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Let AI Debug DynamoDB Without Giving It Your AWS Credentials
Our latest Quito Lambda showcases how to use LocalStack to test AI-assisted DynamoDB fixes locally. The session offers a practical approach to AI-assisted debugging: give the agent a repeatable test environment that exposes meaningful failures, and control where its commands can operate.
Design is Not Dead: The Designer as the AI Director
AI can generate interfaces faster than ever, but speed alone does not create usable, accessible, production-ready products. This blog explores why designers are becoming AI directors, guiding tools like Claude with the right systems, constraints, and human judgment.
Modernizing Legacy Software With Spec-Driven Development
Spec-Driven Development gives teams a structured way to use AI in legacy software modernization while preserving business requirements, technical constraints, and human oversight. By combining durable specifications with incremental delivery and measurable validation, teams can make AI-assisted modernization more reliable, controlled, and repeatable.
Massiv vs. Accelerate: Two Paths to High-Performance CPU Computing in Haskell
Programmers commonly use languages like Python, or C/C++ for those seeking higher performance, to handle array manipulation with libraries such as NumPy, SciPy, or deep learning frameworks like TensorFlow. Although Haskell is not a mainstream language, some developers favor its functional paradigm and strong typing. Importantly, Haskell offers its own libraries for high-performance array manipulation. This article will introduce two of the most popular Haskell libraries for this purpose, Massiv
Reproducible AI Development Environments With devenv
Stack Builders Senior Software Developer Cristhian Motoche demonstrates how devenv and Nix can make AI development environments reproducible by defining packages, services, scripts, hooks, and workflows as code. The session also explores the practical tradeoffs of local and cloud AI models.
Modernize, Replace, or Rewrite? How to Choose the Right First Step
Modernizing a legacy system does not always mean choosing between small fixes and a full rewrite. This guide shows how to identify the right first milestone, reduce risk, and build a clearer path toward improving, replacing, or rebuilding critical software.
Can AI Really Help Reduce Technical Debt? What Engineering Leaders Should Know
AI cannot safely clean up years of technical debt on its own. Its greatest value is helping engineering teams understand complex systems and identify where to begin.
Let AI Debug DynamoDB Without Giving It Your AWS Credentials
Our latest Quito Lambda showcases how to use LocalStack to test AI-assisted DynamoDB fixes locally. The session offers a practical approach to AI-assisted debugging: give the agent a repeatable test environment that exposes meaningful failures, and control where its commands can operate.
Design is Not Dead: The Designer as the AI Director
AI can generate interfaces faster than ever, but speed alone does not create usable, accessible, production-ready products. This blog explores why designers are becoming AI directors, guiding tools like Claude with the right systems, constraints, and human judgment.
Modernizing Legacy Software With Spec-Driven Development
Spec-Driven Development gives teams a structured way to use AI in legacy software modernization while preserving business requirements, technical constraints, and human oversight. By combining durable specifications with incremental delivery and measurable validation, teams can make AI-assisted modernization more reliable, controlled, and repeatable.
Massiv vs. Accelerate: Two Paths to High-Performance CPU Computing in Haskell
Programmers commonly use languages like Python, or C/C++ for those seeking higher performance, to handle array manipulation with libraries such as NumPy, SciPy, or deep learning frameworks like TensorFlow. Although Haskell is not a mainstream language, some developers favor its functional paradigm and strong typing. Importantly, Haskell offers its own libraries for high-performance array manipulation. This article will introduce two of the most popular Haskell libraries for this purpose, Massiv
Reproducible AI Development Environments With devenv
Stack Builders Senior Software Developer Cristhian Motoche demonstrates how devenv and Nix can make AI development environments reproducible by defining packages, services, scripts, hooks, and workflows as code. The session also explores the practical tradeoffs of local and cloud AI models.
Modernize, Replace, or Rewrite? How to Choose the Right First Step
Modernizing a legacy system does not always mean choosing between small fixes and a full rewrite. This guide shows how to identify the right first milestone, reduce risk, and build a clearer path toward improving, replacing, or rebuilding critical software.
Can AI Really Help Reduce Technical Debt? What Engineering Leaders Should Know
AI cannot safely clean up years of technical debt on its own. Its greatest value is helping engineering teams understand complex systems and identify where to begin.
