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@davidculley@hachyderm.io

Post #1457400

2025-11-14 16:14 UTC

@mariyadelano This is exactly my experience. They just ask for some automation. And naturally, due to their cluelessness, their first thought is AI. What they ask for can almost always be delivered with zero AI: - find all occurrences of A in a text and replace them with B - transform the CamelCase words in all this text to snake_case words - auto-indent this text according to our style guide All you need is a basic grasp of your text editor and/or the ecosystem of the programming language of your choice. I understand that a non-technical manager doesn't understand the difference between this and AI. But when I see senior engineers who don't understand this, I wonder how they ever earned the Senior in their title. Years since graduation from university isn't the same as experience. Having survived multiple years in the same job without growth isn't the same as being senior. None of the mentioned "use cases for AI" are sophisticated. If you want to auto-format your code in a way that is always correct, just execute prettier, black, rustfmt or sqlfmt, depending on whether your code is written in JavaScript, Python, Rust or SQL. I will never understand people that write up a Confluence document, jotting down their personal preferences and pet peeves, and then ask Copilot to format the codebase according to that Confluence page, and each time get different results and/or merge conflicts with the non-deterministic LLM messing up half the time. Also, LLMs are popular since about 3 years. How the fuck did managers ask their engineers to replace all occurrences of A with B, four years ago? Before they could say, "Use AI to do it."

Replies (1)

  • @patmikemid@sfba.social 2025-11-14 16:31

    @davidculley @mariyadelano To your point, I was reviewing what is referred to as the “Machine Learning Lifecycle” yesterday and this is the first step: “Step 1: Problem Definition The first step is identifying and clearly defining the business problem. A well-framed problem provides the foundation for the entire lifecycle. Important things like project objectives, desired outcomes and the scope of the task are carefully designed during this stage. Collaborate with stakeholders to understand business goals Define project objectives, scope and success criteria Ensure clarity in desired outcomes” I’m thinking, isn’t that always the first step? https://www.geeksforgeeks.org/machine-learning/machine-learning-lifecycle/

    Open ##1457401