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research#llm📝 BlogAnalyzed: Jan 19, 2026 03:30

Pair Programming with ChatGPT: A Promising Leap Forward!

Published:Jan 19, 2026 03:20
1 min read
Qiita ChatGPT

Analysis

Exploring the potential of pairing with AI like ChatGPT for coding is an exciting frontier! This approach could revolutionize how developers learn and solve complex problems, opening up new avenues for creative problem-solving.
Reference

This is a rapidly evolving field, showcasing the power of human-AI collaboration.

product#agent📝 BlogAnalyzed: Jan 19, 2026 02:15

Winning AI Secrets Unveiled: Dive into the 'everything-claude-code' Repository!

Published:Jan 19, 2026 00:22
1 min read
Zenn Claude

Analysis

Get ready to explore the cutting-edge! This article highlights the secrets behind an Anthropic x Forum Ventures hackathon winner's codebase, 'everything-claude-code,' used in a real-world product. It's a goldmine of practical insights gained from over 10 months of hands-on development, showcasing innovative techniques in action!
Reference

This repository showcases the winning strategies and code used in the Anthropic hackathon.

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#llm📝 BlogAnalyzed: Jan 18, 2026 11:15

ChatGPT Powers Up Horse Racing AI: A Beginner's Guide!

Published:Jan 18, 2026 11:13
1 min read
Qiita AI

Analysis

This project is a fantastic demonstration of how accessible AI development has become! Using ChatGPT as a guide, beginners are building their own horse racing prediction AI. It's a great example of democratizing AI and promoting hands-on learning.

Key Takeaways

Reference

This article discusses the 14th installment of a project where a programming beginner uses ChatGPT to create a horse racing prediction AI.

research#backpropagation📝 BlogAnalyzed: Jan 18, 2026 08:00

Deep Dive into Backpropagation: A Student's Journey with Gemini

Published:Jan 18, 2026 07:57
1 min read
Qiita DL

Analysis

This article beautifully captures the essence of learning deep learning, leveraging the power of Gemini for interactive exploration. The author's journey, guided by a reputable textbook, offers a glimpse into how AI tools can enhance the learning process. It's an inspiring example of hands-on learning in action!
Reference

The article is based on conversations with Gemini.

product#image generation📝 BlogAnalyzed: Jan 18, 2026 08:45

Unleash Your Inner Artist: AI-Powered Character Illustrations Made Easy!

Published:Jan 18, 2026 06:51
1 min read
Zenn AI

Analysis

This article highlights an incredibly accessible way to create stunning character illustrations using Google Gemini's image generation capabilities! It's a fantastic solution for bloggers and content creators who want visually engaging content without the cost or skill barriers of traditional methods. The author's personal experience adds a great layer of authenticity and practical application.
Reference

The article showcases how to use Google Gemini's 'Nano Banana Pro' to create illustrations, making the process accessible for everyone.

research#llm📝 BlogAnalyzed: Jan 18, 2026 14:00

Unlocking AI's Creative Power: Exploring LLMs and Diffusion Models

Published:Jan 18, 2026 04:15
1 min read
Zenn ML

Analysis

This article dives into the exciting world of generative AI, focusing on the core technologies driving innovation: Large Language Models (LLMs) and Diffusion Models. It promises a hands-on exploration of these powerful tools, providing a solid foundation for understanding the math and experiencing them with Python, opening doors to creating innovative AI solutions.
Reference

LLM is 'AI that generates and explores text,' and the diffusion model is 'AI that generates images and data.'

research#agent📝 BlogAnalyzed: Jan 18, 2026 02:00

Deep Dive into Contextual Bandits: A Practical Approach

Published:Jan 18, 2026 01:56
1 min read
Qiita ML

Analysis

This article offers a fantastic introduction to contextual bandit algorithms, focusing on practical implementation rather than just theory! It explores LinUCB and other hands-on techniques, making it a valuable resource for anyone looking to optimize web applications using machine learning.
Reference

The article aims to deepen understanding by implementing algorithms not directly included in the referenced book.

product#agent📝 BlogAnalyzed: Jan 17, 2026 11:15

AI-Powered Web Apps: Diving into the Code with Excitement!

Published:Jan 17, 2026 11:11
1 min read
Qiita AI

Analysis

The ability to generate web applications with AI, like 'Vibe Coding,' is transforming development! The author's hands-on experience, having built multiple apps with over 100,000 lines of AI-generated code, highlights the power and speed of this new approach. It's a thrilling glimpse into the future of coding!
Reference

I've created Web apps more than 6 times, and I've had the AI write a total of 100,000 lines of code, but the answer is No when asked if I have read all the code.

research#llm📝 BlogAnalyzed: Jan 17, 2026 07:01

Local Llama Love: Unleashing AI Power on Your Hardware!

Published:Jan 17, 2026 05:44
1 min read
r/LocalLLaMA

Analysis

The local LLaMA community is buzzing with excitement, offering a hands-on approach to experiencing powerful language models. This grassroots movement democratizes access to cutting-edge AI, letting enthusiasts experiment and innovate with their own hardware setups. The energy and enthusiasm of the community are truly infectious!
Reference

Enthusiasts are sharing their configurations and experiences, fostering a collaborative environment for AI exploration.

business#ai coding📝 BlogAnalyzed: Jan 16, 2026 16:17

Ruby on Rails Creator's Perspective on AI Coding: A Human-First Approach

Published:Jan 16, 2026 16:06
1 min read
Slashdot

Analysis

David Heinemeier Hansson, the visionary behind Ruby on Rails, offers a fascinating glimpse into his coding philosophy. His approach at 37 Signals prioritizes human-written code, revealing a unique perspective on integrating AI in product development and highlighting the enduring value of human expertise.
Reference

"I'm not feeling that we're falling behind at 37 Signals in terms of our ability to produce, in terms of our ability to launch things or improve the products,"

research#visualization📝 BlogAnalyzed: Jan 16, 2026 10:32

Stunning 3D Solar Forecasting Visualizer Built with AI Assistance!

Published:Jan 16, 2026 10:20
1 min read
r/deeplearning

Analysis

This project showcases an amazing blend of AI and visualization! The creator used Claude 4.5 to generate WebGL code, resulting in a dynamic 3D simulation of a 1D-CNN processing time-series data. This kind of hands-on, visual approach makes complex concepts wonderfully accessible.
Reference

I built this 3D sim to visualize how a 1D-CNN processes time-series data (the yellow box is the kernel sliding across time).

product#agent📝 BlogAnalyzed: Jan 16, 2026 03:00

Can Free AI Agent Genspark Revolutionize System Development?

Published:Jan 16, 2026 02:50
1 min read
Qiita AI

Analysis

This article explores the exciting potential of Genspark Super Agent for free system development! The investigation dives into how this versatile AI agent could democratize the creation of software, making it accessible to a wider audience.
Reference

The article's introduction sets the stage for a hands-on examination of Genspark's capabilities.

research#llm📝 BlogAnalyzed: Jan 16, 2026 01:15

Building LLMs from Scratch: A Deep Dive into Modern Transformer Architectures!

Published:Jan 16, 2026 01:00
1 min read
Zenn DL

Analysis

Get ready to dive into the exciting world of building your own Large Language Models! This article unveils the secrets of modern Transformer architectures, focusing on techniques used in cutting-edge models like Llama 3 and Mistral. Learn how to implement key components like RMSNorm, RoPE, and SwiGLU for enhanced performance!
Reference

This article dives into the implementation of modern Transformer architectures, going beyond the original Transformer (2017) to explore techniques used in state-of-the-art models.

product#agent📰 NewsAnalyzed: Jan 15, 2026 17:45

Anthropic's Claude Cowork: A Hands-On Look at a Practical AI Agent

Published:Jan 15, 2026 17:40
1 min read
WIRED

Analysis

The article's focus on user-friendliness suggests a deliberate move toward broader accessibility for AI tools, potentially democratizing access to powerful features. However, the limited scope to file management and basic computing tasks highlights the current limitations of AI agents, which still require refinement to handle more complex, real-world scenarios. The success of Claude Cowork will depend on its ability to evolve beyond these initial capabilities.
Reference

Cowork is a user-friendly version of Anthropic's Claude Code AI-powered tool that's built for file management and basic computing tasks.

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.

research#llm🏛️ OfficialAnalyzed: Jan 16, 2026 01:15

Demystifying RAG: A Hands-On Guide with Practical Code

Published:Jan 15, 2026 10:17
1 min read
Zenn OpenAI

Analysis

This article offers a fantastic opportunity to dive into the world of RAG (Retrieval-Augmented Generation) with a practical, code-driven approach. By implementing a simple RAG system on Google Colab, readers gain hands-on experience and a deeper understanding of how these powerful LLM-powered applications work.
Reference

This article explains the basic mechanisms of RAG using sample code.

product#llm📝 BlogAnalyzed: Jan 15, 2026 08:30

Connecting Snowflake's Managed MCP Server to Claude and ChatGPT: A Technical Exploration

Published:Jan 15, 2026 07:10
1 min read
Zenn AI

Analysis

This article provides a practical, hands-on exploration of integrating Snowflake's Managed MCP Server with popular LLMs. The focus on OAuth connections and testing with Claude and ChatGPT is valuable for developers and data scientists looking to leverage the power of Snowflake within their AI workflows. Further analysis could explore performance metrics and cost implications of the integration.
Reference

The author, while affiliated with Snowflake, emphasizes that this article reflects their personal views and not the official stance of the organization.

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#agent📝 BlogAnalyzed: Jan 15, 2026 04:30

Building Your Own MCP Server: A Deep Dive into AI Agent Interoperability

Published:Jan 15, 2026 04:24
1 min read
Qiita AI

Analysis

The article's premise of creating an MCP server to understand its mechanics is a practical and valuable learning approach. While the provided text is sparse, the subject matter directly addresses the critical need for interoperability within the rapidly expanding AI agent ecosystem. Further elaboration on implementation details and challenges would significantly increase its educational impact.
Reference

Claude Desktop and other AI agents use MCP (Model Context Protocol) to connect with external services.

product#llm📝 BlogAnalyzed: Jan 13, 2026 14:00

Hands-on with Claude Code: A First Look at Anthropic's Coding Assistant

Published:Jan 13, 2026 13:46
1 min read
Qiita AI

Analysis

This article provides a practical, entry-level exploration of Claude Code. It offers valuable insights for users considering Anthropic's coding assistant by focusing on the initial steps of plan selection and environment setup. Further analysis should compare Claude Code's capabilities to competitors and delve into its practical application in real-world coding scenarios.
Reference

However, this time, I finally decided to subscribe and try it out!

research#llm📝 BlogAnalyzed: Jan 13, 2026 19:30

Deep Dive into LLMs: A Programmer's Guide from NumPy to Cutting-Edge Architectures

Published:Jan 13, 2026 12:53
1 min read
Zenn LLM

Analysis

This guide provides a valuable resource for programmers seeking a hands-on understanding of LLM implementation. By focusing on practical code examples and Jupyter notebooks, it bridges the gap between high-level usage and the underlying technical details, empowering developers to customize and optimize LLMs effectively. The inclusion of topics like quantization and multi-modal integration showcases a forward-thinking approach to LLM development.
Reference

This series dissects the inner workings of LLMs, from full scratch implementations with Python and NumPy, to cutting-edge techniques used in Qwen-32B class models.

research#feature engineering📝 BlogAnalyzed: Jan 12, 2026 16:45

Lag Feature Engineering: A Practical Guide for Data Preprocessing in AI

Published:Jan 12, 2026 16:44
1 min read
Qiita AI

Analysis

This article provides a concise overview of lag feature creation, a crucial step in time series data preprocessing for AI. While the description is brief, mentioning the use of Gemini suggests an accessible, hands-on approach leveraging AI for code generation or understanding, which can be beneficial for those learning feature engineering techniques.
Reference

The article mentions using Gemini for implementation.

infrastructure#llm📝 BlogAnalyzed: Jan 12, 2026 19:15

Running Japanese LLMs on a Shoestring: Practical Guide for 2GB VPS

Published:Jan 12, 2026 16:00
1 min read
Zenn LLM

Analysis

This article provides a pragmatic, hands-on approach to deploying Japanese LLMs on resource-constrained VPS environments. The emphasis on model selection (1B parameter models), quantization (Q4), and careful configuration of llama.cpp offers a valuable starting point for developers looking to experiment with LLMs on limited hardware and cloud resources. Further analysis on latency and inference speed benchmarks would strengthen the practical value.
Reference

The key is (1) 1B-class GGUF, (2) quantization (Q4 focused), (3) not increasing the KV cache too much, and configuring llama.cpp (=llama-server) tightly.

product#llm📝 BlogAnalyzed: Jan 12, 2026 08:15

Beyond Benchmarks: A Practitioner's Experience with GLM-4.7

Published:Jan 12, 2026 08:12
1 min read
Qiita AI

Analysis

This article highlights the limitations of relying solely on benchmarks for evaluating AI models like GLM-4.7, emphasizing the importance of real-world application and user experience. The author's hands-on approach of utilizing the model for coding, documentation, and debugging provides valuable insights into its practical capabilities, supplementing theoretical performance metrics.
Reference

I am very much a 'hands-on' AI user. I use AI in my daily work for code, docs creation, and debug.

research#llm📝 BlogAnalyzed: Jan 10, 2026 20:00

Lightweight LLM Finetuning for Humorous Responses via Multi-LoRA

Published:Jan 10, 2026 18:50
1 min read
Zenn LLM

Analysis

This article details a practical, hands-on approach to finetuning a lightweight LLM for generating humorous responses using LoRA, potentially offering insights into efficient personalization of LLMs. The focus on local execution and specific output formatting adds practical value, but the novelty is limited by the specific, niche application to a pre-defined persona.

Key Takeaways

Reference

突然、LoRAをうまいこと使いながら、ゴ〇ジャス☆さんのような返答をしてくる化け物(いい意味で)を作ろうと思いました。

research#geospatial📝 BlogAnalyzed: Jan 10, 2026 08:00

Interactive Geospatial Data Visualization with Python and Kaggle

Published:Jan 10, 2026 03:31
1 min read
Zenn AI

Analysis

This article series provides a practical introduction to geospatial data analysis using Python on Kaggle, focusing on interactive mapping techniques. The emphasis on hands-on examples and clear explanations of libraries like GeoPandas makes it valuable for beginners. However, the abstract is somewhat sparse and could benefit from a more detailed summary of the specific interactive mapping approaches covered.
Reference

インタラクティブなヒートマップ、コロプレスマ...

Analysis

This article provides a hands-on exploration of key LLM output parameters, focusing on their impact on text generation variability. By using a minimal experimental setup without relying on external APIs, it offers a practical understanding of these parameters for developers. The limitation of not assessing model quality is a reasonable constraint given the article's defined scope.
Reference

本記事のコードは、Temperature / Top-p / Top-k の挙動差を API なしで体感する最小実験です。

research#llm📝 BlogAnalyzed: Jan 6, 2026 07:17

Validating Mathematical Reasoning in LLMs: Practical Techniques for Accuracy Improvement

Published:Jan 6, 2026 01:38
1 min read
Qiita LLM

Analysis

The article likely discusses practical methods for verifying the mathematical reasoning capabilities of LLMs, a crucial area given their increasing deployment in complex problem-solving. Focusing on techniques employed by machine learning engineers suggests a hands-on, implementation-oriented approach. The effectiveness of these methods in improving accuracy will be a key factor in their adoption.
Reference

「本当に正確に論理的な推論ができているのか?」

product#preprocessing📝 BlogAnalyzed: Jan 4, 2026 15:24

Equal-Frequency Binning for Data Preprocessing in AI: A Practical Guide

Published:Jan 4, 2026 15:01
1 min read
Qiita AI

Analysis

This article likely provides a practical guide to equal-frequency binning, a common data preprocessing technique. The use of Gemini AI suggests an integration of AI tools for data analysis, potentially automating or enhancing the binning process. The value lies in its hands-on approach and potential for improving data quality for AI models.
Reference

今回はデータの前処理でよ...

product#chatbot🏛️ OfficialAnalyzed: Jan 3, 2026 17:25

Dify Chatbot Creation Part 2: Hybrid Search Implementation

Published:Jan 3, 2026 17:14
1 min read
Qiita OpenAI

Analysis

This article appears to be part of a series documenting the author's experience with Dify, focusing on hybrid search implementation for chatbot creation. The value lies in its practical, hands-on approach, potentially offering insights for developers exploring Dify's capabilities for building AI-powered conversational interfaces. However, without the full article content, it's difficult to assess the depth of the technical analysis or the novelty of the hybrid search implementation.

Key Takeaways

Reference

Following up from the previous time, this is a generative AI related topic.

Technology#AI Development📝 BlogAnalyzed: Jan 3, 2026 18:03

From "Using AI" to "Developing with AI"

Published:Jan 3, 2026 14:08
1 min read
Zenn ChatGPT

Analysis

The article highlights a shift in perspective from simply using AI tools to actively collaborating with them in the development process. It suggests a more hands-on approach, particularly for beginners, moving away from relying solely on AI and instead working alongside it. The author, a novice engineer, shares their experience and the positive outcomes of this change in approach, focusing on personal development and practical application.

Key Takeaways

Reference

The author mentions using ChatGPT, Claude, and Cursor extensively in personal mobile app development.

Research#llm📝 BlogAnalyzed: Jan 3, 2026 18:01

The Fun of Machine Learning Lies in Trial and Error, More Than the Models

Published:Jan 3, 2026 12:37
1 min read
Zenn AI

Analysis

The article highlights the author's shift in perspective on machine learning, emphasizing the hands-on experience and experimentation as the key to engagement, rather than solely focusing on the models themselves. It mentions a specific book and Kaggle as tools for learning.
Reference

The author's experience with a specific book and Kaggle.

product#llm📝 BlogAnalyzed: Jan 3, 2026 12:27

Exploring Local LLM Programming with Ollama: A Hands-On Review

Published:Jan 3, 2026 12:05
1 min read
Qiita LLM

Analysis

This article provides a practical, albeit brief, overview of setting up a local LLM programming environment using Ollama. While it lacks in-depth technical analysis, it offers a relatable experience for developers interested in experimenting with local LLMs. The value lies in its accessibility for beginners rather than advanced insights.

Key Takeaways

Reference

LLMのアシストなしでのプログラミングはちょっと考えられなくなりましたね。

Technology#Generative AI🏛️ OfficialAnalyzed: Jan 3, 2026 06:14

Deploying Dify and Provider Registration

Published:Jan 2, 2026 16:08
1 min read
Qiita OpenAI

Analysis

The article is a follow-up to a previous one, detailing the author's experiments with generative AI. This installment focuses on deploying Dify and registering providers, likely as part of a larger project or exploration of AI tools. The structure suggests a practical, step-by-step approach to using these technologies.
Reference

The article is the second in a series, following an initial article on setting up the environment and initial testing.

Career Advice#AI Engineering📝 BlogAnalyzed: Jan 3, 2026 06:59

AI Engineer Path Inquiry

Published:Jan 2, 2026 11:42
1 min read
r/learnmachinelearning

Analysis

The article presents a student's questions about transitioning into an AI Engineer role. The student, nearing graduation with a CS degree, seeks practical advice on bridging the gap between theoretical knowledge and real-world application. The core concerns revolve around the distinction between AI Engineering and Machine Learning, the practical tasks of an AI Engineer, the role of web development, and strategies for gaining hands-on experience. The request for free bootcamps indicates a desire for accessible learning resources.
Reference

The student asks: 'What is the real difference between AI Engineering and Machine Learning? What does an AI Engineer actually do in practice? Is integrating ML/LLMs into web apps considered AI engineering? Should I continue web development alongside AI, or switch fully? How can I move from theory to real-world AI projects in my final year?'

Research#llm🏛️ OfficialAnalyzed: Jan 3, 2026 09:17

OpenAI Grove Cohort 2 Announced

Published:Jan 2, 2026 10:00
1 min read
OpenAI News

Analysis

This is a straightforward announcement of a founder program by OpenAI. It highlights key benefits like funding, access to tools, and mentorship, targeting individuals at various stages of startup development.

Key Takeaways

Reference

Participants receive $50K in API credits, early access to AI tools, and hands-on mentorship from the OpenAI team.

Ethics in NLP Education: A Hands-on Approach

Published:Dec 31, 2025 12:26
1 min read
ArXiv

Analysis

This paper addresses the crucial need to integrate ethical considerations into NLP education. It highlights the challenges of keeping curricula up-to-date and fostering critical thinking. The authors' focus on active learning, hands-on activities, and 'learning by teaching' is a valuable contribution, offering a practical model for educators. The longevity and adaptability of the course across different settings further strengthens its significance.
Reference

The paper introduces a course on Ethical Aspects in NLP and its pedagogical approach, grounded in active learning through interactive sessions, hands-on activities, and "learning by teaching" methods.

Research#llm🏛️ OfficialAnalyzed: Jan 3, 2026 05:49

Build an AI-powered website assistant with Amazon Bedrock

Published:Dec 29, 2025 16:42
1 min read
AWS ML

Analysis

The article introduces a practical application of Amazon Bedrock, focusing on building an AI-powered website assistant. It highlights the use of Amazon Bedrock and Knowledge Bases, suggesting a hands-on approach to solving a specific challenge. The focus is on implementation and practical use of the technology.
Reference

This post demonstrates how to solve this challenge by building an AI-powered website assistant using Amazon Bedrock and Amazon Bedrock Knowledge Bases.

Research#llm📝 BlogAnalyzed: Dec 29, 2025 09:02

What skills did you learn on the job this past year?

Published:Dec 29, 2025 05:44
1 min read
r/datascience

Analysis

This Reddit post from r/datascience highlights a growing concern in the data science field: the decline of on-the-job training and the increasing reliance on employees to self-learn. The author questions whether companies are genuinely investing in their employees' skill development or simply providing access to online resources and expecting individuals to take full responsibility for their career growth. This trend could lead to a skills gap within organizations and potentially hinder innovation. The post seeks to gather anecdotal evidence from data scientists about their recent learning experiences at work, specifically focusing on skills acquired through hands-on training or challenging assignments, rather than self-study. The discussion aims to shed light on the current state of employee development in the data science industry.
Reference

"you own your career" narratives or treating a Udemy subscription as equivalent to employee training.

Research#llm📝 BlogAnalyzed: Dec 28, 2025 18:02

Software Development Becomes "Boring" with Claude Code: A Developer's Perspective

Published:Dec 28, 2025 16:24
1 min read
r/ClaudeAI

Analysis

This article, sourced from a Reddit post, highlights a significant shift in the software development experience due to AI tools like Claude Code. The author expresses a sense of diminished fulfillment as AI automates much of the debugging and problem-solving process, traditionally considered challenging but rewarding. While productivity has increased dramatically, the author misses the intellectual stimulation and satisfaction derived from overcoming coding hurdles. This raises questions about the evolving role of developers, potentially shifting from hands-on coding to prompt engineering and code review. The post sparks a discussion about whether the perceived "suffering" in traditional coding was actually a crucial element of the job's appeal and whether this new paradigm will ultimately lead to developer dissatisfaction despite increased efficiency.
Reference

"The struggle was the fun part. Figuring it out. That moment when it finally works after 4 hours of pain."

Analysis

This article discusses using AI, specifically classification models, to handle missing data during the data preprocessing stage of AI-driven data analysis. It's the second part of a series focusing on data preprocessing. The article likely covers the methodology of using classification models to predict and impute missing values, potentially comparing it to other imputation techniques. The mention of Gemini suggests the use of Google's AI model for some aspect of the process, possibly for generating code or assisting in the analysis. The inclusion of Python implementation indicates a practical, hands-on approach to the topic. The article's structure includes an introduction to the data used, the Python implementation, the use of Gemini, and a summary.
Reference

AIでデータ分析-データ前処理(22)②-欠損処理:分類モデルによる欠損補完

Analysis

This article is a personal memo detailing the author's difficulties with Chapter 7 of the book "Practical Introduction to AI Agents for On-site Utilization." The chapter focuses on using AI agents to assist with marketing. The article likely delves into specific challenges encountered while trying to implement the concepts and techniques described in the chapter. Without the full content, it's difficult to assess the specific issues, but it seems to be a practical, hands-on account of someone learning to apply AI in a real-world marketing context. It's part of a series of notes covering different chapters of the book.

Key Takeaways

Reference

"This chapter helps with marketing..."

Research#llm📝 BlogAnalyzed: Dec 28, 2025 08:31

Recreating Palantir's "Ontology" with Python

Published:Dec 28, 2025 08:20
1 min read
Qiita LLM

Analysis

This article discusses the implementation of an ontology, similar to Palantir Foundry's, using Python. It addresses the practical application of the ontological concepts previously discussed, moving beyond theoretical understanding to actual implementation. The article likely provides code examples and demonstrates the output of such an implementation. The value lies in bridging the gap between understanding the concept of an ontology and knowing how to build one in a practical setting. It caters to readers who are interested in the hands-on aspects of AI data infrastructure and want to explore how to leverage Python for building ontologies.
Reference

「概念はわかった。で、どう実装して、どんなアウトプットになるの?」

Research#AI Data Infrastructure📝 BlogAnalyzed: Dec 28, 2025 21:57

Recreating Palantir's "Ontology" in Python

Published:Dec 28, 2025 08:09
1 min read
Zenn LLM

Analysis

The article describes an attempt to replicate Palantir's Foundry-like "Supply Chain Control Tower" using Python. The author aims to demonstrate the practical implementation of an ontology, building upon a previous article explaining its importance in AI data infrastructure. The project focuses on the workflow of "viewing data -> AI understanding context -> decision-making and action." This suggests a hands-on approach to understanding and experimenting with ontology concepts, potentially for data analysis and decision support. The article likely provides code and explanations to guide readers through the implementation.
Reference

The article aims to create a minimal version of a "Supply Chain Control Tower" like Palantir Foundry.

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 23:00

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"

Analysis

This article discusses using AI, specifically regression models, to handle missing values in data preprocessing for AI data analysis. It mentions using Python for implementation and Gemini for AI utilization. The article likely provides a practical guide on how to implement this technique, potentially including code snippets and explanations of the underlying concepts. The focus is on a specific method (regression models) for addressing a common data issue (missing values), suggesting a hands-on approach. The mention of Gemini implies the integration of a specific AI tool to enhance the process. Further details would be needed to assess the depth and novelty of the approach.
Reference

AIでデータ分析-データ前処理(22)-欠損処理:回帰モデルによる欠損補完

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

Understanding Tensor Data Structures with Go

Published:Dec 27, 2025 08:08
1 min read
Zenn ML

Analysis

This article from Zenn ML details the implementation of tensors, a fundamental data structure for automatic differentiation in machine learning, using the Go programming language. The author prioritizes understanding the concept by starting with a simple implementation and then iteratively improving it based on existing libraries like NumPy. The article focuses on the data structure of tensors and optimization techniques learned during the process. It also mentions a related article on automatic differentiation. The approach emphasizes a practical, hands-on understanding of tensors, starting from basic concepts and progressing to more efficient implementations.
Reference

The article introduces the implementation of tensors, a fundamental data structure for automatic differentiation in machine learning.

Research#llm📝 BlogAnalyzed: Dec 27, 2025 01:31

Chroma Introduction (Part 1): Registering Text to VectorStore

Published:Dec 26, 2025 23:21
1 min read
Qiita LLM

Analysis

This article introduces Chroma, a free VectorStore usable with Python, and focuses on the initial step of registering text. It's a practical guide for those building RAG systems, highlighting the importance of VectorStores in vectorizing and storing text. The article's focus on a specific tool and a fundamental task makes it immediately useful for developers. However, the title suggests it's part one, implying further articles will be needed for a complete understanding of Chroma and its capabilities. The article's value lies in its hands-on approach to a crucial aspect of RAG implementation.

Key Takeaways

Reference

When building a RAG (Retrieval-Augmented Generation) system, VectorStore, which vectorizes and stores text, plays an important role.