Thoughts on development, design decisions, experiments, and product thinking.
You think your multi-threaded code is correct because it works on your x86 laptop. Spoiler: your CPU has been quietly buffering your stores and serving you lies. Here's what actually happens at the hardware level—and why your "correct" code breaks on ARM.
I spent two weeks chasing a memory leak that didn't exist. The culprit? Linux's cgroup v2 memory controller, transparent hugepages, and the way container runtimes actually construct the prison you think you understand. Here's what nobody tells you about how memory limits actually work in production.
You wrote your code. Tests passed. Staging worked perfectly. Then production happened. Here's why your "atomic" operations are silently corrupting data, and nobody's telling you about it until 3 AM.
Everyone told me Rust would end memory corruption. Then I spent three weeks debugging a use-after-free in a Go service. Here's what's actually happening with memory safety in production systems—and why the industry narrative is dangerously oversimplified.
I benchmarked a Go HTTP handler against raw syscall code last week. The managed solution was 47x slower. Here's what I found when I ripped off the training wheels and actually looked at what's running under your abstractions.
After spending years staring at core dumps at 3 AM, I've learned that memory corruption bugs are far from dead—they've just gotten sneakier. Here's what actually breaks in production, why the usual fixes fail, and the uncomfortable truth about what's really happening in your server's RAM right now.
free says you have plenty of RAM. Your application still OOMs. Here's what's actually happening with the Linux page cache, and why the tools everyone uses tell half the story.
We told ourselves that switching to Rust or Go would end memory corruption. We rewrote the critical paths, celebrated on Twitter, and moved on. But I've spent the last year looking at post-incident reports and CVEs, and what I found was uncomfortable: memory safety is a spectrum, not a binary, and most developers have no idea where their code actually sits on it.
Your microservices are lying to you. That "stateless" service? It maintains state you never see. The database you think is consistent? It's making promises it can't keep. Here's what nobody tells you about the distributed systems you're already running—and why that's a problem.
After 15 years of debugging production systems, I still see engineers misconfigure TCP keepalive constantly. The kernel defaults will burn your CPU, leak your sockets, and still let your connections rot silently. Here's what actually happens, why it happens, and what you should do instead.
Most engineers write concurrent code as if memory writes simply... happen. They're wrong. I've spent years debugging subtle correctness bugs and bizarre performance cliff edges caused by one thing: the gap between what your code looks like it does and what your CPU actually does. Let me show you what's really happening inside that black box.
You've got 64GB of RAM. Your Java process is using 8GB. Your node is using 5GB. Everything looks fine until your checkout service dies at 11:59 PM on Black Friday. The OOM killer struck, and you had no idea this was even possible. Let me explain exactly why this happens and how to prevent it.