Explores how autonomous AI agents (autoresearch) can optimize small language models by running hundreds of experiments overnight, improving performance without human intervention.
A daily tech reading list covering SQL's resurgence, AI agent productivity, CI/CD at scale, Go for backend, and more industry insights.
A minimal demo of implementing and using AI Agent Skills in .NET, including prompt, Python, and C# based skills.
Explains how to import and run LangGraph agents within IBM's watsonx Orchestrate platform for building multi-agent AI systems.
A daily tech reading list covering AI agents in DevOps, software craftsmanship, GKE metrics, AI coding trade-offs, monorepos, and prompt engineering.
A guide to integrating Dremio's data platform with the Zed code editor for enhanced data querying, pipeline generation, and application development.
A daily tech reading list covering AI agents, software engineering trends, secure coding, and new developer tools like Gemini models and MCP SDKs.
A guide to systematically evaluating and testing AI agent skills, covering success criteria, building an evaluation harness, and improving skill performance.
A curated list of articles and blogs about AI agents, software engineering changes, API latency, and developer experience.
Explores using interactive explanations and animated visualizations to understand AI-generated code and reduce cognitive debt in software development.
A senior engineer contrasts disciplined, multi-agent AI workflows with basic AI-assisted coding, arguing that discipline is now the key differentiator in software development.
A skeptic's detailed journey using AI coding agents for complex projects, including porting scikit-learn to Rust, showcasing their surprising capabilities.
A skeptic's detailed journey using AI coding agents to port scikit-learn to Rust, showcasing the surprising capabilities of modern LLMs.
A daily tech reading list covering AI agents, cloud development, software engineering trends, and new tools like Gemini CLI and jQuery v4.
A developer details the process of building evaluation systems for two AI-powered developer tools to measure their real-world effectiveness.
Introduces Skill Eval, a TypeScript framework for testing and benchmarking AI coding agent skills to ensure reliability and correct behavior.
Discussion on MCP servers, apps, and WebMCP for AI agents to perform structured actions on websites.
Explains the difference between an AI agent's inner loop (verifying work within a task) and outer loop (learning across tasks).
Explains how token limits and context windows cause AI coding agents to fail, and offers techniques to keep them stable during long tasks.
A daily tech reading list covering AI agents, platform engineering, API docs, AI UX, and cloud performance trends.
