Search: 使用Gemini - ai.jp.net

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!

Key Takeaways

•The article documents a learning experience on backpropagation using Gemini as an aid.
•The author utilizes the "Zeroから作るDeepLearning" textbook.
•The development environment is VScode with Python and Anaconda.

Reference

“The article is based on conversations with Gemini.”

Permalink Qiita DL

research #llm 📝 BlogAnalyzed: Jan 17, 2026 19:30

AI Alert! Track GAFAM's Latest Research with Lightning-Fast Summaries!

Published:Jan 17, 2026 07:39

•

1 min read

•

Zenn LLM

Analysis

This innovative monitoring bot leverages the power of Gemini 2.5 Flash to provide instant summaries of new research from tech giants like GAFAM, delivering concise insights directly to your Discord. The ability to monitor multiple organizations simultaneously and operate continuously makes this a game-changer for staying ahead of the curve in the AI landscape!

Key Takeaways

•Monitors multiple organizations (e.g., facebookresearch, google-deepmind) simultaneously.
•Uses Gemini 2.5 Flash for rapid, 3-line summaries of READMEs.
•Operates automatically 24/7 using Google Apps Script triggers.

Reference

“The bot uses Gemini 2.5 Flash to summarize English READMEs into 3-line Japanese summaries.”

Permalink Zenn LLM

business #llm 📝 BlogAnalyzed: Jan 15, 2026 07:09

Apple Bets on Google Gemini: A Cloud-Based AI Partnership and OpenAI's Rejection

Published:Jan 15, 2026 06:40

•

1 min read

•

Techmeme

Analysis

This deal signals Apple's strategic shift toward leveraging existing cloud infrastructure for AI, potentially accelerating their AI integration roadmap without heavy capital expenditure. The rejection from OpenAI suggests a competitive landscape where independent models are vying for major platform partnerships, highlighting the valuation and future trajectory of each AI model.

Key Takeaways

•Apple is partnering with Google for Gemini models via a cloud contract.
•OpenAI declined to be Apple's custom model provider.
•The deal reflects a cautious approach to infrastructure investment.

Reference

“Apple's Google Gemini deal will be a cloud contract where Apple pays Google; another source says OpenAI declined to be Apple's custom model provider.”

Permalink Techmeme

business #llm 📰 NewsAnalyzed: Jan 12, 2026 17:15

Apple and Google Forge AI Alliance: Gemini to Power Siri and Future Apple AI

Published:Jan 12, 2026 17:12

•

1 min read

•

TechCrunch

Analysis

This partnership signifies a major shift in the AI landscape, highlighting the strategic importance of access to cutting-edge models and cloud infrastructure. Apple's integration of Gemini underscores the growing trend of leveraging partnerships to accelerate AI development and circumvent the high costs of in-house model creation. This move could potentially reshape the competitive dynamics of the voice assistant market.

Key Takeaways

•Apple is partnering with Google to use Gemini AI models.
•The partnership is non-exclusive and multi-year.
•Google Cloud technology will also be utilized.

Reference

“Apple and Google have embarked on a non-exclusive, multi-year partnership that will involve Apple using Gemini models and Google cloud technology for future foundational models.”

Permalink TechCrunch

product #llm 📝 BlogAnalyzed: Jan 5, 2026 10:25

Samsung's Gemini-Powered Fridge: Necessity or Novelty?

Published:Jan 5, 2026 06:53

•

1 min read

•

r/artificial

Analysis

Integrating LLMs into appliances like refrigerators raises questions about computational overhead and practical benefits. While improved food recognition is valuable, the cost-benefit analysis of using Gemini for this specific task needs careful consideration. The article lacks details on power consumption and data privacy implications.

Key Takeaways

•Samsung's Family Hub refrigerators will now use Google's Gemini AI.
•The AI Vision feature aims to improve food recognition capabilities.
•The system claims to identify unlimited fresh and processed food items.

Reference

““instantly identify unlimited fresh and processed food items””

Permalink r/artificial

infrastructure #automation 📝 BlogAnalyzed: Jan 4, 2026 11:18

AI-Assisted Home Server VPS Setup with React and Go

Published:Jan 4, 2026 11:13

•

1 min read

•

Qiita AI

Analysis

This article details a personal project leveraging AI for guidance in setting up a home server as a VPS and deploying a web application. While interesting as a personal anecdote, it lacks technical depth and broader applicability for professional AI or infrastructure discussions. The value lies in demonstrating AI's potential for assisting novice users with complex technical tasks.

Key Takeaways

•The author used Gemini for guidance on a home server project.
•The project involved setting up Proxmox and deploying a React+Go application.
•The resulting web service is accessible at scof-gallery.com.

Reference

“すべてはGeminiの「謎の提案」から始まった (It all started with Gemini's 'mysterious suggestion')”

Permalink Qiita AI

research #llm 📝 BlogAnalyzed: Jan 4, 2026 07:06

LLM Prompt Token Count and Processing Time Impact of Whitespace and Newlines

Published:Jan 4, 2026 05:30

•

1 min read

•

Zenn Gemini

Analysis

This article addresses a practical concern for LLM application developers: the impact of whitespace and newlines on token usage and processing time. While the premise is sound, the summary lacks specific findings and relies on an external GitHub repository for details, making it difficult to assess the significance of the results without further investigation. The use of Gemini and Vertex AI is mentioned, but the experimental setup and data analysis methods are not described.

Key Takeaways

•Investigates the impact of whitespace and newlines in LLM prompts.
•Uses Gemini and Vertex AI for experimentation.
•Relies on a GitHub repository for experimental details.

Reference

“LLMを使用したアプリケーションを開発している際に、空白文字や改行はどの程度料金や処理時間に影響を与えるのかが気になりました。”

Permalink Zenn Gemini

Technology #AI Applications 📝 BlogAnalyzed: Jan 4, 2026 05:49

Sharing canvas projects

Published:Jan 4, 2026 03:45

•

1 min read

•

r/Bard

Analysis

The article is a user's inquiry on the r/Bard subreddit about sharing projects created using the Gemini app's canvas feature. The user is interested in the file size limitations and potential improvements with future Gemini versions. It's a discussion about practical usage and limitations of a specific AI tool.

Key Takeaways

•User seeks to share and learn about projects created using Gemini app's canvas.
•User is concerned about file size limitations.
•User speculates on improvements in future Gemini versions.

Reference

“I am wondering if anyone has fun projects to share? What is the largest length of your file? I have made a 46k file and found that after that it doesn't seem to really be able to be expanded upon further. Has anyone else run into the same issue and do you think that will change with Gemini 3.5 or Gemini 4? I'd love to see anyone with over-engineered projects they'd like to share!”

Permalink r/Bard

business #llm 📝 BlogAnalyzed: Jan 4, 2026 02:51

Gemini CLI for Core Systems: Double-Entry Bookkeeping and Credit Creation

Published:Jan 4, 2026 02:33

•

1 min read

•

Qiita LLM

Analysis

This article explores the potential of using Gemini CLI to build core business systems, specifically focusing on double-entry bookkeeping and credit creation. While the concept is intriguing, the article lacks technical depth and practical implementation details, making it difficult to assess the feasibility and scalability of such a system. The reliance on natural language input for accounting tasks raises concerns about accuracy and security.

Key Takeaways

•The article discusses using Gemini CLI for building core systems.
•It focuses on double-entry bookkeeping and credit creation.
•The approach aims to simplify system development without requiring extensive programming knowledge.

Reference

“今回は、プログラミングの専門知識がなくても、対話AI（Gemini CLI）を使って基幹システムに挑戦です。”

Permalink Qiita LLM

product #agent 📝 BlogAnalyzed: Jan 4, 2026 00:45

Gemini-Powered Agent Automates Manim Animation Creation from Paper

Published:Jan 3, 2026 23:35

•

1 min read

•

r/Bard

Analysis

This project demonstrates the potential of multimodal LLMs like Gemini for automating complex creative tasks. The iterative feedback loop leveraging Gemini's video reasoning capabilities is a key innovation, although the reliance on Claude Code suggests potential limitations in Gemini's code generation abilities for this specific domain. The project's ambition to create educational micro-learning content is promising.

Key Takeaways

•An open-source Manim coding agent was developed using Gemini and Langchain.
•Gemini's multimodal capabilities are leveraged for iterative video refinement.
•The project aims to create educational micro-learning content through automated animation.

Reference

“"The good thing about Gemini is it's native multimodality. It can reason over the generated video and that iterative loop helps a lot and dealing with just one model and framework was super easy"”

Permalink r/Bard

Technology #AI Performance/User Experience 📝 BlogAnalyzed: Jan 4, 2026 05:50

Gemini text coming in chunks every few seconds. Has anyone else had this problem?

Published:Jan 3, 2026 20:30

•

1 min read

•

r/Bard

Analysis

The article reports a user experiencing slow and fragmented text output from Google's Gemini AI model, specifically when pulling from YouTube. The issue has persisted for almost three weeks and seems to be related to network connectivity, though switching between Wi-Fi and 5G offers only temporary relief. The post originates from a Reddit thread, indicating a user-reported issue rather than an official announcement.

Key Takeaways

•User experiencing slow and fragmented text output from Gemini AI.
•Issue is persistent, lasting almost three weeks.
•Problem seems related to network connectivity, but switching networks offers only temporary relief.
•The issue is reported on Reddit, indicating a user-reported problem.

Reference

“Happens nearly every chat and will 100% happen when pulling from YouTube. Been like this for almost 3 weeks now.”

Permalink r/Bard

Research #llm 📝 BlogAnalyzed: Jan 3, 2026 18:31

The Essence of AI-Driven Development: Natural Language Becomes Programming Language, AI Becomes Compiler

Published:Jan 3, 2026 18:25

•

1 min read

•

Qiita AI

Analysis

The article discusses a paradigm shift in programming, where the abstraction layer has moved up. It highlights the use of AI, specifically Gemini, in Firebase Studio (IDX) for co-programming. The core idea is that natural language is becoming the programming language, and AI is acting as the compiler.

Key Takeaways

•AI is changing the way we program.
•Natural language is becoming a programming interface.
•AI tools are acting as compilers, translating natural language into executable code.

Reference

“The author's experience with Gemini and co-programming in Firebase Studio (IDX) led to the realization of a paradigm shift.”

Permalink Qiita AI

product #nocode 📝 BlogAnalyzed: Jan 3, 2026 12:33

Gemini Empowers No-Code Android App Development: A Paradigm Shift?

Published:Jan 3, 2026 11:45

•

1 min read

•

r/deeplearning

Analysis

This article highlights the potential of large language models like Gemini to democratize app development, enabling individuals without coding skills to create functional applications. However, the article lacks specifics on the app's complexity, performance, and the level of Gemini's involvement, making it difficult to assess the true impact and limitations of this approach.

Key Takeaways

•Gemini is used to build an Android app without traditional coding.
•The author previously lacked coding skills.
•The article originates from a Reddit post, suggesting anecdotal evidence.

Reference

“"I don't know how to code."”

Permalink r/deeplearning

Technology #Image Processing 📝 BlogAnalyzed: Jan 3, 2026 07:02

Inquiry about Removing Watermark from Image

Published:Jan 3, 2026 03:54

•

1 min read

•

r/Bard

Analysis

The article is a discussion thread from a Reddit forum, specifically r/Bard, indicating a user's question about removing a watermark ('synthid') from an image without using Google's Gemini AI. The source and user are identified. The content suggests a practical problem and a desire for alternative solutions.

Key Takeaways

•The article presents a user's question about removing a watermark.
•The user seeks alternatives to Google's Gemini AI.
•The context is a discussion forum (r/Bard).

Reference

“The core of the article is the user's question: 'Anyone know if there's a way to get the synthid watermark from an image without the use of gemini?'”

Permalink r/Bard

Research #llm 📝 BlogAnalyzed: Jan 3, 2026 05:25

The Case Against RAG: Why I Switched from ChatGPT's RAG to Gemini Pro's 'Brute-Force Long Context'

Published:Jan 3, 2026 02:00

•

1 min read

•

Zenn AI

Analysis

This article discusses the author's frustration with implementing Retrieval-Augmented Generation (RAG) with ChatGPT and their subsequent switch to using Gemini Pro's long context window capabilities. The author highlights the complexities and challenges associated with RAG, such as data preprocessing, chunking, vector database management, and query tuning. They suggest that Gemini Pro's ability to handle longer contexts directly eliminates the need for these complex RAG processes in certain use cases.

Key Takeaways

•RAG implementation can be complex and time-consuming.
•Gemini Pro's long context window offers an alternative to RAG in some cases.
•Data preprocessing and vector database management are significant challenges in RAG.
•The choice between RAG and long context models depends on the specific use case and requirements.

Reference

“"I was tired of the RAG implementation with ChatGPT, so I completely switched to Gemini Pro's 'brute-force long context'."”

Permalink Zenn AI

Research #AI Agents 📝 BlogAnalyzed: Jan 3, 2026 02:03

SIMA 2 Generalizes in Unseen 3D and Realistic Worlds Using Gemini and Self-Improvement Techniques

Published:Jan 2, 2026 10:15

•

1 min read

•

InfoQ中国

Analysis

The article discusses SIMA 2, an AI model that uses Gemini and self-improvement techniques to generalize in new 3D and realistic environments. Further analysis would require the full article to understand the specific techniques used and the implications of this generalization.

Key Takeaways

•SIMA 2 uses Gemini for improved performance.
•Self-improvement techniques are key to SIMA 2's generalization ability.
•The model can operate in previously unseen 3D and realistic environments.

Reference

“”

Permalink InfoQ中国

Research #llm 📝 BlogAnalyzed: Jan 3, 2026 06:12

Verification: Mirroring Mac Screen to iPhone for AI Pair Programming with Gemini Live

Published:Jan 2, 2026 04:01

•

1 min read

•

Zenn AI

Analysis

The article describes a method to use Google's Gemini Live for AI pair programming by mirroring a Mac screen to an iPhone. It addresses the lack of a PC version of Gemini Live by using screen mirroring software. The article outlines the steps involved, focusing on a practical workaround.

Key Takeaways

•Addresses the lack of a PC version of Gemini Live.
•Provides a practical workaround using screen mirroring.
•Focuses on a specific technical implementation.

Reference

“The article's content focuses on a specific technical workaround, using LetsView to mirror the Mac screen to an iPhone and then using Gemini Live on the iPhone. The article's introduction clearly states the problem and the proposed solution.”

Permalink Zenn AI

Tutorial #AI Video Generation 📝 BlogAnalyzed: Jan 3, 2026 06:04

Generating Business Videos with AI Day 2: Generating Audio Files with Gemini TTS API

Published:Jan 1, 2026 22:00

•

1 min read

•

Zenn AI

Analysis

The article outlines the process of setting up the Gemini TTS API to generate WAV audio files from text for business videos. It provides a clear goal, prerequisites, and a step-by-step approach. The focus is on practical implementation, starting with audio generation as a fundamental element for video creation. The article is concise and targeted towards users with basic Python knowledge and a Google account.

Key Takeaways

•Focuses on practical implementation of AI for video creation.
•Provides a clear, step-by-step guide for setting up the Gemini TTS API.
•Targets users with basic technical prerequisites (Python, Google account).

Reference

“The goal is to set up the Gemini TTS API and generate WAV audio files from text.”

Permalink Zenn AI

Technology #Artificial Intelligence 📝 BlogAnalyzed: Jan 3, 2026 06:12

Image Segmentation with Gemini for Beginners

Published:Dec 30, 2025 12:57

•

1 min read

•

Zenn Gemini

Analysis

The article introduces image segmentation using Google's Gemini 2.5 Flash model, focusing on its ability to identify and isolate objects within an image. It highlights the practical challenges faced when adapting Google's sample code for specific use cases, such as processing multiple image files from Google Drive. The article's focus is on providing a beginner-friendly guide to overcome these hurdles.

Key Takeaways

•Gemini 2.5 Flash offers image segmentation capabilities.
•The article addresses challenges in adapting Google's sample code.
•The focus is on providing a beginner-friendly guide.

Reference

“This article discusses the use of Gemini 2.5 Flash for image segmentation, focusing on identifying and isolating objects within an image.”

Permalink Zenn Gemini

Research #llm 📝 BlogAnalyzed: Dec 29, 2025 09:30

Latest 2025 Edition: How to Build Your Own AI with Gemini's Free Tier

Published:Dec 29, 2025 09:04

•

1 min read

•

Qiita AI

Analysis

This article, likely a tutorial, focuses on leveraging Gemini's free tier to create a personalized AI using Retrieval-Augmented Generation (RAG). RAG allows users to augment the AI's knowledge base with their own data, enabling it to provide more relevant and customized responses. The article likely walks through the process of adding custom information to Gemini, effectively allowing it to "consult" user-provided resources when generating text. This approach is valuable for creating AI assistants tailored to specific domains or tasks, offering a practical application of RAG techniques for individual users. The "2025" in the title suggests forward-looking relevance, possibly incorporating future updates or features of the Gemini platform.

Key Takeaways

•Gemini's free tier can be used to create custom AI solutions.
•RAG allows for augmenting AI knowledge with user-specific data.
•Personalized AI can be tailored to specific tasks and domains.

Reference

“AI that answers while looking at your own reference books, instead of only talking from its own memory.”

Permalink Qiita AI

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

Gemini's Memory Issues: User Reports Limited Context Retention

Published:Dec 29, 2025 05:44

•

1 min read

•

r/Bard

Analysis

This news item, sourced from a Reddit post, highlights a potential issue with Google's Gemini AI model regarding its ability to retain context in long conversations. A user reports that Gemini only remembered the last 14,000 tokens of a 117,000-token chat, a significant limitation. This raises concerns about the model's suitability for tasks requiring extensive context, such as summarizing long documents or engaging in extended dialogues. The user's uncertainty about whether this is a bug or a typical limitation underscores the need for clearer documentation from Google regarding Gemini's context window and memory management capabilities. Further investigation and user reports are needed to determine the prevalence and severity of this issue.

Key Takeaways

•Gemini may have limitations in retaining context in long conversations.
•The reported context window is significantly smaller than the total conversation length.
•Users should be aware of potential memory limitations when using Gemini for tasks requiring extensive context.

Reference

“Until I asked Gemini (a 3 Pro Gem) to summarize our conversation so far, and they only remembered the last 14k tokens. Out of our entire 117k chat.”

Permalink r/Bard

Technology #AI Tools 📝 BlogAnalyzed: Dec 28, 2025 21:57

Why use Gemini CLI over Antigravity?

Published:Dec 28, 2025 19:47

•

2 min read

•

r/Bard

Analysis

The Reddit post raises a valid question about the utility of the Gemini CLI compared to Antigravity, particularly for Pro and Ultra users. The core issue is the perceived lower limits and faster reset times of the CLI, making it less appealing. The author notes that the limits reset every 24 hours for the CLI, compared to every 5 hours for Antigravity users. The primary advantage seems to be the ability to use both, as their limits are separate, but the overall value proposition of the CLI is questioned due to its limitations. The post highlights a user's practical experience and prompts a discussion about the optimal usage of these tools.

Key Takeaways

•Gemini CLI has lower usage limits compared to Antigravity.
•CLI limits reset every 24 hours, while Antigravity resets every 5 hours for Pro/Ultra users.
•The primary benefit is the ability to use both, but the CLI's value is questioned due to its limitations.

Reference

“It seems that the limits for the CLI are much lower and also reset every 24 hours as opposed to the Antigravity limits that reset every 5 hours (For Pro and Ultra users). In my experience I also tend to reach the limits much faster on the CLI.”

Permalink r/Bard

Research #llm 📝 BlogAnalyzed: Dec 28, 2025 17:31

IME AI Studio is not the best way to use Gemini 3

Published:Dec 28, 2025 17:05

•

1 min read

•

r/Bard

Analysis

This article, sourced from a Reddit post, presents a user's perspective on the performance of Gemini 3. The user claims that Gemini 3's performance is subpar when used within the Gemini App or IME AI Studio, citing issues like quantization, limited reasoning ability, and frequent hallucinations. The user recommends using models in direct chat mode on platforms like LMArena, suggesting that these platforms utilize direct third-party API calls, potentially offering better performance compared to Google's internal builds for free-tier users. The post highlights the potential discrepancies in performance based on the access method and platform used to interact with the model.

Key Takeaways

•Gemini 3 performance may vary depending on the platform used.
•Direct API access might offer better performance than internal builds.
•User experiences with AI models can differ significantly.

Reference

“Gemini 3 is not that great if you use it in the Gemini App or AIS in the browser, it's quite quantized most of the time, doesn't reason for long, and hallucinates a lot more.”

Permalink r/Bard

Research #llm 📝 BlogAnalyzed: Dec 28, 2025 12:30

15 Year Olds Can Now Build Full Stack Research Tools

Published:Dec 28, 2025 12:26

•

1 min read

•

r/ArtificialInteligence

Analysis

This post highlights the increasing accessibility of AI tools and development platforms. The claim that a 15-year-old built a complex OSINT tool using Gemini raises questions about the ease of use and power of modern AI. While impressive, the lack of verifiable details makes it difficult to assess the tool's actual capabilities and the student's level of involvement. The post sparks a discussion about the future of AI development and the potential for young people to contribute to the field. However, skepticism is warranted until more concrete evidence is provided. The rapid generation of a 50-page report is noteworthy, suggesting efficient data processing and synthesis capabilities.

Key Takeaways

•AI tools are becoming more accessible to younger developers.
•Large language models (LLMs) like Gemini can significantly accelerate research and development.
•The potential impact of AI on fields like foreign affairs and market research is growing.

Reference

“A 15 year old in my school built an osint tool with over 250K lines of code across all libraries...”

Permalink r/ArtificialInteligence

Research #llm 📝 BlogAnalyzed: Dec 28, 2025 15:02

When did you start using Gemini (formerly Bard)?

Published:Dec 28, 2025 12:09

•

1 min read

•

r/Bard

Analysis

This Reddit post on r/Bard is a simple question prompting users to share when they started using Google's AI model, now known as Gemini (formerly Bard). It's a basic form of user engagement and data gathering, providing anecdotal information about the adoption rate and user experience over time. While not a formal study, the responses could offer Google insights into user loyalty, the impact of the rebranding from Bard to Gemini, and potential correlations between usage start date and user satisfaction. The value lies in the collective, informal feedback provided by the community. It lacks scientific rigor but offers a real-time pulse on user sentiment.

Key Takeaways

•Simple user engagement question on Reddit.
•Provides anecdotal data on Gemini/Bard adoption.
•Potentially useful for Google to gauge user sentiment.

Reference

“submitted by /u/Short_Cupcake8610”

Permalink r/Bard

Research #llm 📝 BlogAnalyzed: Dec 28, 2025 21:57

Implementation Architecture Proposal for LLM's "Pre-Output Control" and "Time-Axis Independent Long-Term Memory" (Alaya-Core v2.0)

Published:Dec 27, 2025 23:06

•

1 min read

•

Zenn LLM

Analysis

This article analyzes a peculiar behavior observed in a long-term context durability test using Gemini 3 Flash, involving over 800,000 tokens of dialogue. The core focus is on the LLM's ability to autonomously correct its output before completion, a behavior described as "Pre-Output Control." This contrasts with post-output reflection. The article likely delves into the architecture of Alaya-Core v2.0, proposing a method for achieving this pre-emptive self-correction and potentially time-axis independent long-term memory within the LLM framework. The research suggests a significant advancement in LLM capabilities, moving beyond simple probabilistic token generation.

Key Takeaways

•The article explores "Pre-Output Control" in LLMs, where the model corrects its output before completion.
•This behavior was observed in a long-term context test with over 800,000 tokens.
•The research likely proposes an architecture (Alaya-Core v2.0) to enable this and potentially time-axis independent long-term memory.

Reference

“"Ah, there was a risk of an accommodating bias in the current thought process. I will correct it before output."”

Permalink Zenn LLM

Technology #AI in Software Development 📝 BlogAnalyzed: Dec 28, 2025 21:56

I Asked Gemini About Antigravity Settings

Published:Dec 27, 2025 21:03

•

1 min read

•

Zenn Gemini

Analysis

The article discusses the author's experience using Gemini to understand and troubleshoot their Antigravity coding tool settings. The author had defined rules in a file named GEMINI.md, but found that these rules weren't always being followed. They then consulted Gemini for clarification, and the article shares the response received. The core of the issue revolves around ensuring that specific coding protocols, such as branch management, are consistently applied. This highlights the challenges of relying on AI tools to enforce complex workflows and the need for careful rule definition and validation.

Key Takeaways

•The article highlights the use of AI (Gemini) to understand and troubleshoot coding tool settings.
•It emphasizes the importance of clearly defined rules and protocols for coding workflows.
•The issue of ensuring consistent adherence to these rules when using AI tools is raised.

Reference

“The article mentions the rules defined in GEMINI.md, including the critical protocols for branch management, such as creating a working branch before making code changes and prohibiting work on main, master, or develop branches.”

Permalink Zenn Gemini

Research #llm 📝 BlogAnalyzed: Dec 27, 2025 12:31

AI Data Analysis - Data Preprocessing (22) - Missing Value Handling: Missing Value Completion by Regression Model

Published:Dec 27, 2025 12:11

•

1 min read

•

Qiita AI

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.

Key Takeaways

•Using regression models for missing value imputation.
•Implementation in Python.
•AI utilization with Gemini.
•Focus on data preprocessing techniques.

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”

Permalink Qiita AI

Research #llm 📝 BlogAnalyzed: Dec 27, 2025 13:03

Generating 4K Images with Gemini Pro on Nano Banana Pro: Is it Possible?

Published:Dec 27, 2025 11:13

•

1 min read

•

r/Bard

Analysis

This Reddit post highlights a user's struggle to generate 4K images using Gemini Pro on a Nano Banana Pro device, consistently resulting in 2K resolution outputs. The user questions whether this limitation is inherent to the hardware, the software, or a configuration issue. The post lacks specific details about the software used for image generation, making it difficult to pinpoint the exact cause. Further investigation would require knowing the specific image generation tool, its settings, and the capabilities of the Nano Banana Pro's GPU. The question is relevant to users interested in leveraging AI image generation on resource-constrained devices.

Key Takeaways

•Gemini Pro may have resolution limitations depending on the platform.
•Hardware limitations of Nano Banana Pro could be a factor.
•Software settings within the image generation tool need to be checked.

Reference

“"im trying to generate the 4k images but always end with 2k files I have gemini pro, it's fixable or it's limited at 2k?"”

Permalink r/Bard

Paper #RAG, LLM, Information Retrieval 🔬 ResearchAnalyzed: Jan 3, 2026 20:02

HiFi-RAG: Improved RAG for Open-Domain QA

Published:Dec 27, 2025 02:37

•

1 min read

•

ArXiv

Analysis

This paper presents HiFi-RAG, a novel Retrieval-Augmented Generation (RAG) system that won the MMU-RAGent NeurIPS 2025 competition. The core innovation lies in a hierarchical filtering approach and a two-pass generation strategy leveraging different Gemini 2.5 models for efficiency and performance. The paper highlights significant improvements over baselines, particularly on a custom dataset focusing on post-cutoff knowledge, demonstrating the system's ability to handle recent information.

Key Takeaways

•HiFi-RAG is a novel RAG system employing hierarchical filtering and two-pass generation.
•It leverages Gemini 2.5 Flash for efficiency and Gemini 2.5 Pro for reasoning.
•The system achieves significant performance gains, especially on post-cutoff knowledge tasks.
•The approach demonstrates the effectiveness of multi-stage pipelines in RAG.

Reference

“HiFi-RAG outperforms the parametric baseline by 57.4% in ROUGE-L and 14.9% in DeBERTaScore on Test2025.”

Permalink ArXiv

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.

Key Takeaways

•Focuses on count and frequency encoding.
•Uses Python for implementation.
•Leverages Gemini for AI.

Reference

“AIでデータ分析-データ前処理(37)-エン...”

Permalink Qiita AI

Research #llm 📝 BlogAnalyzed: Dec 26, 2025 17:35

Get Gemini to Review Code Locally Like Gemini Code Assist

Published:Dec 26, 2025 06:09

•

1 min read

•

Zenn Gemini

Analysis

This article addresses the frustration of having Gemini generate code that is then flagged by Gemini Code Assist during pull request reviews. The author proposes a solution: leveraging local Gemini instances to perform code reviews in a manner similar to Gemini Code Assist, thereby streamlining the development process and reducing iterative feedback loops. The article highlights the inefficiency of multiple rounds of corrections and suggestions from different Gemini instances and aims to improve developer workflow by enabling self-review capabilities within the local Gemini environment. The article mentions a gemini-cli extension for this purpose.

Key Takeaways

•Local Gemini instances can be used for code review.
•This approach aims to reduce feedback loops during pull requests.
•A gemini-cli extension is available for this purpose.

Reference

“Geminiにコードを書いてもらって、PullRequestを出したらGemini Code Assistにレビュー指摘される。そんな経験ありませんか。”

Permalink Zenn Gemini

Research #llm 📝 BlogAnalyzed: Dec 25, 2025 14:46

AI Data Analysis - Data Preprocessing (36) - Encoding: Target Encoding / Mean Encoding

Published:Dec 25, 2025 14:41

•

1 min read

•

Qiita AI

Analysis

This article discusses target encoding and mean encoding techniques for data preprocessing in AI data analysis. It mentions using Python for implementation and Gemini for AI utilization. The article seems to be part of a series on data preprocessing, specifically focusing on encoding methods. The content is likely practical, providing code examples and explanations of how to apply these encoding techniques. The mention of Gemini suggests the use of AI to assist in the data analysis process, potentially for tasks like feature engineering or model selection. The article's structure includes an introduction to the data used, Python implementation details, AI utilization with Gemini, and a summary.

Key Takeaways

•Target encoding and mean encoding are useful for categorical feature encoding.
•Python is used for implementation.
•Gemini AI can be leveraged for data analysis tasks.

Reference

“AIでデータ分析-データ前処理(36)-エンコーディング：Target Encoding / Mean Encoding”

Permalink Qiita AI

Research #llm 📝 BlogAnalyzed: Dec 24, 2025 20:55

Become a Dual-Wielding OpenAI and Gemini API User with OpenAI's SDK

Published:Dec 24, 2025 11:56

•

1 min read

•

Qiita ChatGPT

Analysis

This article discusses leveraging the OpenAI SDK to integrate Google's Gemini model alongside OpenAI's models. It highlights the desire to utilize Gemini's capabilities, particularly after the release of Gemini 3, which is noted for its improved quality. The article likely provides practical guidance or code examples on how to achieve this integration, enabling developers to switch between or combine the strengths of both AI models within their applications. The focus is on practical application and expanding the range of available AI tools for developers.

Key Takeaways

•Leverage OpenAI's SDK for broader AI model integration.
•Explore Gemini's capabilities alongside OpenAI's.
•Potentially improve application performance by choosing the best model for the task.

Reference

“I want to be able to use Gemini as well as OpenAI!”

Permalink Qiita ChatGPT

Research #llm 👥 CommunityAnalyzed: Jan 4, 2026 08:18

Use the Gemini API with OpenAI Fallback in TypeScript

Published:Apr 4, 2025 09:41

•

1 min read

•

Hacker News

Analysis

This article likely discusses how to integrate Google's Gemini API with a fallback mechanism to OpenAI's models within a TypeScript environment. The focus is on providing a resilient and potentially cost-effective solution for LLM access. The use of a fallback suggests a strategy to handle potential Gemini API outages or rate limits, leveraging OpenAI as a backup. The article's value lies in providing practical code examples and guidance for developers working with these APIs.

Key Takeaways

•Provides a resilient approach to LLM access by using a fallback mechanism.
•Offers practical guidance for developers using Gemini and OpenAI APIs in TypeScript.
•Addresses potential issues like API outages or rate limits.

Reference

“The article likely provides code snippets and explanations on how to switch between the Gemini and OpenAI APIs based on availability or other criteria.”

Permalink Hacker News