I remember going from Java in IntelliJ straight to TypeScript at work for another project, and I recall how _slow_ everything was in the editor(s). I have been using TypeScript 7 RC and most of my complaints have gone away with regards to speed.
At $LARGE_ENTERPRISE_COMPANY, I've found that if you have:
1) A designer that uses Figma correctly (using well defined components / design systems)
2) A front-end framework as close to HTML / CSS as possible for the visuals (I have success with Web Components / Lit) with Figma MCP
The front-end is usually one-shot using frontier models. However in my experience, designers are all over the place with using Figma correctly.
Both NEM 2.0 and 3.0 have serious issues, but for different reasons. NEM 2.0 was basically a early adopter's rich person's subsidy that heavily distorted the market, and NEM 3.0 does not have nearly enough subsidies to justify the cost unless you pay cash up front for a large system. (For the record, I am on NEM 3.0 and got such a system).
At the end of the day, the best case scenario is large scale renewable / battery storage to bring costs down as much as possible, and for those of us who want battery backup / solar can choose to invest in it, but it shouldn't be "the" solution.
I'm currently in repos where the context window required is so large that the output is almost always "wrong" for the problem at hand. Quite a few people at my company burn through tokens this way, and it certainly isn't providing value to the company.
I'm in a large enterprise context--you have to use human reviewers if you don't want to end up like Github's status page. So much context exists outside of the code that the bots are either not provided or are far too large of contexts for current windows.
This was definitely true for me, which is why I write everything acoustically and ensure the song is "good" before going in my later age. If I want a specific effect, I then google what pedals were used in a particular song or artist, then I try to recreate the chain, and then tinker with that on top.
Ultimately I spent so much of my time worrying about "what crazy expensive equipment should I buy" when I was younger and more into this stuff, and I should have simply just played my shitty instruments and recorded on my shitty equipment. That's on me, but I also find it empowering as an artist that I can clean up my recording in the way that replaces my need for expensive equipment while maintaining (in my humble opinion) a sense of authenticity of my performance. I agree there may be too many knobs, but finding the knobs that I want has never been easier and I would rather live in the now than in the past.
A lot of people thought the same thing with everything going from analog -> digital. Or heck, even learning an instrument when MIDI was first introduced.
Even before generative AI, there is a long-going debate in audio circles around simulated guitar amplifiers. The truth is, the simulations of them have gotten so insanely good that now one could simply purchase an all-in-one pedalboard and have basically all of guitar history at your toes.
My rule-of-thumb is this: "does this tool I'm using in particular take away from the authenticity of my performance or songwriting?" Example: I am very keen on performing vocals and guitar at the same time, and I don't have an expensive studio setup, and my office has background noise. I use these tools, and yes even some open source AI ones, 1) remove background noise of the individual tracks and 2) do a final master against a recording I want to target (using something like Matchering or similar [0]). It still sounds like me, my voice isn't perfect, my beat isn't consistent, but it sounds like I rented some studio space. So for me it was a cost-saving measure.
Not to be "that guy that just says to use LLMs", but writing out how you want these things to work on your computer to something like Claude, or heck even Google AI mode without logging in with an account, allows you to describe your ideal "home server as a docker-compose.yml file" and for me it did a damn fine job doing it. I had done this all with a previous server manually, and with a new server I simply had provided that I was a Fedora Linux box, with these hard drives, with these containers, and these are the locations of the files, etc. It worked the first try.
This wasn't something that I didn't want to learn myself, but I have so little time with children and gardening on top of my super busy work at this point that I didn't have time to simply google everything. I did know enough about it beforehand to provide a general idea of what I want, so YMMV.
I'm at a large enterprise outfit, and "shoving things in your face" has been a problem with large software suites for a long time, long before the AI craze. I keep telling my skip level leadership that we need more User-Experience "mob goons" that have authority across product domains to (metaphorically) beat the living daylight out of bad "PM-brained" ideas.
My work 64GB M1 Max Macbook Pro is consistently out of memory. (To be fair my $LARGE_ENTERPRISE_EMPLOYER reserves about half of it to very bad Big Brother daemons and applications I have no control over)
I have only purchased Toyota vehicles (currently in the market for an EV) and it baffles me that Dodge created a Charger in EV form and Toyota hasn’t made even an EV Corolla or Camry.
I generally agree with you. As a recent father with a toddler, and two parents with a full time job, I’ve found that the only way I can make time for those personal side projects is to use AI to do most of the bootstrapping, and then do the final tweaks on my own. Most of this is around home automation, managing my Linux ISO server, among other things. But it certainly would be more fun and rewarding if I did it all myself.
I’m at $LARGE_ENTERPRISE_SAAS and I agree. There is a mass psychosis going on around what these LLM tools (which I use daily) are capable of *at scale*. The amount of business processes and tasks these software suites can, and must perform at near 100% correctness every time is massive, across an insane number of domains, accounting for an insane number of laws, countries, languages, browser configurations, business requests, legal teams. The list goes on, and while you can bootstrap a front end that appears to do 80% of a large dinosaur competitor like ours, the reality is it can’t, and the context windows to get there are in the orders of magnitude larger than they are today.
The weird part is that people at our company also fail to see this. “This vibe coder is going to recreate 20+ years of code, use cases, business processes and integrations for thousands of companies across hundreds of domains!” is uttered every day and just simply isn’t true.