De ce glue code e cea mai importantă abilitate din inginerie modernă
Tehnologiile de bază sunt comoditate. Valoarea reală trăiește în coasere — codul care face sisteme disparate să funcționeze împreună.
Most production systems aren't bottlenecked on algorithms anymore. They're bottlenecked on the code between components — the API gateway that routes requests, the queue that fans out events, the worker that retries with backoff, the migration that shifts data between two boring services without losing a row.
That code rarely makes it into conference talks. It doesn't ship as a polished open-source library. It's yours to write, every time, because it depends on the exact shape of your system.
The new bottleneck
In the AI era this is even more true. Every team has access to the same models. The differentiation isn't "which LLM did you pick?" — it's the prompt versioning, the eval harness, the fallback chain, the retry-with-different-model logic, the cache layer, the feature flag that lets you A/B-test prompts in production.
In our recent fintech project, we spent maybe 5% of the engineering effort on the LLM call itself. The other 95% was the glue: regulatory ingestion, schema-validated outputs, audit trails, eval pipelines, kill switches, observability.
What this means for hiring
We hire for taste in seams, not for memorized framework APIs. Can you read a system and tell us the three failure modes that are most likely to bite in production? Can you design a small extension that adds capability without making the failure modes worse? That's the senior bar.
What this means for clients
When you commission a system from us, the spec usually undersells where the work goes. The "build a chat with our docs" ticket is 10% chat, 90% retrieval quality, eval, security, latency, cost control. If your vendor estimates that as a 2-week job, find a different vendor.
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