There is a simple test for whether a software system is doing something hard. Look at the questions it has to answer. If most of them have the shape "what is the value of X?", the system is doing lookups. If most of them have the shape "
The security conversation around AI coding agents focuses on the wrong variable. The concern, as usually stated, is that agents are powerful — they can write code, execute commands, push to repositories, trigger deployments. The implication is that limiting agent capabilities limits the risk. It doesn't. The risk isn&
There is a particular kind of intellectual dishonesty that the AI industry has normalised — dressing up workarounds as solutions, and calling incremental noise reduction "solving hallucination." I want to be precise about why this bothers me. Not because the work is useless. Some of it is useful. But
The architecture industry spent a decade treating edge and cloud as two places where computation happens. You decide which workloads belong where, deploy accordingly, and call it a distributed system. That mental model is now the primary source of edge architecture failures. The shift isn't from "edge
Engineering teams at mid-scale routinely pay $30–50k/month for telemetry infrastructure and simultaneously cannot answer the questions that matter during an incident. The dashboards exist. The traces exist. And when an incident occurs that affects 0.3% of users, nobody can find it. The conventional response is to instrument
The industry is consolidating back toward monoliths. Modular monoliths are respectable again. "Right-sized services" has replaced "decompose everything" as the default recommendation. Senior engineers who built distributed systems at scale are publicly advocating for fewer services. The conventional explanation: teams underestimated operational complexity. Network hops, distributed
The framing you'll see in most security writeups goes like this: AI models hallucinate package names, attackers register those names, developers get compromised. The implied fix is obvious — improve the models, reduce hallucinations, problem shrinks. The Attack Doesn't Need Hallucinations to Be Frequent. It Needs Them
When a node is saturated — CPU pegged, latency climbing, queues building — the symptom looks the same regardless of cause. The monitoring shows one overloaded node. It does not tell you why. Underneath that symptom are two fundamentally different problems, and the intervention that fixes one does nothing for the other.
Consistent hashing is one of those techniques that gets introduced as a solution and rarely examined as a design decision. You learn it, you understand why it's better than modulo hashing, and it goes into your mental toolkit as "the right way to partition data." What
Imagine you’re a security guard at a massive corporate building with millions of employees. Your job is to quickly check whether each…
Imagine you’re the manager of a busy restaurant. Every day, hundreds of customers come through your doors, and you want to understand how…
Why AI-Driven Development Doesn't Solve the Maintenance Problem — It Transforms It Software maintenance has always been the dominant cost. Estimates have consistently placed maintenance at 60–80% of total lifecycle cost for decades. The industry's response has been a long arc of attempts to reduce the