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The glamourous AI coding agent for your favourite terminal
A new study by MIT CSAIL researchers maps the challenges of AI in software development, identifying bottlenecks and highlighting research directions to move the field forward, aiming to allow humans to focus on high-level design while automating routine tasks.
Amazon has released Kiro, an AI-powered IDE designed for "vibe coding" that focuses on bringing prototypes into production with features like specs and hooks. It's powered by Claude 4 Sonnet and aims to apply software engineering best practices to the vibe-coding workflow.
AI coding tools are increasingly interacting directly with the system shell (terminal) rather than traditional code editors, driven by the rise of agentic AI and tools like Claude Code, Gemini CLI, and CLI Codex. This shift, highlighted by benchmarks like Terminal-Bench, is occurring as some code-based tools face challenges and offers a versatile interface for developers.
This article explores how AI agents are reshaping software development and the impact they have on a developer’s workflow. It introduces a practical approach to staying in control while working with these tools by adopting key best practices from the discipline of software architecture, including defining an implementation plan, splitting tasks, and so on.
The article details the author's use of Claude Code to add a feature to a GitHub repository: an automatically updated README index. It's accompanied by a 7-minute video demonstrating the process.
GitHub Copilot now has an Agents page to help developers kick off tasks and track progress. Users can assign tasks to Copilot (tech debt, bug fixes, new features) and Copilot will create a draft pull request for review. The feature is available to Copilot Pro/Pro+, Business, and Enterprise users with the coding agent enabled.
This article discusses the impact of Large Language Models (LLMs) on the field of software engineering, arguing that while LLMs can increase efficiency, it's crucial to maintain a pipeline of junior engineers who learn through practical experience and problem-solving, rather than solely relying on AI-generated code.
Guidance on choosing the best AI model for GitHub Copilot projects, considering speed, depth, cost, and task complexity. Models discussed include GPT-4.1, GPT-4o, Claude 3.5 Sonnet, o4-mini, o3, Gemini 2.0 Flash, and GPT-4.5.
