Gonzalo Gomez

🚀 I help SaaS, startups & telcos build cloud communication platforms using Twilio + Laravel ✅ Twilio Flex setup & plugin development ✅ A2P 10DLC + SMS/WhatsApp ✅ AI voice agents + SaaS apps 🌍 LATAM-based, US client focus 📅 Book a call → ggomez.dev

so this is the way of getting a new job nowadays huh

It works on my machine 😶‍🌫️

Usage limits are up, effective today we're: 1) Doubling Claude Code's 5-hour limits for Pro, Max, Team and seat-based Enterprise plans 2) Removing peak hours limit reduction on Claude Code for Pro and Max plans 3) Substantially raising our API rate limits for Opus models

@PovilasKorop When searching for jobs, you got to check the hiring rate and money spent. Not to mention the verified payment method. When hiring, I noticed some freelancers hire themselves through different accounts and receive around 10-50 bucks on each job, but they do it to boost ratings

I noticed a pattern of possibly FAKE jobs on Upwork. Some jobs finish description with EXACT SAME sentence structure "if you have/are ... we want to hear from you". 3 jobs attached. All clients with 0% hire rate. Coincidence? I don't think so. Who/why is doing this? Dunno.

Introducing Claude Opus 4.7, our most capable Opus model yet. It handles long-running tasks with more rigor, follows instructions more precisely, and verifies its own outputs before reporting back. You can hand off your hardest work with less supervision.

If you're evaluating WhatsApp infrastructure for an AI communication layer, the BSP decision is made before anything else. Reply "official" and I'll share a list of compliant providers that actually support AI integration at scale

The tradeoffs are real: per-conversation pricing, template approval for business-initiated messages, 24-hour session windows that require queue management. These are solvable at the architecture level. A permanently banned production number is not.

1/ WhatsApp banning your bot is not a moderation glitch. It's your architecture failing at a predictable point.

I’ve been spending more time thinking about these pipelines as infrastructure rather than “AI features”. Curious how others are approaching this once things move past prototypes.

Two teams can use very similar models and end up with completely different products. One feels usable. The other feels like a demo. At that point, it’s no longer an AI problem. It’s a systems design problem.

Most voice AI problems I see in production have nothing to do with the model. They come from how the system is wired together.

We all gotta start somewhere. Can’t thank enough the ppl who found my content useful 💪🏻🙌🏻