Below is a conversation I had with the OpenAi chat bot. about Continuous Coding and my attempts to get it to write the website for me.

Did you learn from the AI? Did the AI give you more information than the prompt? Let me know in the comments.

The first prompt

Please write an article about Continuous Coding. Continuous Coding is the set of development practices which are needed to enable Continuous Deployments. Please include detailed techniques such as feature flags, dark launches, independent code paths, and other related techniques. Do not include version control, as that is very common. Also, include some practices which Continuous Coders should avoid.

First draft of the the Article

Continuous Coding is a set of development practices that aim to enable the implementation of Continuous Deployments. Continuous Deployments are a key component of modern software development, as they allow teams to quickly and easily push new code to production environments. This helps to accelerate the development and release process, making it possible to deploy new features and bug fixes faster than ever before.

One key practice in Continuous Coding is the use of feature flags. Feature flags are a way of selectively enabling or disabling features in a codebase. This allows developers to work on new features without disrupting the existing code, and to easily roll back changes if necessary.

Another important practice in Continuous Coding is the use of dark launches. A dark launch is the practice of deploying new code to a production environment without immediately making it available to users. This allows teams to test new code in a real-world setting without affecting the user experience.

Independent code paths are another important technique in Continuous Coding. This involves dividing the codebase into separate paths, each of which can be developed and deployed independently. This allows teams to work on different features simultaneously without affecting each other's work.

One practice that Continuous Coders should avoid is the use of long-lived branches. In traditional software development, teams often work on long-lived branches, which can last for weeks or even months. This can lead to conflicts and merge issues, which can slow down the development process. Instead, Continuous Coders should use short-lived branches, which are easier to merge and less prone to conflicts.

In conclusion, Continuous Coding is a set of practices that enable Continuous Deployments. These practices include the use of feature flags, dark launches, and independent code paths. Continuous Coders should avoid the use of long-lived branches, and instead use short-lived branches to accelerate the development and release process.

My first response to the bot.

Thank you, please continue the article with three more practices that contribute to continuous coding, and three practices which detract from continuous coding.

OpenAI chat responds!

Three additional practices that contribute to continuous coding are:

1. Continuous Integration (CI): This is the practice of automatically building and testing code changes as they are made, to ensure that they are always in a deployable state. This helps to prevent conflicts and errors, and makes it easier to deploy new code to production environments.

2. Automated testing: In order to make Continuous Deployments possible, it is essential that the codebase is well-tested. Automated testing allows teams to quickly and easily validate new code changes, without the need for manual testing.

3. Monitoring and feedback: Continuous Coding relies on the ability to quickly identify and fix errors in production environments. Monitoring tools and feedback mechanisms, such as error logs and user feedback, can help teams to quickly identify and fix issues in real-time.

Three practices that detract from continuous coding are:

1. Lack of communication: In order for Continuous Coding to be successful, it is essential that all members of the development team are on the same page. Poor communication can lead to misunderstandings and conflicts, which can slow down the development process.

2. Inadequate testing: As mentioned above, automated testing is crucial for Continuous Coding. Without adequate testing, it is impossible to ensure that new code changes will not introduce errors or regressions.

3. Lack of planning: In order to successfully implement Continuous Deployments, teams must have a clear plan in place. Without proper planning, it can be difficult to coordinate work and ensure that new code is deployed in a timely manner.

Another Prompt

At this point, I felt like the AI didn't include everything I thought should be part of Continuous Coding practices so I tried to nudge it a little and see what it came up with.

Please add a paragraph about the importance of small commits, and separating refactoring commits from behavior changes commits. Also, what is the best way to add tests to allow for continuous coding?

The Chat bot responded:

The use of small commits is an important practice in Continuous Coding. Small commits make it easier to review and merge code changes, and they also make it easier to roll back changes if necessary. Additionally, separating refactoring commits from behavior changes commits can help to keep the codebase clean and organized.

To allow for continuous coding, it is essential to have a comprehensive suite of automated tests. The best way to add tests is to follow the practice of Test-Driven Development (TDD), which involves writing tests before writing the code itself. This ensures that the code is well-tested, and makes it easier to quickly identify and fix any errors or regressions. Additionally, using a combination of unit tests, integration tests, and end-to-end tests can help to provide comprehensive coverage of the codebase.

Where we see how much unique information the chat bot can provide on it's own...

Is there anything else that should be added on the topic of Continuous Coding?

Response

One other important aspect of Continuous Coding is the use of version control systems. Version control systems, such as Git, allow teams to track changes to the codebase, collaborate on code changes, and roll back changes if necessary. This is a key component of Continuous Coding, as it allows teams to manage the codebase and ensure that new code is always in a deployable state.

Additionally, the use of DevOps practices can also contribute to successful Continuous Coding. DevOps is a set of practices that focus on collaboration between development and operations teams, with the aim of improving the speed and reliability of the software development process. By adopting DevOps practices, teams can improve the efficiency and effectiveness of their Continuous Coding efforts.

I push it further, and ask...

"Anything else?"

One other important consideration in Continuous Coding is the use of feature toggles. Feature toggles are a way of enabling or disabling new features in a codebase without changing the code itself. This allows teams to test new features without disrupting the existing code, and to easily roll back changes if necessary. Feature toggles are an essential tool in Continuous Coding, as they allow teams to quickly and easily deploy new features to production environments.

Another important aspect of Continuous Coding is the use of continuous feedback. Continuous feedback is the practice of gathering and responding to feedback from users and other stakeholders in real-time. This allows teams to quickly identify and fix issues, and to improve the quality and usability of their software. By incorporating continuous feedback into their development process, teams can ensure that their software is always meeting the needs of users.

Overall, Continuous Coding is a set of practices that aim to enable Continuous Deployments and improve the efficiency and effectiveness of the software development process. By adopting practices such as feature flags, dark launches, and automated testing, teams can quickly and easily deploy new code to production environments, and ensure that their software is always of the highest quality.

But wait, there is more...

"Anything else?"

One final aspect of Continuous Coding is the need for continuous learning and improvement. In order to be successful, teams must be willing to constantly learn and adapt to new technologies and development practices. This may involve attending conferences and workshops, reading industry blogs and publications, and experimenting with new tools and techniques. By staying up-to-date with the latest developments in the field, teams can ensure that their Continuous Coding practices are always effective and efficient.

Additionally, teams must also be willing to continuously assess and improve their development processes. This may involve conducting regular code reviews, implementing new testing strategies, and making changes to the development workflow. By constantly seeking ways to improve, teams can ensure that their Continuous Coding practices are always aligned with their goals and objectives.

In conclusion, Continuous Coding is a set of practices that enable Continuous Deployments and improve the efficiency and effectiveness of the software development process. By incorporating techniques such as feature flags, dark launches, and automated testing, and by staying up-to-date with the latest developments in the field, teams can ensure that their software is always of the highest quality and that new code is always in a deployable state.

Final final answer? ...

"Anything else?"

One final note on Continuous Coding is the importance of collaboration and communication within the development team. In order for Continuous Coding to be successful, all members of the team must be aligned and working towards the same goals. This may involve implementing collaboration tools and processes, such as agile methodologies, to ensure that everyone is on the same page. Additionally, regular communication and feedback among team members can help to identify and address any issues or obstacles in the development process. By fostering a culture of collaboration and communication, teams can ensure that their Continuous Coding practices are always effective and efficient.

At this point, I fear it's repeating itself so I switch it up a bit.

Is there more about Continuous Coding that I should know that you have not mentioned already?

I believe I have covered the most important aspects of Continuous Coding. However, one other thing to keep in mind is that Continuous Coding is not a one-size-fits-all approach, and different teams may have different needs and requirements. Therefore, it is important for teams to experiment and find the practices and tools that work best for them, and to continuously assess and adapt their development processes as needed. By staying flexible and open to change, teams can ensure that their Continuous Coding practices are always effective and aligned with their goals and objectives

End of Round 1

At this point, I post the blog and gather some feedback. An initial response from Vlad points me to this blog post and reminds me of some important things the AI forgot to mention, and so I continue...

New question:

What about branching strategies and best code review practices?

Answer:

Branching strategies and code review practices are important aspects of Continuous Coding. In terms of branching, it is generally best to use short-lived branches, as these are easier to merge and less prone to conflicts. This allows teams to quickly and easily integrate new code changes into the main codebase. Additionally, using a branching strategy such as GitFlow can help to ensure that the codebase is organized and well-maintained.

As for code review, it is generally best to conduct regular, thorough code reviews, in which all members of the team review and provide feedback on new code changes. This can help to identify potential issues and improve the quality of the code. Additionally, using a code review tool, such as Pull Requests in Git, can make the code review process more efficient and organized. By following best practices for branching and code review, teams can ensure that their Continuous Coding efforts are always effective and efficient.

I did not like this answer and respond:

But GitFlow is known to be a hinderance to Continuous Deployments, and Pull Requests show signs of low trust on a team that needs to have high trust. These can't be Continuous Coding practices. What are better alternatives?

The bot corrects itself, kinda

You are correct, GitFlow and Pull Requests may not be the best practices for teams that are looking to implement Continuous Deployments. While these practices can be effective in some contexts, they can also create barriers to Continuous Deployments by slowing down the development process and introducing conflicts and merge issues.

Alternative branching strategies that may be more conducive to Continuous Deployments include trunk-based development and feature toggles. In trunk-based development, teams work on a single branch, known as the "trunk," and continuously integrate and deploy code changes to this branch. This allows for a more streamlined and efficient development process, and reduces the need for complex branching and merging.

Feature toggles, as mentioned earlier, are another way of enabling or disabling new features in a codebase without changing the code itself. This allows teams to quickly and easily deploy new code to production environments, without the need for complex branching and merging.

In terms of code review, it may be more effective for teams looking to implement Continuous Deployments to use a more lightweight review process. This could involve conducting regular, informal code reviews, in which team members quickly review and provide feedback on new code changes. This can help to ensure that the codebase is always in a deployable state, without slowing down the development process.

Overall, there are many different approaches to branching and code review that can be effective in the context of Continuous Coding. The best approach will depend on the specific needs and goals of the team, and may involve experimenting with different strategies and tools to find what works best.

The conversation continues...

You have mentioned feature flags a few times now. What are some important pitfalls to avoid when using feature flags and how can they be avoided, or what should one do to make feature flags easy and effective to use?

The answer

One important pitfall to avoid when using feature flags is creating a codebase that is difficult to maintain. If feature flags are not used carefully, they can lead to a codebase that is cluttered and hard to understand. This can make it difficult to identify and fix issues, and can also make it harder to roll back changes if necessary.

To avoid this pitfall, it is important to use feature flags in a disciplined and consistent manner. This may involve implementing strict guidelines for how and when to use feature flags, and regularly reviewing and cleaning up the codebase to remove unnecessary or outdated flags. Additionally, using a feature flag management tool can help to keep the codebase organized and maintainable.

Another pitfall to avoid when using feature flags is creating a fragmented codebase. If feature flags are used to enable or disable large sections of the code, it can be difficult to ensure that the codebase remains cohesive and consistent. This can lead to confusion and errors, and can make it harder to deploy new code to production environments.

To avoid this pitfall, it is important to use feature flags in a way that minimizes fragmentation. This may involve using flags to enable or disable only small, isolated sections of the code, rather than large, complex sections. Additionally, using independent code paths can help to keep the codebase organized and maintainable, even when using feature flags.

Overall, using feature flags effectively in the context of Continuous Coding requires discipline, consistency, and attention to detail. By following best practices and using the right tools, teams can avoid common pitfalls and ensure that their codebase remains maintainable and deployable.

Conclusions

I think the bot did better than I expected. It also gave some bad and conflicting advice, but that's expected from a Large Language Model (LLM) which is just collecting data from the internet. For myself, I found this useful and gives me places to focus. For example, it reminded me of some of the hard decisions and conversations around feature flags, and how this should probably be the focus of my next article.