> all of us who did have been in absolute awe of the new capabilities and tools that AI can deliver to augment our skills, crafts, and fill in our gaps.
Am I fundamentally missing something about the experience upper management has with AI versus the experience of a mid/senior level developer in the trenches? I also have access to Cursor and a few other LLMs.
They're handy for bouncing ideas around and for some simple tasks, but I've hardly felt like it was a force multiplier.
When it comes to coding, the amount of times it's hallucinated a function that didn't exist or used a deprecated implementation has felt like a net neutral at best.
> We will add AI usage questions to our performance and peer review questionnaire.
> AI must be part of your GSD Prototype phase.
I can understand asking your devs and other employees to try out AI in their workflows in an attempt to get some productivity gains. Sounds like a responsible CEO trying to milk some value out of his existing headcount. But, it sounds absolutely dystopian to tie performance metrics or changing the project planning process to be AI-centric.
I started out as a business analyst and technical product manager at a startup before transitioning to software engineer job family there and in my 2 subsequent roles over the past 6 years. It sounds like your management is especially supportive of you making this transition internally, which is going to be very helpful.
I want to add a caveat that your manager and colleagues should be a resource for you when tackling projects at work, and not to hesitate to ask pragmatic questions about the project(s) you might pitch in on there. It's ok to not have all the fundamentals down, as long as you're working to be a net positive on the project(s).
A subset of the resources listed there are probably the most pragmatic for the topics you asked, but you might discover that you're interested in other areas of CS as you slowly work through them. I think it's ok to nibble away at exercises while juggling your family and work obligations.
* Structure and Interpretation of Computer Programs - SICP. If the book doesn't necessarily click right away, doing a subset of the Scheme exercises are still worthwhile.
* Computer Systems: A Programmer's Perspective - CS:APP. Incredibly helpful knowledge about low-level programming.
* Computer Networking: A Top-Down Approach - You mentioned having a home lab, and more networking understanding will help contextualize some of your work.
Am I fundamentally missing something about the experience upper management has with AI versus the experience of a mid/senior level developer in the trenches? I also have access to Cursor and a few other LLMs.
They're handy for bouncing ideas around and for some simple tasks, but I've hardly felt like it was a force multiplier.
When it comes to coding, the amount of times it's hallucinated a function that didn't exist or used a deprecated implementation has felt like a net neutral at best.
> We will add AI usage questions to our performance and peer review questionnaire.
> AI must be part of your GSD Prototype phase.
I can understand asking your devs and other employees to try out AI in their workflows in an attempt to get some productivity gains. Sounds like a responsible CEO trying to milk some value out of his existing headcount. But, it sounds absolutely dystopian to tie performance metrics or changing the project planning process to be AI-centric.
I doubt every project fits that paradigm.