nicodjimenez
Joined 11 years ago
Joined 11 years ago
One thing I learned after buying some gear at home to try to record electric guitar at low volume is how important the physics of the speakers are. You can plug a tube amp into a cabinet simulator and you'll lose a lot more than using solid state electronics on a good but not great Fender amp, especially if you use fuzz / distortion pedals.
I'm not sure Hendrix was a systems engineer, but he was a transcendent blues artist, that's for sure.
With vibe coding you just give the code some constraints and then system will try to work within those constraints, but what if those constraints are wrong? What if you’re asking the wrong question? Then you’ll end up with over complicated slop.
It’s a shame that vibe coded slop seems to be a new standard, when in fact you can use AI tools to produce much higher quality code if you actually care to engage in thoughtful conversations with the AIs and take a growth mindset.
It would make more sense to say that the event tree should not have any cycles, but anyway this seems like a silly point to make.
I would not use Golang for a big codebase with lots of business logic. Golang has not made a dent in Java usage at big companies, no large company is going to try replacing their Java codebases with Golang because there's no benefit, Java is almost as fast as Golang and has classes and actually has a richer set of concurrency primitives.
I've found a huge boost from using AI to deal with APIs (databases, k8s, aws, ...) but less so on large codebases that needed conceptual improvements. But at worst, i'm getting more than 10% benefit, just cause the AI's can read files so quickly and answer questions and propose reasonable ideas.
For startups, the devil's in the details though. The goal is to scale but you get there by doing things that don't scale successively.
https://github.com/mathpix/mpxpy
Disclaimer: I'm the founder. Reducto does cool stuff on post processing (and other input formats), but some people have told me Mathpix is better at just getting data out of PDFs accurately.
https://mathpix.com/careers/apply
Then internally we have dashboards and sorting based on AI agent scoring. I noticed the scoring is imperfect but still saves a lot of time. Candidates scored at or below 2/5 are reliably bad and candidates above 4/5 are consistently impressive and leave thoughtful answers.
The biggest thing is not using resumes. You can’t reliably gage applicants without a writing sample and resumes are the worst form of writing sample. Also you need to be intentional about who you’re hiring for, both to craft the questions as well as grade the responses.