It’s an interesting time to be in software and data; the world of generative AI is changing the landscape beneath our feet. I don’t see this as a bad thing for software folk, but as an opportunity to learn new technologies and BUILD / UNDERSTAND the technologies used in an LLM and AI context.
You can’t expect an LLM trained two years ago to be up-to-date on what the new and best approaches are for X, Y, Z tech.
Sure, they can do a decent job given enough context, Agents, etc, but if you’re working on the cutting edge of AI and LLM infrastructure, you are going to have to be active in the community and reading about what others are doing, who’s releasing new tools, and what those tools do.
Don’t forget, there is the whole architectural and systems design piece. One part of the LLM and AI infrastructure is vector and embedding representations.
Anyone who’s been around for more than a decade or so in the programming, development, and data world might get a slight eye twitch when the word database driver appears. Before the modern times we live in came along, the entire data world was driven by SQL Server, Oracle, with just a sprinkling of Postgres and MySQL … a-la AWS. That’s just the way it was.
Part of that joy was dealing with database drivers, such as JDBC and ODBC, which are language-neutral, OS-level standards for accessing databases.
It seems we have several cadres of people when it comes to “clean code.” I know there is a lot of previous baggage that comes with that nomenclature, good and bad. But, I think we can think about “clean code” from a simplistic point of view. It doesn’t have to be that complex.
We live in the Age of AI, in relation to the generation of code, of products, features … the software developer’s role has shifted. We can argue how it’s shifted, but it has.
If the generation of most of the mundane and everyday code is given to our AI peons like Cursor and Claude, then what value can you bring to the table?
You can bring a sense of good architecture from a systems perspective and from a “these modules of code” perspective. This data pipeline. Sure, some places, businesses just want you to churn out bits and bytes as fast as those tokens will let you, I feel bad for you. Many places still recognize the business context and keep the product running well … leading to happy customers who give us money … is extremely important.
There is an argument to be made that you should ensure you, or your AI, is producing clean code.
