadmap for leveraging Claude Code Skills! It dives into the crucial first step of identifying ideal tasks for skill-based AI, using the Qiita tag validation process as a compelling example. This focused approach promises to unlock significant efficiency gains in various applications.
Reference

Claude Code Skill is not suitable for every task. As a first step, this article introduces the criteria for determining which tasks are suitable for Skill development, using the Qiita tag verification Skill as a concrete example.

product#code generation📝 BlogAnalyzed: Jan 15, 2026 14:45

Hands-on with Claude Code: From App Creation to Deployment

Published:Jan 15, 2026 14:42
1 min read
Qiita AI

Analysis

This article offers a practical, step-by-step guide to using Claude Code, a valuable resource for developers seeking to rapidly prototype and deploy applications. However, the analysis lacks depth regarding the technical capabilities of Claude Code, such as its performance, limitations, or potential advantages over alternative coding tools. Further investigation into its underlying architecture and competitive landscape would enhance its value.
Reference

This article aims to guide users through the process of creating a simple application and deploying it using Claude Code.

business#agent📝 BlogAnalyzed: Jan 15, 2026 10:45

Demystifying AI: Navigating the Fuzzy Boundaries and Unpacking the 'Is-It-AI?' Debate

Published:Jan 15, 2026 10:34
1 min read
Qiita AI

Analysis

This article targets a critical gap in public understanding of AI, the ambiguity surrounding its definition. By using examples like calculators versus AI-powered air conditioners, the article can help readers discern between automated processes and systems that employ advanced computational methods like machine learning for decision-making.
Reference

The article aims to clarify the boundary between AI and non-AI, using the example of why an air conditioner might be considered AI, while a calculator isn't.

research#llm📝 BlogAnalyzed: Jan 15, 2026 10:15

AI Dialogue on Programming: Beyond Manufacturing

Published:Jan 15, 2026 10:03
1 min read
Qiita AI

Analysis

The article's value lies in its exploration of AI-driven thought processes, specifically in the context of programming. The use of AI-to-AI dialogue to generate insights, rather than a static presentation of code or results, suggests a focus on the dynamics of AI reasoning. This approach could be very helpful in understanding how these models actually arrive at their conclusions.

Key Takeaways

Reference

The article states the AI dialogue yielded 'unexpectedly excellent thought processes'.

research#llm📝 BlogAnalyzed: Jan 15, 2026 07:15

Analyzing Select AI with "Query Dekisugikun": A Deep Dive (Part 2)

Published:Jan 15, 2026 07:05
1 min read
Qiita AI

Analysis

This article, the second part of a series, likely delves into a practical evaluation of Select AI using "Query Dekisugikun". The focus on practical application suggests a potential contribution to understanding Select AI's strengths and limitations in real-world scenarios, particularly relevant for developers and researchers.

Key Takeaways

Reference

The article's content provides insights into the continued evaluation of Select AI, building on the initial exploration.

infrastructure#gpu📝 BlogAnalyzed: Jan 12, 2026 13:15

Passing the NVIDIA NCA-AIIO: A Personal Account

Published:Jan 12, 2026 13:01
1 min read
Qiita AI

Analysis

This article, while likely containing practical insights for aspiring AI infrastructure specialists, lacks crucial information for a broader audience. The absence of specific technical details regarding the exam content and preparation strategies limits its practical value beyond a very niche audience. The limited scope also reduces its ability to contribute to broader industry discourse.

Key Takeaways

Reference

The article's disclaimer clarifies that the content is based on personal experience and is not affiliated with any company. (Note: Since the original content is incomplete, this is a general statement based on the provided snippet.)

product#llm📝 BlogAnalyzed: Jan 11, 2026 20:00

AI-Powered Writing System Facilitates Qiita Advent Calendar Success

Published:Jan 11, 2026 15:49
1 min read
Zenn AI

Analysis

This article highlights the practical application of AI in content creation for a specific use case, demonstrating the potential for AI to streamline and improve writing workflows. The focus on quality maintenance, rather than just quantity, shows a mature approach to AI-assisted content generation, indicating the author's awareness of the current limitations and future possibilities.
Reference

This year, the challenge was not just 'completion' but also 'quality maintenance'.

policy#agent📝 BlogAnalyzed: Jan 11, 2026 18:36

IETF Digest: Early Insights into Authentication and Governance in the AI Agent Era

Published:Jan 11, 2026 14:11
1 min read
Qiita AI

Analysis

The article's focus on IETF discussions hints at the foundational importance of security and standardization in the evolving AI agent landscape. Analyzing these discussions is crucial for understanding how emerging authentication protocols and governance frameworks will shape the deployment and trust in AI-powered systems.
Reference

日刊IETFは、I-D AnnounceやIETF Announceに投稿されたメールをサマリーし続けるという修行的な活動です!! (This translates to: "Nikkan IETF is a practice of summarizing the emails posted to I-D Announce and IETF Announce!!")

product#llm📝 BlogAnalyzed: Jan 3, 2026 22:15

Beginner's Guide: Saving AI Tokens While Eliminating Bugs with Gemini 3 Pro

Published:Jan 3, 2026 22:15
1 min read
Qiita LLM

Analysis

The article focuses on practical token optimization strategies for debugging with Gemini 3 Pro, likely targeting novice developers. The use of analogies (Pokemon characters) might simplify concepts but could also detract from the technical depth for experienced users. The value lies in its potential to lower the barrier to entry for AI-assisted debugging.
Reference

カビゴン(Gemini 3 Pro)に「ひでんマシン」でコードを丸呑みさせて爆速デバッグする戦略

business#management📝 BlogAnalyzed: Jan 3, 2026 16:45

Effective AI Project Management: Lessons Learned

Published:Jan 3, 2026 16:25
1 min read
Qiita AI

Analysis

The article likely provides practical advice on managing AI projects, potentially focusing on common pitfalls and best practices for image analysis tasks. Its value depends on the depth of the insights and the applicability to different project scales and team structures. The Qiita platform suggests a focus on developer-centric advice.
Reference

最近MLを利用した画像解析系のAIプロジェクトを受け持つ機会が増えてきました。

business#llm📝 BlogAnalyzed: Jan 3, 2026 10:09

LLM Industry Predictions: 2025 Retrospective and 2026 Forecast

Published:Jan 3, 2026 09:51
1 min read
Qiita LLM

Analysis

This article provides a valuable retrospective on LLM industry predictions, offering insights into the accuracy of past forecasts. The shift towards prediction validation and iterative forecasting is crucial for navigating the rapidly evolving LLM landscape and informing strategic business decisions. The value lies in the analysis of prediction accuracy, not just the predictions themselves.

Key Takeaways

Reference

Last January, I posted "3 predictions for what will happen in the LLM (Large Language Model) industry in 2025," and thanks to you, many people viewed it.

Technology#Podcasts📝 BlogAnalyzed: Dec 29, 2025 01:43

Listen to Today's Qiita Trend Articles in a Podcast!

Published:Dec 29, 2025 00:50
1 min read
Qiita AI

Analysis

This article announces a daily podcast summarizing trending articles from Qiita, a Japanese platform for technical articles. The podcast is updated every morning at 7 AM, aiming to provide easily digestible information for listeners, particularly during commutes. The article humorously acknowledges that the original Qiita posts might not be timely for commutes. It encourages feedback and provides a link to the podcast. The source article is a post about taking the Fundamental Information Technology Engineer Examination after 30 years.
Reference

The article encourages feedback and provides a link to the podcast.

Research#llm📝 BlogAnalyzed: Dec 29, 2025 01:43

LLM Prompt to Summarize 'Why' Changes in GitHub PRs, Not 'What' Changed

Published:Dec 28, 2025 22:43
1 min read
Qiita LLM

Analysis

This article from Qiita LLM discusses the use of Large Language Models (LLMs) to summarize pull requests (PRs) on GitHub. The core problem addressed is the time spent reviewing PRs and documenting the reasons behind code changes, which remain bottlenecks despite the increased speed of code writing facilitated by tools like GitHub Copilot. The article proposes using LLMs to summarize the 'why' behind changes in a PR, rather than just the 'what', aiming to improve the efficiency of code review and documentation processes. This approach highlights a shift towards understanding the rationale behind code modifications.

Key Takeaways

Reference

GitHub Copilot and various AI tools have dramatically increased the speed of writing code. However, the time spent reading PRs written by others and documenting the reasons for your changes remains a bottleneck.

Technology#AI📝 BlogAnalyzed: Dec 28, 2025 22:31

Programming Notes: December 29, 2025

Published:Dec 28, 2025 21:45
1 min read
Qiita AI

Analysis

This article, sourced from Qiita AI, presents a collection of personally interesting topics from the internet, specifically focusing on AI. It positions 2025 as a "turbulent AI year" and aims to summarize the year from a developer's perspective, highlighting recent important articles. The author encourages readers to leave comments and feedback. The mention of a podcast version suggests the content is also available in audio format. The article seems to be a curated collection of AI-related news and insights, offering a developer-centric overview of the year's developments.

Key Takeaways

Reference

This article positions 2025 as a "turbulent AI year".

Analysis

This article is a personal memo on the topic of representation learning on graphs, covering methods and applications. It's a record of personal interests and is not guaranteed to be accurate or complete. The article's structure includes an introduction, notation and prerequisites, EmbeddingNodes, and extensions to multimodal graphs. The source is Qiita ML, suggesting it's a blog post or similar informal publication. The focus is on summarizing and organizing information related to the research paper, likely for personal reference.

Key Takeaways

Reference

This is a personal record, and does not guarantee the accuracy or completeness of the information.

Business#AI in IT📝 BlogAnalyzed: Dec 28, 2025 17:00

Why Information Systems Departments are Strong in the AI Era

Published:Dec 28, 2025 15:43
1 min read
Qiita AI

Analysis

This article from Qiita AI argues that despite claims of AI making system development accessible to everyone and rendering engineers obsolete, the reality observed from the perspective of information systems departments suggests a less disruptive change. It implies that the fundamental structure of IT and system management remains largely unchanged, even with the integration of AI tools. The article likely delves into the specific reasons why the expertise and responsibilities of information systems professionals remain crucial in the age of AI, potentially highlighting the need for integration, governance, and security oversight.
Reference

AIの話題になると、「誰でもシステムが作れる」「エンジニアはいらなくなる」といった主張を目にすることが増えた。

Research#llm📝 BlogAnalyzed: Dec 28, 2025 21:58

3 Walls Engineers Face in AI App Development and Prescriptions to Prevent PoC Failure

Published:Dec 28, 2025 13:56
1 min read
Qiita LLM

Analysis

This article from Qiita LLM discusses the challenges engineers face when developing AI applications. It highlights the gap between simply making an AI app "work" and making it "usable." The article likely delves into specific obstacles, such as data quality, model selection, and user experience design. It probably offers practical advice to avoid "PoC death," meaning the failure of a Proof of Concept project to move beyond the initial testing phase. The focus is on bridging the gap between basic functionality and practical, user-friendly AI applications.
Reference

"Hitting the ChatGPT API and displaying the response on the screen." This is something anyone can implement now, in a weekend hackathon or a few hours of personal development...

Research#llm📝 BlogAnalyzed: Dec 28, 2025 21:58

Asking ChatGPT about a Math Problem from Chubu University (2025): Minimizing Quadrilateral Area (Part 5/5)

Published:Dec 28, 2025 10:50
1 min read
Qiita ChatGPT

Analysis

This article excerpt from Qiita ChatGPT details a user's interaction with ChatGPT to solve a math problem related to minimizing the area of a quadrilateral, likely from a Chubu University exam. The structure suggests a multi-part exploration, with this being the fifth and final part. The user seems to be investigating which of 81 possible solution combinations (derived from different methods) ChatGPT's code utilizes. The article's brevity makes it difficult to assess the quality of the interaction or the effectiveness of ChatGPT's solution, but it highlights the use of AI for educational purposes and problem-solving.
Reference

The user asks ChatGPT: "Which combination of the 81 possibilities does the following code correspond to?"

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

Reference

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

Research#llm📝 BlogAnalyzed: Dec 28, 2025 11:31

A Very Rough Understanding of AI from the Perspective of a Code Writer

Published:Dec 28, 2025 10:42
1 min read
Qiita AI

Analysis

This article, originating from Qiita AI, presents a practical perspective on AI, specifically generative AI, from the viewpoint of a junior engineer. It highlights the common questions and uncertainties faced by developers who are increasingly using AI tools in their daily work. The author candidly admits to a lack of deep understanding regarding the fundamental concepts of AI, the distinction between machine learning and generative AI, and the required level of knowledge for effective utilization. This article likely aims to provide a simplified explanation or a starting point for other engineers in a similar situation, focusing on practical application rather than theoretical depth.
Reference

"I'm working as an engineer or coder in my second year of practical experience."

Tutorial#coding📝 BlogAnalyzed: Dec 28, 2025 10:31

Vibe Coding: A Summary of Coding Conventions for Beginner Developers

Published:Dec 28, 2025 09:24
1 min read
Qiita AI

Analysis

This Qiita article targets beginner developers and aims to provide a practical guide to "vibe coding," which seems to refer to intuitive or best-practice-driven coding. It addresses the common questions beginners have regarding best practices and coding considerations, especially in the context of security and data protection. The article likely compiles coding conventions and guidelines to help beginners avoid common pitfalls and implement secure coding practices. It's a valuable resource for those starting their coding journey and seeking to establish a solid foundation in coding standards and security awareness. The article's focus on practical application makes it particularly useful.
Reference

In the following article, I wrote about security (what people are aware of and what AI reads), but when beginners actually do vibe coding, they have questions such as "What is best practice?" and "How do I think about coding precautions?", and simply take measures against personal information and leakage...

Research#llm📝 BlogAnalyzed: Dec 29, 2025 01:43

Implementing GPT-2 from Scratch: Part 4

Published:Dec 28, 2025 06:23
1 min read
Qiita NLP

Analysis

This article from Qiita NLP focuses on implementing GPT-2, a language model developed by OpenAI in 2019. It builds upon a previous part that covered English-Japanese translation using Transformers. The article likely highlights the key differences between the Transformer architecture and GPT-2's implementation, providing a practical guide for readers interested in understanding and replicating the model. The focus on implementation suggests a hands-on approach, suitable for those looking to delve into the technical details of GPT-2.

Key Takeaways

Reference

GPT-2 is a language model announced by OpenAI in 2019.

Research#llm📝 BlogAnalyzed: Dec 27, 2025 23:31

Listen to Today's Trending Qiita Articles on Podcast! (December 28, 2025)

Published:Dec 27, 2025 23:27
1 min read
Qiita AI

Analysis

This article announces a daily AI-generated podcast summarizing the previous night's trending articles on Qiita, a Japanese programming Q&A site. It aims to provide a convenient way for users to stay updated on the latest trends while commuting. The podcast is updated every morning at 7 AM. The author also requests feedback from listeners. The provided link leads to an article titled "New AI Ban and the Answer to its Results." The service seems useful for busy developers who want to stay informed without having to read through numerous articles. The mention of the "New AI Ban" article suggests a focus on AI-related content within the trending topics.
Reference

"The latest trending articles from the previous night's AI podcast are updated every morning at 7 AM. Listen while commuting!"

Research#llm📝 BlogAnalyzed: Dec 27, 2025 23:00

The Relationship Between AI, MCP, and Unity - Why AI Cannot Directly Manipulate Unity

Published:Dec 27, 2025 22:30
1 min read
Qiita AI

Analysis

This article from Qiita AI explores the limitations of AI in directly manipulating the Unity game engine. It likely delves into the architectural reasons why AI, despite its advancements, requires an intermediary like MCP (presumably a message communication protocol or similar system) to interact with Unity. The article probably addresses the common misconception that AI can seamlessly handle any task, highlighting the specific challenges and solutions involved in integrating AI with complex software environments like game engines. The mention of a GitHub repository suggests a practical, hands-on approach to the topic, offering readers a concrete example of the architecture discussed.
Reference

"AI can do anything"

Software Development#Unity📝 BlogAnalyzed: Dec 27, 2025 23:00

What Happens When MCP Doesn't Work - AI Runaway and How to Deal With It

Published:Dec 27, 2025 22:30
1 min read
Qiita AI

Analysis

This article, originating from Qiita AI, announces the public release of a Unity MCP server. The author highlights that while the server covers basic Unity functionalities, unstable APIs have been excluded for the time being. The author actively encourages users to provide feedback and report issues via GitHub. The focus is on community-driven development and improvement of the MCP server. The article is more of an announcement and call for collaboration than a deep dive into the technical aspects of AI runaway scenarios implied by the title. The title is somewhat misleading given the content.
Reference

I have released the Unity MCP server I created!

Research#llm📝 BlogAnalyzed: Dec 27, 2025 17:01

Stopping LLM Hallucinations with "Physical Core Constraints": IDE / Nomological Ring Axioms

Published:Dec 27, 2025 16:32
1 min read
Qiita AI

Analysis

This article from Qiita AI explores a novel approach to mitigating LLM hallucinations by introducing "physical core constraints" through IDE (presumably referring to Integrated Development Environment) and Nomological Ring Axioms. The author emphasizes that the goal isn't to invalidate existing ML/GenAI theories or focus on benchmark performance, but rather to address the issue of LLMs providing answers even when they shouldn't. This suggests a focus on improving the reliability and trustworthiness of LLMs by preventing them from generating nonsensical or factually incorrect responses. The approach seems to be structural, aiming to make certain responses impossible. Further details on the specific implementation of these constraints would be necessary for a complete evaluation.
Reference

既存のLLMが「答えてはいけない状態でも答えてしまう」問題を、構造的に「不能(Fa...

Research#llm📝 BlogAnalyzed: Dec 27, 2025 16:32

Are You Really "Developing" with AI? Developer's Guide to Not Being Used by AI

Published:Dec 27, 2025 15:30
1 min read
Qiita AI

Analysis

This article from Qiita AI raises a crucial point about the over-reliance on AI in software development. While AI tools can assist in various stages like design, implementation, and testing, the author cautions against blindly trusting AI and losing critical thinking skills. The piece highlights the growing sentiment that AI can solve everything quickly, potentially leading developers to become mere executors of AI-generated code rather than active problem-solvers. It implicitly urges developers to maintain a balance between leveraging AI's capabilities and retaining their core development expertise and critical thinking abilities. The article serves as a timely reminder to ensure that AI remains a tool to augment, not replace, human ingenuity in the development process.
Reference

"AIに聞けば何でもできる」「AIに任せた方が速い" (Anything can be done by asking AI, it's faster to leave it to AI)

Research#llm📝 BlogAnalyzed: Dec 27, 2025 09:02

How to Approach AI

Published:Dec 27, 2025 06:53
1 min read
Qiita AI

Analysis

This article, originating from Qiita AI, discusses approaches to utilizing generative AI, particularly in the context of programming learning. The author aims to summarize existing perspectives on the topic. The initial excerpt suggests a consensus that AI is beneficial for programming education. The article promises to elaborate on this point with a bullet-point list, implying a structured and easily digestible format. While the provided content is brief, it sets the stage for a practical guide on leveraging AI in programming, potentially covering tools, techniques, and best practices. The value lies in its promise to synthesize diverse viewpoints into a coherent and actionable framework.
Reference

Previously, I often hesitated about how to utilize generative AI, but this time, I would like to briefly summarize the ideas that many people have talked about so far.

Research#llm📝 BlogAnalyzed: Dec 27, 2025 08:02

Thinking About AI Optimization

Published:Dec 27, 2025 06:24
1 min read
Qiita ChatGPT

Analysis

This article, sourced from Qiita ChatGPT, introduces the concept of Generative AI and references Nomura Research Institute's (NRI) definition. The provided excerpt is very short, making a comprehensive analysis difficult. However, it sets the stage for a discussion on AI optimization, likely focusing on Generative AI models. The article's value hinges on the depth and breadth of the subsequent content, which is not available in the provided snippet. It's a basic introduction, suitable for readers unfamiliar with the term Generative AI. The source being Qiita ChatGPT suggests a practical, potentially code-focused approach to the topic.
Reference

Generative AI (or Generative AI) is also called "Generative AI: Generative AI", and...

Analysis

This article announces the personal development of a web editor that streamlines slide creation using Markdown. The editor supports multiple frameworks like Marp and Reveal.js, offering users flexibility in their presentation styles. The focus on speed and ease of use suggests a tool aimed at developers and presenters who value efficiency. The article's appearance on Qiita AI indicates a target audience of technically inclined individuals interested in AI-related tools and development practices. The announcement highlights the growing trend of leveraging Markdown for various content creation tasks, extending its utility beyond simple text documents. The tool's support for multiple frameworks is a key selling point, catering to diverse user preferences and project requirements.
Reference

こんにちは、AIと個人開発をテーマに活動しているK(@kdevelopk)です。

Technology#AI📝 BlogAnalyzed: Dec 27, 2025 00:02

Listen to Today's Qiita Trending Articles in a Podcast! (December 27, 2025)

Published:Dec 26, 2025 23:26
1 min read
Qiita AI

Analysis

This article announces a daily AI-generated podcast summarizing the previous night's trending articles on Qiita, a Japanese programming Q&A site. It's updated every morning at 7 AM, targeting commuters who want to stay informed while on the go. The author acknowledges that Qiita posts might not be timely enough for the morning commute but encourages feedback. The provided link leads to a discussion about a "new AI ban" and its consequences, suggesting the podcast might cover controversial or thought-provoking topics within the AI community. The initiative aims to make technical content more accessible through audio, catering to a specific audience with limited time for reading.
Reference

"Updated every morning at 7 AM. Listen while commuting!"

Research#llm📝 BlogAnalyzed: Dec 26, 2025 16:26

AI Data Analysis - Data Preprocessing (37) - Encoding: Count / Frequency Encoding

Published:Dec 26, 2025 16:21
1 min read
Qiita AI

Analysis

This Qiita article discusses data preprocessing techniques for AI, specifically focusing on count and frequency encoding methods. It mentions using Python for implementation and leveraging Gemini for AI applications. The article seems to be part of a larger series on data preprocessing. While the title is informative, the provided content snippet is brief and lacks detail. A more comprehensive summary of the article's content, including the specific steps involved in count/frequency encoding and the benefits of using Gemini, would be beneficial. The article's practical application and target audience could also be clarified.
Reference

AIでデータ分析-データ前処理(37)-エン...

Research#llm📝 BlogAnalyzed: Dec 26, 2025 11:59

How to Use Chat AI "Correctly" for Learning ~With Prompt Examples~

Published:Dec 26, 2025 11:57
1 min read
Qiita ChatGPT

Analysis

This article, originating from Qiita, focuses on effectively utilizing chat AI like ChatGPT, Claude, and Gemini for learning purposes. It acknowledges the widespread adoption of these tools and emphasizes the importance of using them correctly. The article likely provides practical advice and prompt examples to guide users in maximizing the learning potential of chat AI. The promise of prompt examples is a key draw, suggesting actionable strategies rather than just theoretical discussion. The article caters to individuals already familiar with chat AI but seeking to refine their approach for educational gains. It's a practical guide for leveraging AI in self-directed learning.
Reference

Are you using chat AI (ChatGPT, Claude, Gemini, etc.) when learning new technologies?

Analysis

This article from Qiita Vision aims to compare the image recognition capabilities of Google's Gemini 3 Pro and its predecessor, Gemini 2.5 Pro. The focus is on evaluating the improvements in image recognition and OCR (Optical Character Recognition) performance. The article's methodology involves testing the models on five challenging problems to assess their accuracy and identify any significant advancements. The article's value lies in providing a practical, comparative analysis of the two models, which is useful for developers and researchers working with image-based AI applications.
Reference

The article mentions that Gemini 3 models are said to have improved agent workflows, autonomous coding, and complex multimodal performance.

Research#llm📝 BlogAnalyzed: Dec 29, 2025 01:43

Thorough Comparison of Image Recognition Capabilities: Gemini 3 Flash vs. Gemini 2.5 Flash!

Published:Dec 26, 2025 01:42
1 min read
Qiita Vision

Analysis

This article from Qiita Vision announces the arrival of Gemini 3 Flash, a new model in the Flash series. The article highlights the model's balance of high inference capabilities with speed and cost-effectiveness. The comparison with Gemini 2.5 Flash suggests an evaluation of improvements in image recognition. The focus on the Flash series implies a strategic emphasis on models optimized for rapid processing and efficient resource utilization, likely targeting applications where speed and cost are critical factors. The article's structure suggests a detailed analysis of the new model's performance.

Key Takeaways

Reference

The article mentions the announcement of Gemini 3 Flash on December 17, 2025 (US time).

Research#llm📝 BlogAnalyzed: Dec 25, 2025 22:29

Cultivating AI with the Compound Interest of Thought

Published:Dec 25, 2025 22:26
1 min read
Qiita AI

Analysis

This article, seemingly a blog post from Qiita AI, discusses the author's motivation for actively participating in an Advent Calendar event. The author, "Zazen Inu," mentions two reasons, one of which is the timing of the event immediately after the completion of the Manabi DX Quest 2025. While the provided excerpt is brief, it suggests a focus on continuous learning and development within the AI field. The title implies a long-term, compounding effect of thoughtful effort in AI development, which is an interesting concept. More context is needed to fully understand the author's specific arguments and insights.
Reference

おはようございます、座禅いぬです。

Research#llm📝 BlogAnalyzed: Dec 25, 2025 15:01

Analyzing 25 Advent Calendar Articles with AI

Published:Dec 25, 2025 14:58
1 min read
Qiita AI

Analysis

This article discusses the author's experience of writing 25 articles for an Advent Calendar on Qiita, motivated by the desire to win a Qiitan plush toy. The author credits AI tools for helping them complete the challenge, especially since they joined the Advent Calendar partway through. The article itself is the 26th, a reflection on the process. While brief, it hints at the potential of AI in assisting content creation and highlights the gamified aspect of participating in online communities like Qiita. It would be interesting to see a more detailed breakdown of how the AI tools were used and their specific impact on the writing process.
Reference

今年は初めてアドベントカレンダーに参加し、Qiitanぬいぐるみ欲しさに25記事完走しました!

Research#llm📝 BlogAnalyzed: Dec 25, 2025 13:44

Can Prompt Injection Prevent Unauthorized Generation and Other Harassment?

Published:Dec 25, 2025 13:39
1 min read
Qiita ChatGPT

Analysis

This article from Qiita ChatGPT discusses the use of prompt injection to prevent unintended generation and harassment. The author notes the rapid advancement of AI technology and the challenges of keeping up with its development. The core question revolves around whether prompt injection techniques can effectively safeguard against malicious use cases, such as unauthorized content generation or other forms of AI-driven harassment. The article likely explores different prompt injection strategies and their effectiveness in mitigating these risks. Understanding the limitations and potential of prompt injection is crucial for developing robust and secure AI systems.
Reference

Recently, the evolution of AI technology is really fast.

Analysis

This article from Qiita AI discusses Snowflake's shift from a "DATA CLOUD" theme to an "AI DATA CLOUD" theme, highlighting the integration of Large Language Models (LLMs) into their products. It likely details the advancements and new features related to AI and applications within the Snowflake ecosystem over the past two years. The article probably covers the impact of these changes on data management, analytics, and application development within the Snowflake platform, potentially focusing on the innovations presented at the Snowflake Summit 2024.
Reference

At the Snowflake Summit in June 2024, the DATA CLOUD theme, which had previously been advocated, was changed to AI DATA CLOUD as the direction of the product, which had already achieved many innovative LLM adaptations.

Analysis

This article from Qiita AI explores the use of AI for improving audio quality. Written from the perspective of a young engineer, it delves into the mechanisms and practical experiences of using "sound quality improvement AI." The article likely covers various tools and techniques, offering insights into how AI can enhance audio beyond simple generation. It's valuable for engineers and enthusiasts interested in the intersection of AI and audio processing, providing a hands-on perspective on the capabilities and limitations of current technologies. The focus on practical usage makes it more appealing to those looking for actionable information rather than purely theoretical discussions.
Reference

最近は、AIを活用して音声生成だけでなく音質向上も同時に行えるツールが増えてきました。(Recently, there has been an increase in tools that utilize AI to improve sound quality as well as generate audio.)

Research#llm📝 BlogAnalyzed: Dec 25, 2025 08:49

Why AI Coding Sometimes Breaks Code

Published:Dec 25, 2025 08:46
1 min read
Qiita AI

Analysis

This article from Qiita AI addresses a common frustration among developers using AI code generation tools: the introduction of bugs, altered functionality, and broken code. It suggests that these issues aren't necessarily due to flaws in the AI model itself, but rather stem from other factors. The article likely delves into the nuances of how AI interprets context, handles edge cases, and integrates with existing codebases. Understanding these limitations is crucial for effectively leveraging AI in coding and mitigating potential problems. It highlights the importance of careful review and testing of AI-generated code.
Reference

"動いていたコードが壊れた"

Research#llm📝 BlogAnalyzed: Dec 25, 2025 08:22

Frankly, the Era of Humans Reading Technical Articles is Over. Yet, I Still Write Articles.

Published:Dec 25, 2025 08:18
1 min read
Qiita AI

Analysis

This article from Qiita AI discusses the changing landscape of technical information consumption. With the rise of AI, the author questions the relevance of traditional technical articles. The core argument revolves around the efficiency of AI in providing solutions and explanations compared to searching and reading through articles. The author acknowledges that AI can quickly summarize and explain complex topics, making it a preferred method for many. However, the article implies that there's still value in human-authored content, though the specific reasons are not fully elaborated in this excerpt. The article prompts reflection on the future role of technical writers in an AI-driven world.
Reference

AI can read and explain technical articles in an easy-to-understand way.

Research#llm📝 BlogAnalyzed: Dec 25, 2025 08:19

Summary of Security Concerns in the Generative AI Era for Software Development

Published:Dec 25, 2025 07:19
1 min read
Qiita LLM

Analysis

This article, likely a blog post, discusses security concerns related to using generative AI in software development. Given the source (Qiita LLM), it's probably aimed at developers and engineers. The provided excerpt mentions BrainPad Inc. and their mission related to data utilization. The article likely delves into the operational maintenance of products developed and provided by the company, focusing on the security implications of integrating generative AI tools into the software development lifecycle. A full analysis would require the complete article to understand the specific security risks and mitigation strategies discussed.
Reference

We are promoting the "daily use of data utilization" for companies through data analysis support and the provision of SaaS products.

Analysis

This article from Qiita DL introduces TensorRT as a solution to the problem of slow deep learning inference speeds in production environments. It targets beginners, aiming to explain what TensorRT is and how it can be used to optimize deep learning models for faster performance. The article likely covers the basics of TensorRT, its benefits, and potentially some simple examples or use cases. The focus is on making the technology accessible to those who are new to the field of deep learning deployment and optimization. It's a practical guide for developers looking to improve the efficiency of their deep learning applications.
Reference

Have you ever had the experience of creating a highly accurate deep learning model, only to find it "heavy... slow..." when actually running it in a service?

Research#llm📝 BlogAnalyzed: Dec 25, 2025 04:10

The Future of AI Debugging with Cursor Bugbot: Latest Trends in 2025

Published:Dec 25, 2025 04:07
1 min read
Qiita AI

Analysis

This article from Qiita AI discusses the potential impact of Cursor Bugbot on the future of AI debugging, focusing on trends expected by 2025. It likely explores how Bugbot differs from traditional debugging methods and highlights key features related to logical errors, security vulnerabilities, and performance bottlenecks. The article's structure, indicated by the table of contents, suggests a comprehensive overview, starting with an introduction to the new era of AI debugging and then delving into the specifics of Bugbot's functionalities. It aims to inform readers about the advancements in AI-assisted debugging tools and their implications for software development.
Reference

AI Debugging: A New Era

Research#llm📝 BlogAnalyzed: Dec 25, 2025 02:43

Are Personas Really Necessary in System Prompts?

Published:Dec 25, 2025 02:41
1 min read
Qiita AI

Analysis

This article from Qiita AI questions the increasingly common practice of including personas in system prompts for generative AI. It suggests that while defining a persona (e.g., "You are an excellent engineer") might seem beneficial, it can lead to a black box effect, making it difficult to understand why the AI generates specific outputs. The article likely explores alternative design approaches that avoid relying heavily on personas, potentially focusing on more direct and transparent instructions to achieve desired results. The core argument seems to be about balancing control and understanding in AI prompt engineering.
Reference

"Are personas really necessary in system prompts? ~ Designs that lead to black boxes and their alternatives ~"