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

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

Published:Jan 16, 2026 16:06

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1 min read

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Slashdot

Analysis

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

Key Takeaways

•37 Signals' Kanban-inspired product, Fizzy, relies heavily on human-written code, demonstrating a hands-on approach.
•The team experimented with AI-powered features but ultimately chose to prioritize human-centric coding.
•Hansson observes a significant bet being placed on AI advancements by the US economy.

Reference

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

Permalink Slashdot

Software Development #LLM Integration 📝 BlogAnalyzed: Dec 24, 2025 13:32

Building LLM Services with Rails: The OpenCode Server Option

Published:Dec 24, 2025 01:54

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1 min read

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Zenn LLM

Analysis

This article highlights the challenges of using Ruby and Rails for LLM-based services due to the relatively underdeveloped AI/LLM ecosystem compared to Python and TypeScript. It introduces OpenCode Server as a solution, abstracting LLM interactions via HTTP API, enabling language-agnostic LLM functionality. The article points out the lag in Ruby's support for new models and providers, making OpenCode Server a potentially valuable tool for Ruby developers seeking to integrate LLMs into their Rails applications. Further details on OpenCode's architecture and performance would strengthen the analysis.

Key Takeaways

•Ruby's LLM ecosystem is less mature than Python/TypeScript.
•OpenCode Server abstracts LLM interactions via HTTP API.
•OpenCode Server enables language-agnostic LLM functionality for Ruby/Rails.

Reference

“LLMとのやりとりをHTTP APIで抽象化し、言語を選ばずにLLM機能を利用できる仕組みを提供してくれる。”

Permalink Zenn LLM

Product #LLM, Code 👥 CommunityAnalyzed: Jan 10, 2026 14:52

LLM-Powered Code Repair: Addressing Ruby's Potential Errors

Published:Oct 24, 2025 12:44

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1 min read

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Hacker News

Analysis

The article likely discusses a new tool leveraging Large Language Models (LLMs) to identify and rectify errors in Ruby code. The focus on a 'billion dollar mistake' suggests the tool aims to address significant and potentially costly coding flaws within the Ruby ecosystem.

Key Takeaways

•The product uses LLMs for automated code repair in Ruby.
•It addresses potentially costly errors, framed as a significant problem.
•The context is a Hacker News Show HN post, indicating early-stage product visibility.

Reference

“Fixing the billion dollar mistake in Ruby.”

Permalink Hacker News

Technology #Programming 📝 BlogAnalyzed: Dec 29, 2025 09:41

DHH on Programming, AI, Ruby on Rails, and More

Published:Jul 12, 2025 17:16

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1 min read

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Lex Fridman Podcast

Analysis

This article summarizes a podcast episode featuring David Heinemeier Hansson (DHH), the creator of Ruby on Rails and co-owner of 37signals. The episode covers a range of topics, including the future of programming, AI, and DHH's work on Ruby on Rails. It also touches upon his views on productivity, parenting, and his other interests like race car driving. The article provides links to the podcast transcript, DHH's social media, and the sponsors of the episode. The outline suggests the conversation delves into DHH's early programming experiences, JavaScript, Google Chrome, and the Ruby programming language.

Key Takeaways

•DHH is a prominent figure in the programming world, known for Ruby on Rails.
•The podcast episode covers a wide range of topics, including AI and the future of programming.
•The episode provides insights into DHH's perspectives on various subjects beyond programming.

Reference

“The article doesn't contain a direct quote, but it highlights the topics discussed, such as programming, AI, and Ruby on Rails.”

Permalink Lex Fridman Podcast

Research #llm 👥 CommunityAnalyzed: Jan 3, 2026 08:42

RubyLLM: A delightful Ruby way to work with AI

Published:Mar 11, 2025 12:40

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1 min read

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Hacker News

Analysis

The article introduces RubyLLM, a library or framework that simplifies the interaction with AI models using the Ruby programming language. The focus is on the ease of use and the enjoyable experience it provides for Ruby developers.

Key Takeaways

•RubyLLM provides a Ruby-centric approach to interacting with AI.
•The article emphasizes the 'delightful' experience, suggesting ease of use and developer-friendliness.
•The focus is on making AI accessible and enjoyable for Ruby developers.

Reference

“”

Permalink Hacker News

Research #llm 👥 CommunityAnalyzed: Jan 4, 2026 09:25

Running Open-Source AI Models Locally with Ruby

Published:Feb 5, 2024 07:41

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1 min read

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Hacker News

Analysis

This article likely discusses the technical aspects of using Ruby to interact with and run open-source AI models on a local machine. It would probably cover topics like setting up the environment, choosing appropriate Ruby libraries, and the practical challenges and benefits of this approach. The focus is on the implementation details and the advantages of local execution, such as data privacy and potentially lower costs compared to cloud-based services.

Key Takeaways

•Explores the use of Ruby for local AI model execution.
•Focuses on practical implementation details.
•Highlights benefits like data privacy and cost savings.

Reference

“”

Permalink Hacker News

Development #AI Assistant 👥 CommunityAnalyzed: Jan 3, 2026 09:47

Creating AI assistant with GPT and Ruby and Redis using embeddings

Published:Apr 26, 2023 18:02

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1 min read

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Hacker News

Analysis

The article describes a technical project involving the development of an AI assistant. It highlights the use of GPT (likely referring to OpenAI's GPT models), Ruby programming language, Redis for data storage, and embeddings for representing information. The focus is on the practical application of these technologies to build an AI-powered application.

Key Takeaways

•Demonstrates a practical application of LLMs (Large Language Models) like GPT.
•Utilizes Ruby and Redis, showcasing a specific technology stack.
•Emphasizes the use of embeddings, a key technique for semantic understanding and information retrieval in AI applications.

Reference

“”

Permalink Hacker News

Software Development #Machine Learning 👥 CommunityAnalyzed: Jan 3, 2026 15:47

New Machine Learning Gems for Ruby

Published:Jun 16, 2021 08:48

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1 min read

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Hacker News

Analysis

The article announces the availability of new machine learning libraries (gems) for the Ruby programming language. This suggests advancements in the Ruby ecosystem for AI/ML development, potentially making it easier for Ruby developers to incorporate machine learning into their projects. The lack of detail in the summary makes it difficult to assess the specific impact or novelty of these gems.

Key Takeaways

•New machine learning libraries (gems) are available for Ruby.
•This could improve the ease of use of machine learning in Ruby projects.
•The specific features and impact are unclear from the summary.

Reference

“”

Permalink Hacker News

Technology #Fraud Detection 📝 BlogAnalyzed: Dec 29, 2025 08:37

Fighting Fraud with Machine Learning at Shopify with Solmaz Shahalizadeh - TWiML Talk #60

Published:Oct 30, 2017 19:54

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1 min read

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Practical AI

Analysis

This article summarizes a podcast episode featuring Solmaz Shahalizadeh, Director of Merchant Services Algorithms at Shopify. The episode discusses Shopify's transition from a rules-based fraud detection system to a machine learning-based system. The conversation covers project scope definition, feature selection, model choices, and the use of PMML to integrate Python models with a Ruby-on-Rails web application. The podcast provides insights into practical applications of machine learning in combating fraud and improving merchant satisfaction, offering valuable lessons for developers and data scientists.

Key Takeaways

•Shopify transitioned from a rules-based fraud detection system to a machine learning-based system.
•The podcast discusses the importance of well-defined project scope and feature selection.
•PMML is used to integrate Python machine learning models with a Ruby-on-Rails web application.

Reference

“Solmaz gave a great talk at the GPPC focused on her team’s experiences applying machine learning to fight fraud and improve merchant satisfaction.”

Permalink Practical AI

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

Analysis

Key Takeaways

Building LLM Services with Rails: The OpenCode Server Option

Analysis

Key Takeaways

LLM-Powered Code Repair: Addressing Ruby's Potential Errors

Analysis

Key Takeaways

DHH on Programming, AI, Ruby on Rails, and More

Analysis

Key Takeaways

RubyLLM: A delightful Ruby way to work with AI

Analysis

Key Takeaways

Running Open-Source AI Models Locally with Ruby

Analysis

Key Takeaways

Creating AI assistant with GPT and Ruby and Redis using embeddings

Analysis

Key Takeaways

New Machine Learning Gems for Ruby

Analysis

Key Takeaways

Fighting Fraud with Machine Learning at Shopify with Solmaz Shahalizadeh - TWiML Talk #60

Analysis

Key Takeaways

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