I'll echo that the UI design is cool. I do think it could be improved to help parse information. Right now, it feels like everything is bold, highlighted, italicized, or something denoting how important it is. This is made worse as every UI element is also bold and highlighted.
I built something like this at work using plain Docker images. Can you help me understand your value prop a little better?
The memory forking seems like a cool technical achievement, but I don't understand how it benefits me as a user. If I'm delegating the whole thing to the AI anyway, I care more about deterministic builds so that the AI can tackle the problem.
Good for you for trying to do right by your team, but oof. An entirely junior team with no tech leadership is going to have problems beyond mentorship.
For reference, 14 yoe and currently in management.
Today, I don't think the tools are good enough to make a material difference. It may help a bad engineer tread water, but it won't take you from good to great. It may save you time writing basic boilerplate and individual functions, but I suspect 99% of engineers don't struggle with that. What's hard about our jobs is knowing how to orchestrate the whole thing and put structure around complexity. AI can't do that yet.
When I use it personally, it feels like a harder context switch trying to describe in english what I already know how to code. Then I still have to review the function to make sure it's accurate. It feels like a waste of time and an additional context switch.
Whenever the AI gets better, we'll have to use it to be productive I have no doubt. But the pool of engineers will change too - there will be a categories of engineers who can't debug the AI output and who still write crazy prompts.
Maybe I'm old, but I'll only be worried about AI when it can write and maintain a full app with no human intervention.
Hopefully it will eliminate all the boring shit like managing JIRA, giving status updates, and following up with communication tasks. Another large part of my day is also troubleshooting random things, so hopefully AI will benefit my team before I have to get engaged.
I don't think AI poses a risk when it comes to setting engineering priorities and building the roadmap. If it could do that, it could probably just build the entire system anyway.
EMs are there for the human aspect of engineering, so I also doubt it will impact hiring or EM-engineer ratios.
I do expect the bar to being an EM to be higher as the job will be more technical and less project management.
When you say 'analytics database', what kind of performance are you implying? Massive queries that respond in 10min? How tuned were things for the queries you were running?
I'm currently working through an analytics architecture and I'm having to defend against "why aren't you using postgres" when I'm talking about olap dbs.
What process is going to help senior contractors understand the codebase they've been in for 2+ years? We can create spike tickets all day, but at a point, that's just a distraction.