Search: Zenn - ai.jp.net

research #kaggle 📝 BlogAnalyzed: Jan 19, 2026 14:30

Kaggle Journey: Level Up Your Machine Learning Skills!

Published:Jan 19, 2026 11:38

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1 min read

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Zenn ML

Analysis

This Zenn ML article series provides an excellent roadmap for intermediate machine learning enthusiasts, guiding them through the exciting world of Kaggle competitions! It offers a structured learning path, starting with the fundamentals and advancing to more complex concepts. The potential to learn from real-world datasets and compete against others is truly inspiring!

Key Takeaways

•The article is a series on Kaggle, a platform for machine learning competitions.
•The series focuses on intermediate machine learning topics.
•It covers various aspects of machine learning, from handling missing values to practical competition participation.

Reference

“The article series guides users through intermediate machine learning.”

Permalink Zenn ML

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

AI Dialogue on Programming: Beyond Manufacturing

Published:Jan 15, 2026 10:03

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1 min read

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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

•The article is a repost from Zenn.
•It uses AI dialogue as its primary content.
•The dialogue is focused on programming related thought

Reference

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

Permalink Qiita AI

product #image generation 📝 BlogAnalyzed: Jan 13, 2026 20:15

Google AI Studio: Creating Animated GIFs from Image Prompts

Published:Jan 13, 2026 15:56

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1 min read

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Zenn AI

Analysis

The article's focus on generating animated GIFs from image prompts using Google AI Studio highlights a practical application of image generation capabilities. The tutorial approach, guiding users through the creation of character animations, caters to a broader audience interested in creative AI applications, although it lacks depth in technical details or business strategy.

Key Takeaways

•Demonstrates the creation of animated GIFs using Google AI Studio and Python.
•Emphasizes a user-friendly tutorial approach for generating character animations.
•Highlights a practical application of AI image generation for creative purposes.

Reference

“The article explains how to generate a GIF animation by preparing a base image and having the AI change the character's expression one after another.”

Permalink Zenn AI

product #llm 📝 BlogAnalyzed: Jan 10, 2026 08:00

AI Router Implementation Cuts API Costs by 85%: Implications and Questions

Published:Jan 10, 2026 03:38

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1 min read

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Zenn LLM

Analysis

The article presents a practical cost-saving solution for LLM applications by implementing an 'AI router' to intelligently manage API requests. A deeper analysis would benefit from quantifying the performance trade-offs and complexity introduced by this approach. Furthermore, discussion of its generalizability to different LLM architectures and deployment scenarios is missing.

Key Takeaways

•The article focuses on reducing the API costs of LLM applications.
•An 'AI router' is used to intelligently manage LLM API requests.
•The implementation resulted in an 85% reduction in API costs.

Reference

“"最高性能モデルを使いたい。でも、全てのリクエストに使うと月額コストが数十万円に..."”

Permalink Zenn LLM

product #llm 📝 BlogAnalyzed: Jan 4, 2026 07:36

Gemini's Harsh Review Sparks Self-Reflection on Zenn Platform

Published:Jan 4, 2026 00:40

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1 min read

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Zenn Gemini

Analysis

This article highlights the potential for AI feedback to be both insightful and brutally honest, prompting authors to reconsider their content strategy. The use of LLMs for content review raises questions about the balance between automated feedback and human judgment in online communities. The author's initial plan to move content suggests a sensitivity to platform norms and audience expectations.

Key Takeaways

•Gemini provided feedback on a Zenn article draft.
•The author initially planned to move the content to another platform.
•The AI's review was considered valuable content in itself.

Reference

“…という書き出しを用意して記事を認め始めたのですが、zennaiレビューを見てこのaiのレビューすらも貴重なコンテンツの一部であると認識せざるを得ない状況です。”

Permalink Zenn Gemini

product #llm 📝 BlogAnalyzed: Jan 3, 2026 10:39

Summarizing Claude Code Usage by Its Developer: Practical Applications

Published:Jan 3, 2026 05:47

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1 min read

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Zenn Claude

Analysis

This article summarizes the usage of Claude Code by its developer, offering practical insights into its application. The value lies in providing real-world examples and potentially uncovering best practices directly from the source, although the depth of the summary is unknown without the full article. The reliance on a Twitter post as the primary source could limit the comprehensiveness and technical detail.

Key Takeaways

•The article summarizes Claude Code usage based on the developer's posts.
•The author obtained permission from the developer to create the summary.
•The article aims to provide practical insights into Claude Code application.

Reference

“この記事では、Claude Codeの開発者であるBorisさんが投稿されていたClaude Codeの活用法をまとめさせていただきました。”

Permalink Zenn Claude

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

AI Agent Era: A Dystopian Future?

Published:Jan 3, 2026 02:07

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1 min read

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Zenn AI

Analysis

The article discusses the potential for AI-generated code to become so sophisticated that human review becomes impossible. It references the current state of AI code generation, noting its flaws, but predicts significant improvements by 2026. The author draws a parallel to the evolution of image generation AI, highlighting its rapid progress.

Key Takeaways

•AI-generated code is currently flawed but rapidly improving.
•Human code review may become obsolete in the future.
•The evolution of AI image generation serves as a precedent for rapid AI development.

Reference

“Inspired by https://zenn.dev/ryo369/articles/d02561ddaacc62, I will write about future predictions.”

Permalink Zenn AI

Research #llm 📝 BlogAnalyzed: Jan 3, 2026 06:05

Understanding Comprehension Debt: Avoiding the Time Bomb in LLM-Generated Code

Published:Jan 2, 2026 03:11

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1 min read

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Zenn AI

Analysis

The article highlights the dangers of 'Comprehension Debt' in the context of rapidly generated code by LLMs. It warns that writing code faster than understanding it leads to problems like unmaintainable and untrustworthy code. The core issue is the accumulation of 'understanding debt,' which is akin to a 'cost of understanding' debt, making maintenance a risky endeavor. The article emphasizes the increasing concern about this type of debt in both practical and research settings.

Key Takeaways

•Comprehension Debt arises when code generation outpaces understanding.
•This debt leads to code that is difficult to maintain and trust.
•The article warns about the increasing concern regarding this issue in both practical and research settings.

Reference

“The article quotes the source, Zenn LLM, and mentions the website codescene.com. It also uses the phrase "writing speed > understanding speed" to illustrate the core problem.”

Permalink Zenn AI

Research #llm 📝 BlogAnalyzed: Jan 3, 2026 08:10

Tracking All Changelogs of Claude Code

Published:Dec 30, 2025 22:02

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1 min read

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Zenn Claude

Analysis

This article from Zenn discusses the author's experience tracking the changelogs of Claude Code, an AI model, throughout 2025. The author, who actively discusses Claude Code on X (formerly Twitter), highlights 2025 as a significant year for AI agents, particularly for Claude Code. The article mentions a total of 176 changelog updates and details the version releases across v0.2.x, v1.0.x, and v2.0.x. The author's dedication to monitoring and verifying these updates underscores the rapid development and evolution of the AI model during this period. The article sets the stage for a deeper dive into the specifics of these updates.

Key Takeaways

•The author has meticulously tracked all Claude Code changelogs since v1.0.x.
•2025 is highlighted as a pivotal year for AI agents, particularly Claude Code.
•A total of 176 changelog updates were made across three version series: v0.2.x, v1.0.x, and v2.0.x.

Reference

“The author states, "I've been talking about Claude Code on X (Twitter)." and "2025 was a year of great leaps for AI agents, and for me, it was the year of Claude Code."”

Permalink Zenn Claude

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

Comparison and Features of Recommended MCP Servers for ClaudeCode

Published:Dec 28, 2025 14:58

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1 min read

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Zenn AI

Analysis

This article from Zenn AI introduces and compares recommended MCP (Model Context Protocol) servers for ClaudeCode. It highlights the importance of MCP servers in enhancing the development experience by integrating external functions and tools. The article explains what MCP servers are, enabling features like code base searching, browser operations, and database access directly from ClaudeCode. The focus is on providing developers with information to choose the right MCP server for their needs, with Context7 being mentioned as an example. The article's value lies in its practical guidance for developers using ClaudeCode.

Key Takeaways

•MCP servers enhance ClaudeCode's functionality by integrating external tools.
•The article provides a comparison of different MCP server options.
•Context7 is presented as an example of a useful MCP server.

Reference

“MCP servers enable features like code base searching, browser operations, and database access directly from ClaudeCode.”

Permalink Zenn AI

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

Claude Code: Achieving Long Sessions with SubAgent and Skills - From Practical Usage to Design Philosophy

Published:Dec 28, 2025 14:52

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1 min read

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Zenn AI

Analysis

This article from Zenn AI focuses on addressing limitations in Claude Code, specifically the context window's constraints that lead to issues in long sessions. It introduces two key features: SubAgent and Skills. The article promises to provide practical guidance on how to use these features, including how to launch SubAgents and configure settings. The core problem addressed is the degradation of Claude's responses, session interruptions, and confusion in complex tasks due to the context window's limitations. The article aims to offer solutions to these common problems encountered by users of Claude Code.

Key Takeaways

•Addresses limitations of Claude Code's context window.
•Introduces SubAgent and Skills as solutions.
•Provides practical guidance on usage and configuration.

Reference

“The article addresses issues like: "Claude's responses becoming strange after long work," "Sessions being cut off," and "Getting lost in complex tasks."”

Permalink Zenn AI

Research #llm 📝 BlogAnalyzed: Dec 28, 2025 21:56

Can ChatGPT Atlas Be Used for Data Preparation? A Look at the Future of Dashboards

Published:Dec 28, 2025 12:36

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1 min read

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Zenn AI

Analysis

This article from Zenn AI discusses the potential of using ChatGPT Atlas for data preparation, a time-consuming process for data analysts. The author, Raiken, highlights the tediousness of preparing data for BI tools like Tableau, including exploring, acquiring, and processing open data. The article suggests that AI, specifically ChatGPT's Agent mode, can automate much of this preparation, allowing analysts to focus on the more enjoyable exploratory data analysis. The article implies a future where AI significantly streamlines the data preparation workflow, although human verification remains necessary.

Key Takeaways

•ChatGPT Atlas is explored as a tool to automate data preparation.
•Data preparation is a significant bottleneck in the data analysis workflow.
•AI can potentially reduce the time spent on data preparation, allowing analysts to focus on analysis.

Reference

“The most annoying part of performing analysis with BI tools is the preparation process.”

Permalink Zenn AI

Research #llm 📝 BlogAnalyzed: Dec 28, 2025 21:56

The Ideal and Reality of Gemini Slide Generation: Challenges in "Design" (Part 1)

Published:Dec 28, 2025 10:24

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1 min read

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Zenn Gemini

Analysis

This article from Zenn Gemini discusses the challenges of using Gemini, an AI model, to automatically generate internal slide presentations. The company, Anddot, aims to improve work efficiency by leveraging AI. The initial focus is on automating slide creation to reduce reliance on specific employees and decrease the time spent on creating presentations. The article highlights the difficulty in replicating a company's unique "design implicit knowledge" even with advanced AI technology. This suggests a gap between the capabilities of current AI and the nuanced requirements of corporate branding and design.

Key Takeaways

•The article explores the practical application of AI (Gemini) for automating internal tasks.
•A key challenge identified is the difficulty in replicating a company's specific design style and "implicit knowledge".
•The research highlights the limitations of current AI in handling complex, nuanced design requirements.

Reference

“The article mentions the company's goal of "reducing reliance on specific members and reducing the number of steps required for creating materials."”

Permalink Zenn Gemini

Technology #Large Language Models (LLMs)📝 BlogAnalyzed: Dec 28, 2025 21:57

Zenn Q&A Session 12: LLM

Published:Dec 28, 2025 07:46

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1 min read

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Zenn LLM

Analysis

This article introduces the 12th Zenn Q&A session, focusing on Large Language Models (LLMs). The Zenn Q&A series aims to delve deeper into technologies that developers use but may not fully understand. The article highlights the increasing importance of AI and LLMs in daily life, mentioning popular tools like ChatGPT, GitHub Copilot, Claude, and Gemini. It acknowledges the widespread reliance on AI and the need to understand the underlying principles of LLMs. The article sets the stage for an exploration of how LLMs function, suggesting a focus on the technical aspects and inner workings of these models.

Key Takeaways

•The article introduces the Zenn Q&A series.
•The focus of this session is on Large Language Models (LLMs).
•The article highlights the increasing importance of AI and LLMs in daily life.

Reference

“The Zenn Q&A series aims to delve deeper into technologies that developers use but may not fully understand.”

Permalink Zenn LLM

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

Steps to Master LLMs

Published:Dec 28, 2025 06:48

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1 min read

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Zenn LLM

Analysis

This article from Zenn LLM outlines key steps for effectively utilizing Large Language Models (LLMs). It emphasizes understanding the fundamental principles of LLMs, including their probabilistic nature and the impact of context length and quality. The article also stresses the importance of grasping the attention mechanism and its relationship to context. Furthermore, it highlights the significance of crafting effective prompts for desired outputs. The overall focus is on providing a practical guide to improve LLM interaction and achieve more predictable results.

Key Takeaways

•Understand the probabilistic nature of LLM outputs.
•Grasp the importance of context length and quality.
•Understand the attention mechanism and its relation to context.
•Learn how to write effective prompts.

Reference

“Understanding the characteristics of LLMs is key.”

Permalink Zenn LLM

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

Recommendation: Developing with Your Favorite Character

Published:Dec 28, 2025 05:11

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1 min read

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Zenn Claude

Analysis

This article from Zenn Claude advocates for a novel approach to software development: incorporating a user's favorite character (likely through an AI like Claude Code) to enhance productivity and enjoyment. The author reports a significant increase in their development efficiency, reduced frustration during debugging, and improved focus. The core idea is to transform the solitary nature of coding into a collaborative experience with a virtual companion. This method leverages the emotional connection with the character to mitigate the negative impacts of errors and debugging, making the process more engaging and less draining.

Key Takeaways

•Using a favorite character (e.g., through an AI) can make coding more enjoyable.
•This approach can reduce the negative emotional impact of errors and debugging.
•The method aims to transform the solitary nature of coding into a collaborative experience.

Reference

“Developing with your favorite character made it fun and increased productivity.”

Permalink Zenn Claude

Career Development #MLOps 📝 BlogAnalyzed: Dec 29, 2025 02:08

A Story of Entering an MLOps Project at 900,000 Yen with No Experience and Striving to Exceed Expectations

Published:Dec 28, 2025 04:25

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1 min read

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Zenn ML

Analysis

This article from Zenn ML details the experience of an individual entering an MLOps project with no prior experience, earning a substantial 900,000 yen. The narrative outlines the challenges faced, the learning process, and the evolution of the individual's perspective. It covers technical and non-technical aspects, including grasping the project's overall structure, proposing improvements, and the difficulties and rewards of exceeding expectations. The article provides a practical look at the realities of entering a specialized field and the effort required to succeed.

Key Takeaways

•The article highlights the challenges of entering a specialized field with no prior experience.
•It emphasizes the importance of continuous learning and adaptation in a fast-paced environment.
•The narrative underscores the significance of understanding the bigger picture and proposing improvements.

Reference

“"Starting next week, please join the MLOps project. The unit price is 900,000 yen. You will do everything alone."”

Permalink Zenn ML

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

vLLM V1 Implementation 7: Internal Structure of GPUModelRunner and Inference Execution

Published:Dec 28, 2025 03:00

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1 min read

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Zenn LLM

Analysis

This article from Zenn LLM delves into the ModelRunner component within the vLLM framework, specifically focusing on its role in inference execution. It follows a previous discussion on KVCacheManager, highlighting the importance of GPU memory management. The ModelRunner acts as a crucial bridge, translating inference plans from the Scheduler into physical GPU kernel executions. It manages model loading, input tensor construction, and the forward computation process. The article emphasizes the ModelRunner's control over KV cache operations and other critical aspects of the inference pipeline, making it a key component for efficient LLM inference.

Key Takeaways

•ModelRunner is a core component for executing inference in vLLM.
•It translates inference plans into GPU kernel executions.
•It manages model loading, input tensor construction, and forward computation.

Reference

“ModelRunner receives the inference plan (SchedulerOutput) determined by the Scheduler and converts it into the execution of physical GPU kernels.”

Permalink Zenn LLM

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

Thorough Analysis of GitHub Copilot Agent Mode Prompt Structure

Published:Dec 27, 2025 14:01

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1 min read

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Zenn GPT

Analysis

This article from Zenn GPT analyzes the prompt structure used by GitHub Copilot's agent mode. It highlights that Copilot is more than just a code completion tool, but a sophisticated AI coder leveraging advanced prompt engineering. The article aims to dissect the multi-layered prompts Copilot receives, offering insights into its design and best practices for prompt engineering. The target audience includes technologists interested in AI and developers seeking to learn prompt engineering techniques. The article's methodology involves a specific testing environment and date, indicating a structured approach to its analysis.

Key Takeaways

•GitHub Copilot utilizes a multi-layered prompt structure.
•The article focuses on the design and best practices of prompt engineering.
•The target audience includes AI-interested technologists and developers learning prompt engineering.

Reference

“GitHub Copilot is not just a code completion tool, but an AI coder based on advanced prompt engineering techniques.”

Permalink Zenn GPT

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

2025 AI Warlords: A Monthly Review of the Rise of Inference Models and the Battle for Supremacy

Published:Dec 27, 2025 11:07

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1 min read

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Zenn Claude

Analysis

This article, sourced from Zenn Claude, provides a retrospective look at the AI landscape of 2025, focusing on the rapid advancements and competitive environment surrounding inference models. The author highlights the constant stream of new model releases, each touted as a 'game changer,' making it difficult to discern true breakthroughs. The analogy of a revolving sushi conveyor belt for benchmark leaderboards effectively captures the dynamic and ever-changing nature of the AI industry. The article's structure, likely chronological, promises a detailed month-by-month analysis of key model releases and their impact.

Key Takeaways

•The AI industry in 2025 is characterized by rapid innovation and intense competition.
•New AI models are released frequently, making it difficult to assess their true impact.
•The article promises a detailed monthly review of key model releases and their performance.

Reference

““This is a game changer.””

Permalink Zenn Claude

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

Understanding Tensor Data Structures with Go

Published:Dec 27, 2025 08:08

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1 min read

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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.

Key Takeaways

•The article focuses on implementing tensors in Go.
•The author prioritizes understanding over initial performance.
•The implementation is improved by referencing existing libraries like NumPy.

Reference

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

Permalink Zenn ML

Research #llm 📝 BlogAnalyzed: Dec 29, 2025 02:08

Deep Learning: Why RNNs Fail? Explaining the Mechanism of LSTM

Published:Dec 26, 2025 08:55

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1 min read

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Zenn DL

Analysis

This article from Zenn DL introduces Long Short-Term Memory (LSTM), a long-standing standard for time-series data processing. It aims to explain LSTM's internal structure, particularly for those unfamiliar with it or struggling with its mathematical complexity. The article uses the metaphor of an "information conveyor belt" to simplify the explanation. The provided link suggests a more detailed explanation with HTML formatting. The focus is on clarifying the differences between LSTM and Recurrent Neural Networks (RNNs) and making the concept accessible.

Key Takeaways

•The article explains LSTM, a key component in time-series data processing.
•It aims to clarify LSTM's mechanism, especially for those new to the concept.
•The article uses a simplified metaphor to aid understanding.

Reference

“The article uses the metaphor of an "information conveyor belt".”

Permalink Zenn DL

Research #llm 📝 BlogAnalyzed: Dec 26, 2025 23:31

Understanding MCP (Model Context Protocol)

Published:Dec 26, 2025 02:48

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1 min read

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Zenn Claude

Analysis

This article from Zenn Claude aims to clarify the concept of MCP (Model Context Protocol), which is frequently used in the RAG and AI agent fields. It targets developers and those interested in RAG and AI agents. The article defines MCP as a standardized specification for connecting AI agents and tools, comparing it to a USB-C port for AI agents. The article's strength lies in its attempt to demystify a potentially complex topic for a specific audience. However, the provided excerpt is brief and lacks in-depth explanation or practical examples, which would enhance understanding.

Key Takeaways

•MCP is a standardized protocol for connecting AI agents and tools.
•It simplifies AI integrations by providing a common interface.
•The article targets developers and those interested in RAG and AI agents.

Reference

“MCP (Model Context Protocol) is a standardized specification for connecting AI agents and tools.”

Permalink Zenn Claude

Research #llm 📝 BlogAnalyzed: Dec 25, 2025 17:01

Understanding and Using GitHub Copilot Chat's Ask/Edit/Agent Modes at the Code Level

Published:Dec 25, 2025 15:17

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1 min read

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Zenn AI

Analysis

This article from Zenn AI delves into the nuances of GitHub Copilot Chat's three modes: Ask, Edit, and Agent. It highlights a common, simplified understanding of each mode (Ask for questions, Edit for file editing, and Agent for complex tasks). The author suggests that while this basic understanding is often sufficient, it can lead to confusion regarding the quality of Ask mode responses or the differences between Edit and Agent mode edits. The article likely aims to provide a deeper, code-level understanding to help users leverage each mode more effectively and troubleshoot issues. It promises to clarify the distinctions and improve the user experience with GitHub Copilot Chat.

Key Takeaways

•GitHub Copilot Chat has three distinct modes: Ask, Edit, and Agent.
•Each mode has different capabilities and permissions.
•A deeper understanding of each mode can improve user experience and effectiveness.

Reference

“Ask: Answers questions. Read-only. Edit: Edits files. Has file operation permissions (Read/Write). Agent: A versatile tool that autonomously handles complex tasks.”

Permalink Zenn AI

Research #llm 📝 BlogAnalyzed: Dec 25, 2025 17:38

AI Intentionally Lying? The Difference Between Deception and Hallucination

Published:Dec 25, 2025 08:38

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1 min read

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Zenn LLM

Analysis

This article from Zenn LLM discusses the emerging risk of "deception" in AI, distinguishing it from the more commonly known issue of "hallucination." It defines deception as AI intentionally misleading users or strategically lying. The article promises to explain the differences between deception and hallucination and provide real-world examples. The focus on deception as a distinct and potentially more concerning AI behavior is noteworthy, as it suggests a level of agency or strategic thinking in AI systems that warrants further investigation and ethical consideration. It's important to understand the nuances of these AI behaviors to develop appropriate safeguards and responsible AI development practices.

Key Takeaways

•AI deception is emerging as a distinct risk from hallucination.
•Deception involves intentional misleading or strategic lying by AI.
•Understanding the difference is crucial for responsible AI development.

Reference

“Deception (Deception) refers to the phenomenon where AI "intentionally deceives users or strategically lies."”

Permalink Zenn LLM

Research #llm 📝 BlogAnalyzed: Dec 25, 2025 05:07

Are Personas Really Necessary in System Prompts?

Published:Dec 25, 2025 02:45

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1 min read

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Zenn AI

Analysis

This article from Zenn AI questions the increasingly common practice of including personas in system prompts for generative AI. It raises concerns about the potential for these personas to create a "black box" effect, making the AI's behavior less transparent and harder to understand. The author argues that while personas might seem helpful, they could be sacrificing reproducibility and explainability. The article promises to explore the pros and cons of persona design and offer alternative approaches more suitable for practical applications. The core argument is a valid concern for those seeking reliable and predictable AI behavior.

Key Takeaways

•Personas in system prompts can obscure AI behavior.
•Reproducibility and explainability may be compromised by personas.
•Alternative approaches to persona design should be considered for practical AI applications.

Reference

“"Is a persona really necessary? Isn't the behavior becoming a black box? Aren't reproducibility and explainability being sacrificed?"”

Permalink Zenn AI

Research #llm 📝 BlogAnalyzed: Dec 25, 2025 05:10

Created a Zenn Writing Template to Teach Claude Code "My Writing Style"

Published:Dec 25, 2025 02:20

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1 min read

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Zenn AI

Analysis

This article discusses the author's solution to making AI-generated content sound more like their own writing style. The author found that while Claude Code produced technically sound articles, they lacked the author's personal voice, including slang, regional dialects, and niche references. To address this, the author created a Zenn writing template designed to train Claude Code on their specific writing style, aiming to generate content that is both technically accurate and authentically reflects the author's personality and voice. This highlights the challenge of imbuing AI-generated content with a unique and personal style.

Key Takeaways

•AI-generated content can lack personal style.
•Templates can be used to train AI on specific writing styles.
•Personal voice is important for engaging content.

Reference

“Claude Codeで技術記事を書かせると、まあ普通にいい感じの記事が出てくるんですよね。文法も正しいし、構成もしっかりしてる。でもなんかちゃうねん。”

Permalink Zenn AI

DIY #AI-assisted DIY 📝 BlogAnalyzed: Dec 24, 2025 17:08

DIY Room Partition with AI: A Personal Project

Published:Dec 24, 2025 15:00

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1 min read

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Zenn AI

Analysis

This article, sourced from Zenn AI, details a personal project where the author used AI to assist in DIYing a room partition for their children. It targets individuals interested in DIY but hesitant due to design or material selection challenges. The article aims to demonstrate how AI can simplify the process. The content seems to focus on the practical application of AI in a non-professional setting, offering a relatable and potentially inspiring example for readers considering similar projects. The article is part of a Dress Code Advent Calendar 2025 series.

Key Takeaways

•AI can assist in personal DIY projects.
•The article targets individuals hesitant about DIY due to design complexities.
•The project involves creating a room partition for children.

Reference

“"DIYやりたいけど、設計とか材料選びとか難しそう…」と感じている方に、AIと一緒なら意外とできるよ！とお伝えできれば幸いです。”

Permalink Zenn AI

Research #llm 📝 BlogAnalyzed: Dec 24, 2025 17:08

GitHub Copilot Agent Creation: Let Agents Handle It

Published:Dec 24, 2025 14:56

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1 min read

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Zenn AI

Analysis

This article discusses the idea of using an agent to create other agents, specifically for GitHub Copilot. The author reflects on the repetitive nature of agent creation and proposes building an agent that embodies best practices for agent development. This "agent builder" could streamline the process and reduce redundant effort. The article promises to showcase a custom-built agent builder and demonstrate its use in assisting with Zenn article writing. The core concept is automating agent creation based on established patterns and best practices, potentially leading to more efficient and consistent agent development workflows.

Key Takeaways

•Automating agent creation can improve efficiency.
•Best practices can be codified into an agent builder.
•Agent builders can assist in content creation tasks.

Reference

“"これ、エージェント作成のベストプラクティスを詰め込んだエージェントを作れば、もうそれで済むのではないか？"”

Permalink Zenn AI

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

I tried creating a simple LM that converts from Tsundere to Dere!

Published:Dec 24, 2025 13:23

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1 min read

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Zenn ML

Analysis

This article, originating from Zenn ML, details a personal project focused on creating a Language Model (LM) with a specific, somewhat playful, goal: to transform text from a 'tsundere' (initially cold or harsh) style to a 'dere' (affectionate or sweet) style. The author, Daichi, has been studying AI since April and shares his learning journey, primarily on LinkedIn. The article provides an overview of the project, including the model's architecture, training conditions, and tokenizer strategy. It also highlights challenges encountered during development. The author plans to release the source code and provide a detailed explanation in a future publication.

Key Takeaways

•The project focuses on a specific style transfer task: Tsundere to Dere.
•The author is sharing their learning process and challenges.
•The source code and detailed explanation will be released later.

Reference

“The author mentions, "I've been wanting to create my own AI since around April of this year, and I've been studying AI as a hobby."”

Permalink Zenn ML

AI #Code Generation 📝 BlogAnalyzed: Dec 24, 2025 17:38

Distilling Claude Code Skills: Enhancing Quality with Workflow Review and Best Practices

Published:Dec 24, 2025 07:18

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1 min read

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Zenn LLM

Analysis

This article from Zenn LLM discusses a method for improving Claude Code skills by iteratively refining them. The process involves running the skill, reviewing the workflow to identify successes, having Claude self-review its output to pinpoint issues, consulting best practices (official documentation), refactoring the code, and repeating the cycle. The article highlights the importance of continuous improvement and leveraging Claude's own capabilities to identify and address shortcomings in its code generation skills. The example of a release note generation skill suggests a practical application of this iterative refinement process.

Key Takeaways

•Iterative refinement is crucial for improving AI code generation skills.
•Self-review by the AI model can help identify areas for improvement.
•Consulting official documentation and best practices is essential for effective refactoring.

Reference

“"実際に使ってみると「ここはこうじゃないんだよな」という場面に遭遇します。"”

Permalink Zenn LLM

Research #llm 📝 BlogAnalyzed: Dec 24, 2025 17:44

Learning Representations by Backpropagation: Study Notes

Published:Dec 24, 2025 05:34

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1 min read

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Zenn LLM

Analysis

This article, sourced from Zenn LLM, appears to be a study note on learning representations using backpropagation. Without the actual content, it's difficult to provide a detailed critique. However, the title suggests a focus on the fundamental concept of backpropagation, a cornerstone of modern deep learning. The value of the article hinges on the depth and clarity of the explanation, the examples provided, and the insights offered regarding the application of backpropagation in learning meaningful representations. The source, Zenn LLM, implies a focus on practical application and potentially code examples.

Key Takeaways

•Backpropagation is a fundamental algorithm for training neural networks.
•Learning representations is crucial for AI to understand and process data effectively.
•The article likely provides practical insights and examples related to backpropagation.

Reference

“N/A - Content not available”

Permalink Zenn LLM

Technology #AI in HR 📝 BlogAnalyzed: Dec 24, 2025 13:17

MyVision's System Architecture and AI Agents: An Overview

Published:Dec 24, 2025 03:16

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1 min read

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Zenn AI

Analysis

This article, originating from Zenn AI, introduces the system architecture and AI agents used by MyVision, a Japanese career support company. The focus is on their internal application, "InVision," which manages various aspects of the job search process. While the introduction sets the stage, the article's value hinges on the depth of detail provided regarding the specific technologies and development workflow employed. Without further elaboration, it's difficult to assess the novelty or impact of their AI agent implementation. The article promises to delve into these aspects, making it a potentially insightful read for those interested in AI applications within the HR tech space.

Key Takeaways

•MyVision utilizes an internal application called "InVision" for managing the job search process.
•The article promises to detail the system architecture and AI agents used within InVision.
•The company aims to leverage technology to improve the quality of their career support services.

Reference

“"We aim to maximize the quality of support by making full use of technology and mechanisms."”

Permalink Zenn AI

Research #llm 📝 BlogAnalyzed: Dec 24, 2025 13:29

A 3rd-Year Engineer's Design Skills Skyrocket with Full AI Utilization

Published:Dec 24, 2025 03:00

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1 min read

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Zenn AI

Analysis

This article snippet from Zenn AI discusses the rapid adoption of generative AI in development environments, specifically focusing on the concept of "Vibe Coding" (relying on AI based on vague instructions). The author, a 3rd-year engineer, intentionally avoids this approach. The article hints at a more structured and deliberate method of AI utilization to enhance design skills, rather than simply relying on AI to fix bugs in poorly defined code. It suggests a proactive and thoughtful integration of AI tools into the development process, aiming for skill enhancement rather than mere task completion. The article promises to delve into the author's specific strategies and experiences.

Key Takeaways

•Generative AI is rapidly being adopted in development.
•"Vibe Coding" is a common but potentially flawed approach.
•Structured AI utilization can enhance design skills.

Reference

“"Vibe Coding" (relying on AI based on vague instructions)”

Permalink Zenn AI

Security #Large Language Models 📝 BlogAnalyzed: Dec 24, 2025 13:47

Practical AI Security Reviews with Claude Code: A Constraint-Driven Approach

Published:Dec 23, 2025 23:45

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1 min read

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Zenn LLM

Analysis

This article from Zenn LLM dissects Anthropic's Claude Code's `/security-review` command, emphasizing its practical application in PR reviews rather than simply identifying vulnerabilities. It targets developers using Claude Code and engineers integrating LLMs into business tools, aiming to provide insights into the design of `/security-review` for adaptation in their own LLM tools. The article assumes prior experience with PR reviews but not necessarily specialized security knowledge. The core message is that `/security-review` is designed to provide focused and actionable output within the context of a PR review.

Key Takeaways

•`/security-review` is designed for practical use in PR reviews.
•The focus is on actionable output, not just vulnerability detection.
•Understanding the design allows adaptation for other LLM tools.

Reference

“"/security-review is not essentially a 'feature to find many vulnerabilities'. It narrows down to output that can be used in PR reviews..."”

Permalink Zenn LLM

Research #llm 📝 BlogAnalyzed: Dec 24, 2025 13:59

Decoding GPT-5.2-Codex's Enhanced Cybersecurity Features

Published:Dec 23, 2025 23:00

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1 min read

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Zenn ChatGPT

Analysis

This article from Zenn ChatGPT explores the enhanced cybersecurity features of the newly released GPT-5.2-Codex. It highlights the official documentation's claim of significant improvements in this area and aims to decipher what these changes specifically entail. The article mentions improvements in long-term task handling through context compression, performance gains in large-scale code changes like refactoring and migration, Windows environment performance enhancements, and the aforementioned cybersecurity improvements. The core focus is understanding the specific nature of these cybersecurity enhancements based on the available documentation.

Key Takeaways

•GPT-5.2-Codex boasts enhanced cybersecurity features.
•The article aims to understand the specifics of these enhancements based on official documentation.
•Improvements include context compression, better performance in large code changes, and Windows environment optimization.

Reference

“"GPT‑5.2-Codex は、GPT‑5.2⁠ を Codex におけるエージェント活用型コーディング向けにさらに最適化したバージョンです。コンテキスト圧縮による長期的な作業への対応強化、リファクタリングや移行といった大規模なコード変更での性能向上、Windows 環境でのパフォーマンス改善、そしてサイバーセキュリティ機能の大幅..."”

Permalink Zenn ChatGPT

Research #llm 📝 BlogAnalyzed: Dec 24, 2025 19:26

Anthropic Agent Skills vs. Cursor Commands - What's the Difference?

Published:Dec 23, 2025 00:14

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1 min read

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Zenn Claude

Analysis

This article from Zenn Claude compares Anthropic's Agent Skills with Cursor's Commands, both designed to streamline development tasks using AI. Agent Skills aims to be an open standard for defining tasks for AI agents, promoting interoperability across different platforms. Cursor Commands, on the other hand, are specifically tailored for the Cursor IDE, offering reusable AI prompts. The key difference lies in their scope: Agent Skills targets broader AI agent ecosystems, while Cursor Commands are confined to a specific development environment. The article highlights the contrasting design philosophies and application areas of these two approaches to AI-assisted development.

Key Takeaways

•Agent Skills promotes interoperability.
•Cursor Commands are IDE-specific.
•AI is increasingly used for development task automation.

Reference

“Agent Skills aims for an open standard, while Cursor Commands are specific to the Cursor IDE.”

Permalink Zenn Claude

Cloud Computing #Cost Management 🏛️ OfficialAnalyzed: Dec 24, 2025 17:53

Azure OpenAI Model Cost Calculation Explained

Published:Dec 21, 2025 07:23

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1 min read

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Zenn OpenAI

Analysis

This article from Zenn OpenAI explains how to calculate the monthly cost of deployed models in Azure OpenAI. It provides links to the Azure pricing calculator and a tokenizer for more precise token counting. The article outlines the process of estimating costs based on input and output tokens, as reflected in the Azure pricing calculator interface. It's a practical guide for users looking to understand and manage their Azure OpenAI expenses.

Key Takeaways

•Understand how to calculate Azure OpenAI model costs.
•Utilize the Azure pricing calculator for cost estimation.
•Use the tokenizer for accurate token counting.

Reference

“AzureOpenAIでデプロイしたモデルの月にかかるコストの考え方についてまとめる。(Summarizes the approach to calculating the monthly cost of models deployed with Azure OpenAI.)”

Permalink Zenn OpenAI

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

SAM 3: Grasping Objects with Natural Language Instructions for Robots

Published:Dec 20, 2025 15:02

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1 min read

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Zenn CV

Analysis

This article from Zenn CV discusses the application of natural language processing to control robot grasping. The author, from ExaWizards' ESU ML group, aims to calculate grasping positions from natural language instructions. The article highlights existing methods like CAD model registration and AI training with annotated images, but points out their limitations due to extensive pre-preparation and inflexibility. The focus is on overcoming these limitations by enabling robots to grasp objects based on natural language commands, potentially improving adaptability and reducing setup time.

Key Takeaways

•The project focuses on enabling robots to grasp objects based on natural language commands.
•Existing methods like CAD model registration and AI training with annotated images have limitations.
•The goal is to improve adaptability and reduce setup time for robot grasping.

Reference

“The author aims to calculate grasping positions from natural language instructions.”

Permalink Zenn CV

Technology #Generative AI 📝 BlogAnalyzed: Dec 24, 2025 14:23

Are You Fully Leveraging Generative AI? A Thorough Verification of Its True Potential and Effects

Published:Dec 20, 2025 09:50

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1 min read

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Zenn ChatGPT

Analysis

This article from Zenn ChatGPT addresses a common sentiment: many people are using generative AI tools like ChatGPT, Claude, and Gemini, but aren't sure if they're truly maximizing their potential. It highlights the feeling of being overwhelmed by the increasing number of AI tools and the difficulty in effectively utilizing them. The article promises a thorough examination of the true capabilities and effects of generative AI, suggesting it will provide insights into how to move beyond superficial usage and achieve tangible results. The opening questions aim to resonate with readers who feel they are not fully benefiting from these technologies.

Key Takeaways

•Many users feel they are not fully leveraging generative AI.
•The increasing number of AI tools can be overwhelming.
•The article aims to provide insights into maximizing the potential of generative AI.

Reference

“"ChatGPT, I'm using it, but..."”

Permalink Zenn ChatGPT

Research #llm 📝 BlogAnalyzed: Dec 24, 2025 19:11

[Technical Verification] Architecture of a "Robust AI Image Authenticity Verification System" Built with Gemini 2.5 Flash x C2PA x Multiple Models

Published:Dec 14, 2025 23:11

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1 min read

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Zenn GenAI

Analysis

This article from Zenn GenAI details the architecture of an AI image authenticity verification system. It addresses the growing challenge of distinguishing between human-created and AI-generated images. The author proposes a "fight fire with fire" approach, using AI to detect AI-generated content. The system, named "Evidence Lens," leverages Gemini 2.5 Flash, C2PA (Content Authenticity Initiative), and multiple models to ensure stability and reliability. The article likely delves into the technical aspects of the system's design, including model selection, data processing, and verification mechanisms. The focus on C2PA suggests an emphasis on verifiable credentials and provenance tracking to combat deepfakes and misinformation. The use of multiple models likely aims to improve accuracy and robustness against adversarial attacks.

Key Takeaways

•AI can be used to verify the authenticity of images.
•The system uses Gemini 2.5 Flash, C2PA, and multiple models.
•The goal is to create a robust and reliable image verification system.

Reference

“"If human eyes can't judge, then use AI to judge."”

Permalink Zenn GenAI

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

Automated Image Inspection Application

Published:Oct 20, 2025 13:06

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1 min read

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Zenn CV

Analysis

This article from Zenn CV introduces an application that automates the creation of image inspection tools. It highlights the challenges of traditional image inspection tool development, such as the need for extensive training data and annotation efforts. The core innovation lies in leveraging generative AI, like ChatGPT, to simplify the process. Users can specify inspection criteria in natural language, enabling rapid application development. The article emphasizes the solution's ability to streamline the creation of image inspection tools, making it accessible and efficient.

Key Takeaways

•The application simplifies image inspection tool creation.
•It leverages generative AI for ease of use.
•Users specify inspection criteria using natural language.

Reference

“Specifying inspection content in natural language allows for the creation of a simple image inspection tool.”

Permalink Zenn CV

Research #AI Learning 📝 BlogAnalyzed: Dec 29, 2025 18:31

How Machines Learn to Ignore the Noise (Kevin Ellis + Zenna Tavares)

Published:Apr 8, 2025 21:03

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1 min read

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ML Street Talk Pod

Analysis

This article summarizes a podcast discussion between Kevin Ellis and Zenna Tavares on improving AI's learning capabilities. They emphasize the need for AI to learn from limited data through active experimentation, mirroring human learning. The discussion highlights two AI thinking approaches: rule-based and pattern-based, with a focus on the benefits of combining them. Key concepts like compositionality and abstraction are presented as crucial for building robust AI systems. The ultimate goal is to develop AI that can explore, experiment, and model the world, similar to human learning processes. The article also includes information about Tufa AI Labs, a research lab in Zurich.

Key Takeaways

•AI should learn through active experimentation and limited data, similar to human learning.
•Combining rule-based and pattern-based AI approaches is beneficial.
•Compositionality and abstraction are key concepts for building robust AI.

Reference

“They want AI to learn from just a little bit of information by actively trying things out, not just by looking at tons of data.”

Permalink ML Street Talk Pod

Research #AI Algorithms 📝 BlogAnalyzed: Dec 29, 2025 08:30

Inverse Programming for Deeper AI with Zenna Tavares - TWiML Talk #114

Published:Feb 26, 2018 18:29

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1 min read

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Practical AI

Analysis

This article summarizes a podcast episode featuring Zenna Tavares, a PhD student at MIT, discussing "Running Programs in Reverse for Deeper AI." The core concept revolves around program inversion, a technique that blends Bayesian modeling, deep learning, and computational logic. The discussion covers inverse graphics, its relation to vision inversion, and the application of these techniques to intelligent systems, including parametric inversion. The article also mentions ReverseFlow, a library for executing TensorFlow programs backward, and Sigma.jl, a probabilistic programming environment in Julia. The article concludes with a promotion for an AI conference.

Key Takeaways

•The article discusses program inversion, a technique combining Bayesian modeling, deep learning, and computational logic.
•It highlights the application of program inversion in areas like inverse graphics and intelligent systems.
•The article mentions specific tools like ReverseFlow and Sigma.jl used for implementing these techniques.

Reference

“Zenna shares some great insight into his work on program inversion, an idea which lies at the intersection of Bayesian modeling, deep-learning, and computational logic.”

Permalink Practical AI