This quickstart guide provides a step-by-step walkthrough for building, testing, and deploying AI agents using the Amazon Bedrock AgentCore CLI.
- code-based agents for full orchestration control using frameworks like LangGraph or OpenAI Agents
- managed harness preview for rapid configuration-based deployment.
- code-based agents for full orchestration control using frameworks like LangGraph or OpenAI Agents
- managed harness preview for rapid configuration-based deployment.
This article explores the evolution of developer workflows, proposing that "skills" are becoming as essential as traditional Command Line Interfaces (CLIs). While CLIs are deterministic and require developers to provide all the necessary context, skills consist of simple Markdown files that teach AI agents how to operate within the specific context of a project.
By using YAML frontmatter and specific instructions, skills can orchestrate multiple tools like git, npm, and gh, adapting to project conventions and stack details automatically. The author argues that skills do not replace CLIs but rather sit on top of them, providing an orchestration layer that enables reasoning, adaptation, and complex multi-step workflows that traditional, static tools cannot achieve alone.
By using YAML frontmatter and specific instructions, skills can orchestrate multiple tools like git, npm, and gh, adapting to project conventions and stack details automatically. The author argues that skills do not replace CLIs but rather sit on top of them, providing an orchestration layer that enables reasoning, adaptation, and complex multi-step workflows that traditional, static tools cannot achieve alone.
This repository focuses on the concept of an "agent" as a trained model, not just a framework or prompt chain. It emphasizes building a "harness" – the tools, knowledge, and interfaces that allow the model to function effectively in a specific domain. The core idea is that the model *is* the agent, and the engineer’s role is to create the environment it needs to succeed.
The content details a 12-session learning path, reverse-engineering the architecture of Claude Code to understand how to build robust and scalable agent harnesses. It highlights the importance of separating the agent (model) from the harness, and provides resources for extending this knowledge into practical applications.
The content details a 12-session learning path, reverse-engineering the architecture of Claude Code to understand how to build robust and scalable agent harnesses. It highlights the importance of separating the agent (model) from the harness, and provides resources for extending this knowledge into practical applications.
OpenCode is an open-source AI coding agent designed for development work. It offers two built-in agents: 'build' for full access and 'plan' for read-only analysis and code exploration. Installation is possible via curl, package managers (npm, brew, etc.), or as a desktop application for macOS, Windows, and Linux. It distinguishes itself from tools like Claude Code by being 100% open source, provider-agnostic, offering LSP support, and having a focus on a Terminal UI. OpenCode is built with a client/server architecture, allowing for remote access via mobile apps.
Think of Continuous AI as background agents that operate in your repository for tasks that require reasoning.
>Check whether documented behavior matches implementation, explain any mismatches, and propose a concrete fix.”
> “Generate a weekly report summarizing project activity, emerging bug trends, and areas of increased churn.”
>“Flag performance regressions in critical paths.”
>“Detect semantic regressions in user flows.”
>Check whether documented behavior matches implementation, explain any mismatches, and propose a concrete fix.”
> “Generate a weekly report summarizing project activity, emerging bug trends, and areas of increased churn.”
>“Flag performance regressions in critical paths.”
>“Detect semantic regressions in user flows.”
LLM-powered Rust development assistant with meta-cognition framework.
Canonical announced today that they will formally support the NVIDIA CUDA toolkit and also make it available via the Ubuntu repositories. This aims to simplify CUDA installation and usage on Ubuntu, particularly with the rise of AI development.
AGENTS.md is a simple, open format for guiding coding agents. It's a dedicated, predictable place to provide context and instructions to help AI coding agents work on your project. The document provides an example AGENTS.md file and details on running a local Next.js website associated with the project.
Augment Code is an AI coding assistant aimed specifically at professional software engineers and large codebases, offering features like project summaries, code improvements, and real-time code completions. It is designed to enhance productivity by understanding the context and style of your project, providing useful suggestions and improvements.
