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AI coding assistants have become essential tools for developers, but many rely solely on IDE extensions like GitHub Copilot or Cursor. While these are valuable, AI CLI tools offer unique advantages that can significantly boost your productivity and code quality.
CLI-based AI tools provide a focused, distraction-free environment with full terminal context, making them ideal for complex tasks, debugging, and working with entire codebases.
Anecdotally, developers have noticed that CLI tools often produce higher-quality, more accurate results than their IDE counterparts - possibly due to better context handling and fewer UI constraints.
"Vibe coding" is a trend that has taken the software development world by storm recently. It means developing via a coding agent, and never even looking at - let alone editing - the code. It has also become synonymous with low-quality code 👎.
When writing code as a professional developer, "vibe coding" may make it easy to get a solution up and running without worrying about the details, but as soon as you commit it to the repository under your name, it becomes your responsibility, as if you had written it yourself.
GitHub Copilot CLI is incredibly powerful, but giving AI deep access to your terminal and file system can be concerning. When you use features like --allow-all-tools - which approves all actions - Copilot can execute commands on your behalf, which means one wrong suggestion could have serious consequences.
Running Copilot CLI in a secure Docker container provides the best of both worlds: powerful AI assistance with strict security boundaries that limit the "blast radius" of any potential mistakes.
Previously, testing desktop features created with AI agents meant checking out a PR branch locally, building the app, and running it manually. Which took time, slowed feedback loops, and encouraged "vibe coding" where changes are shipped without a deep understanding of the code.
By exposing a settings option to switch to specific PR builds, they can be easily installed tested - no local branch juggling or manual builds required.
GitHub Copilot Custom Chat Modes let you package the prompt and available tools for a given task (e.g. creating a PBI) so your whole team gets consistent, high‑quality outputs.
Without a chat mode, individuals might copy/paste prompts . Important acceptance criteria or governance links get lost. New starters don't know the “standard way” and quality varies.
Since the release of GitHub Copilot, we have witnessed a dramatic evolution in how developers work within their IDE. It started with a simple AI autocomplete, then a chat function, and now we are in an agentic gold mine. AI has now been integrated deeply into IDEs with products like Visual Studio Code and Cursor, embedding an even deeper level of AI integration within a developer's workflow.
AI coding assistants like Cursor and GitHub Copilot are incredibly powerful, but they work best when they understand your project's specific architecture, coding standards, and common tasks. Without this context, they may suggest patterns that don't align with your codebase.
AGENTS.mdis an agreed-upon standard between all the big AI players, replacing things like .cursorrules and copilot_instructions.md.
When using AI assistants (like GitHub Copilot, Claude, ChatGPT, or Cursor) for development tasks, they often generate valuable documentation summaries explaining the changes made, architectural decisions, and implementation details. Properly organizing these AI-generated documentation ensures that:
When AI assistants make code changes on your behalf, proper attribution is essential. Adding yourself as a co-author when AI implements changes ensures transparency about who verified the work and maintains accurate contribution history.
This practice is especially important in team environments where AI assists with implementing features, fixing bugs, or making changes.
When AI assistants (e.g. GitHub Copilot, Claude, ChatGPT) perform development tasks, they sometimes create temporary files, scripts, or intermediate outputs. By default, they might use system temp directories like /tmp, /var/tmp, or the user's home directory. This creates several problems: files become hard to find, they're outside version control, and cleanup becomes unpredictable.
Configuring AI assistants to work exclusively within the repository boundaries ensures all work is visible, properly managed, and easy to clean up.
You’re in the zone: the AI is pumping out code, you’re copy-pasting at light speed, and everything *seems* to work… until a weird edge case hits production, a security scanner lights up, or your team can’t explain the “magic” function anyone merged last week.
Vibe coding is awesome—**as long as you add guardrails**.
AI‑assisted tools can turn rough ideas into working demos in hours instead of weeks. They help you scaffold codebases, generate UI from prompts or designs, and wire up data so you can validate scope and risk with clients quickly.
When you need AI assistance for development but find yourself offline (whether you're on a flight, gone camping, or facing the inevitable zombie apocalypse) you'll appreciate having local LLMs ready in your workflow!
Developers love submitting Pull Requests (PRs) but far fewer enjoy reviewing them. In particular, when Sprint Review approaches, developers get tunnel vision and only focus on tasks they've been assigned. By leveraging AI agents, you can catch many problems and gotchas in your PRs early, buying your senior devs more time (and sanity!) to review higher quality code.
Here are SSW's recommended MCP servers for enhancing your AI coding assistants with additional context and capabilities.
When working with AI agents, a common bottleneck is the human review loop. If you ask an agent to "refactor this class" and it introduces a syntax error, you have to spot it and tell the agent to fix it. This manual feedback loop is slow, unscalable, and wastes your time.