The LokerDollar.com web up to 85% faster in some cities — by moving our code farther from users.

Sounds backwards. Here's why it worked.

1. The edge promise is seductive: run your code in 300+ cities, and it's milliseconds from every user. Newark, Sydney, São Paulo — all local.

2. Your code doesn't work alone. It talks to a database. And that database lives in one place. A single page render can mean 6+ sequential trips to it.

3. Put your compute "at the edge," and each of those trips crosses an ocean and comes back. The edge didn't remove the distance. It multiplied it. That's how a Sydney request quietly turned into seconds of pure waiting.

The fix was one line of config Cloudflare smart placement stopped stop pinning our code next to the user, and run it next to the database instead. One long trip to reach compute, then all the DB chatter is local and nearly free.

The result, measured on our own traffic (P90 — the slowest 1 in 10 requests): • Newark ↓ 85% • Sydney ↓ 82% • Melbourne ↓ 63% • São Paulo ↓ 60% and …12 cities faster overall

The lesson I'm taking with me:

Latency isn't about distance to the user. It's the number of long trips on the critical path. Put compute where it talks the most — for data-heavy apps, that's your database, not the edge.

#WebPerformance #EdgeComputing #Cloudflare #BuildInPublic #SoftwareEngineering

Great Work

One pattern I’ve seen repeatedly while working with growing businesses: At first, manual processes seem manageable. A few spreadsheets. A few marketplaces. A few team members coordinating inventory manually. Everything works. Until growth starts. Suddenly, the same processes that supported the business become bottlenecks. Inventory mismatches increase. Orders take longer to process. Teams spend more time updating data than making decisions. And scaling requires hiring more people just to keep operations running. Recently, we worked on a project where the challenge wasn't building another customer-facing feature. The challenge was creating a system that could automate inventory management, synchronize data across multiple channels, and give the business better operational control as it grew. The project reinforced something important: Many companies think they need more people. Often, they need better systems. The strongest operational improvements usually happen when repetitive manual work is removed and teams can focus on higher-value activities instead. Technology creates the biggest impact when it solves operational friction, not just when it adds new functionality. Have you ever reached a point where growth exposed weaknesses in internal processes that seemed perfectly fine before?

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