About
Austin, TXA field guide, not a hype cycle.
Goldwater.dev exists for a simple reason: most cloud and AI advice is either marketing in disguise or theory with no contact with production. This is the resource I wish I'd had — clear, opinionated, and grounded in what actually ships.
I'm Wes Goldwater, Director of Engineering at Prosigliere, based in Austin, Texas. I've spent years building and operating systems on the cloud, leading the teams that ship them, and — more recently — figuring out where AI genuinely belongs in the mix. I've seen the elegant architectures that collapsed under real load and the unglamorous ones that just kept working. I've watched AI projects burn quarters chasing demos, and small ones quietly save real money.
At Prosigliere, my team builds AI-forward engineering talent and fractional technology leadership for companies from early startups to the Fortune 500 — nearshore teams in Argentina and Brazil, a Google Cloud partnership, and tools like Devin in our stack. That's the lens behind much of what I write here, and the reason a few of these guides point you toward Prosigliere when it's a genuine fit.
I help engineering teams and leaders make pragmatic cloud and AI decisions — cutting through the hype to the choices that actually move reliability, cost, and speed. If your team is weighing a platform, planning an AI rollout, or just wants a second set of eyes from someone who's shipped it, let's talk. Goldwater.dev is where I write it all down, so you can skip a few of the expensive lessons and get to the useful part faster.
What this site believes
Pragmatism over hype
New isn't the same as better. Every recommendation here is weighed against the boring question that actually matters: does it make the system more reliable, cheaper, or easier to change?
Show the trade-offs
There are no free lunches in distributed systems or AI. Good advice names the cost as clearly as the benefit, so you can decide what fits your context.
Built from real systems
These guides come from production — the deploys that broke, the bills that surprised, the AI features that worked and the ones that quietly didn't.
Free and open
Knowledge compounds when it's shared. The resource is free to read, with no paywall standing between you and a clearer mental model.
Working on something cloud or AI?
I work with teams in Austin, TX and remotely on cloud architecture, platform strategy, and pragmatic AI adoption. If that's on your plate, I'd love to hear about it.