Go
A statically typed, compiled language designed around explicit control flow, minimal abstraction, and readable concurrency.
Go's design trades expressiveness for predictability: no generics sprawl, no hidden allocations from framework magic, no GC stop-the-world surprises at scale, and no event loop that blocks under I/O pressure. The stdlib handles HTTP, JSON, crypto, and SQL without third-party dependencies. Where it wins is workloads that demand consistent latency and straightforward operability — a single static binary, no runtime to install, no classpath to untangle.
My default package layout: cmd/ for entry points, internal/ for packages that shouldn't be imported externally, and a flat-ish structure under internal/ until the codebase actually earns more nesting. I don't reach for pkg/ unless I'm building a library — for services it's mostly noise. Error handling: I wrap with fmt.Errorf and %w at call boundaries, use sentinel errors sparingly (only when callers need to match on them with errors.Is), and avoid custom error types unless I need to carry structured data. Interfaces stay small and defined at the consumer, not the producer — that's how I get testability without a mocking framework. For profiling, my workflow is: attach pprof handlers in dev builds, hit the service under realistic load, pull a 30-second CPU profile and a heap profile, load them in go tool pprof, and look at the top allocators before I look at CPU time. Most Go performance problems I've encountered are allocation-related, not compute-bound. Benchmarks live in _test.go files and I run them with -benchmem so I'm always looking at allocs/op alongside ns/op.
Service Architecture
I lay out services with cmd/ for binaries, internal/ for private packages, and constructor-based DI — no framework, just functions that accept interfaces. Dependencies flow inward; nothing in internal/ knows about the HTTP layer.
Performance Optimization
I instrument services with net/http/pprof, capture CPU and heap profiles under load, then work through go tool pprof's top and list views. Most issues I find are in the heap profile — unexpected allocations inside hot loops or per-request struct copies that should be pooled.
Testing Strategy
I write table-driven tests with t.Run subtests and define narrow interfaces at the call site so I can swap in a hand-written stub without a mocking library. Integration tests run against real dependencies via testcontainers or a local compose file, isolated from unit tests by a build tag.
High-Throughput APIs
Go's goroutine scheduler handles tens of thousands of concurrent connections on a small thread pool, making it well-suited for REST or gRPC services where you need predictable p99 latency without tuning a thread pool or configuring an async runtime.
CLI Tools & Automation
Cross-compiling to a single static binary means the tool runs on any target OS without installing a runtime, managing a virtualenv, or shipping a JAR — the binary is the deployment unit.
Data Processing Services
Goroutines and channels map naturally to pipeline-style workloads: fan-out reads, concurrent transforms, and fan-in aggregation without the overhead of OS threads or the complexity of async/await chains.
Let's talk Go.
No pitch. Just a technical conversation about the problem you're working on.