Want to revolutionize your business with AI? Check SSW's Artificial Intelligence and Machine Learning consulting page.
"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.
Vibe coding empowers non-developers to build software using AI agents. However, without proper foundation and structure, vibe coding can result in unmaintainable code that fails "the bus test" – meaning if you were hit by a bus tomorrow, no one else could understand or maintain your code.
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.
The advent of GPT and LLMs have sent many industries for a loop. If you've been automating tasks with ChatGPT, how can you share the efficiency with others?
GPT is an awesome product that can do a lot out-of-the-box. However, sometimes that out-of-the-box model doesn't do what you need it to do.
In that case, you need to provide the model with more training data, which can be done in a couple of ways.
When you're building a custom AI application using a GPT API you'll probably want the model to respond in a way that fits your application or company. You can achieve this using the system prompt.
AI agents are autonomous entities powered by AI that can perform tasks, make decisions, and collaborate with other agents. Unlike traditional single-prompt LLM interactions, agents act as specialized workers with distinct roles, tools, and objectives.
Repetitive tasks like updating spreadsheets, sending reminders, and syncing data between services are time-consuming and distract your team from higher-value work. Businesses that fail to automate these tasks fall behind.
The goal is to move from humans doing and approving the work, to automation doing and humans approving the work.