There's an odd trend with these sorts of posts where the author claims to have had some transformative change in their workflow brought upon by LLM coding tools, but also seemingly has nothing to show for it. To me, using the most recent ChatGPT Codex (5.3 on "Extra High" reasoning), it's incredibly obvious that while these tools are surprisingly good at doing repetitive or locally-scoped tasks, they immediately fall…
It might be role-specific. I'm a solutions engineer. A large portion of my time is spent making demos for customers. LLMs have been a game-changer for me, because not only can I spit out _more_ demos, but I can handle more edge cases in demos that people run into. E.g. for example, someone wrote in asking how to use our REST API with Python. I KNOW a common issue people run into is they forget to handle rate limits,…
They seem to fall apart (for me, at least) when the projects get larger or have multiple people working on them.
They're also super helpful for analytics projects (I'm a data person) as generally the needed context is much smaller (and because I know exactly how to approach these problems, it's that typing the code/handling API changes takes a bunch of time).