Optimization

eBPF (extended Berkeley Packet Filter) lets you attach tiny sandboxed programs to kernel events: syscalls, network packets, scheduler decisions, and filesystem calls. You collect detailed performance data in real time. No kernel source changes, no custom modules, no service restarts. With bpftrace one-liners and the BCC toolkit, you can measure per-process disk latency, trace TCP connections, profile CPU hotspots, and find memory leaks on production Linux. Overhead is usually under 2%.

The Short Answer

When Zigbee devices keep dropping, the culprit is almost always the mesh topology, not the sensors. Add mains-powered routers every 10 to 15 meters. Move the coordinator away from the WiFi router and any USB 3.0 port. Switch to Zigbee channel 15, 20, or 25 to dodge WiFi. Use the network map in Zigbee2MQTT or ZHA to spot weak links. Roughly 90% of complaints trace back to three things: too few routers, a coordinator in an RF hot zone, and a Zigbee channel that clashes with WiFi.

Zig is a modern systems language built to replace C. It keeps manual memory management and zero hidden control flow: no garbage collector, no runtime, and one statically-linked binary that runs anywhere. Install Zig from ziglang.org/download , scaffold a project with zig init, and you’ll have a working CLI tool in about 50 lines using comptime, error unions, and first-class C interop. The killer feature: zig build-exe -target x86_64-linux-musl cross-compiles to any target from any host with zero toolchain setup.

Python is excellent for most of what developers throw at it - API servers, data pipelines, automation scripts, machine learning glue code. But CPU-bound work is a different story. When you’re parsing 500MB log files, running simulation loops, or crunching millions of rows in a tight inner loop, you’re going to hit a wall. Not always, but often enough that it becomes a real problem.

The solution is not to rewrite your entire application in Rust. That’s dramatic and usually unnecessary. The better approach is to profile your code, find the 5-10% that consumes most of the CPU time, and rewrite just that part in Rust. The rest of your codebase stays Python. Your interfaces stay Python. You just swap out the slow function for a fast one.

SQLite is the right default database for most apps. With WAL mode on, it gives you unlimited concurrent readers and one writer. That writer can sustain thousands of transactions per second on modern NVMe drives. SQLite also handles files up to 281 TB and needs zero config, zero extra processes, and zero network hops. Start with SQLite. Move to PostgreSQL only when you hit a real, measured limit, not a guess.

Prompt caching lets you skip re-processing identical prefix tokens across LLM API calls, cutting costs by up to 90% and reducing latency by 50-80% on requests that share long system prompts, few-shot examples, or document context. Anthropic’s Claude offers prompt caching with explicit cache_control breakpoints, OpenAI’s GPT-4o supports automatic prefix caching, and local inference servers like vLLM and SGLang implement prefix caching natively. The rule: put your static, reusable prompt content first and the variable user query last.