347 tests and a murder weapon: how the suite is organized
Bottom-heavy by design: the layers that hold your data get the most hostile coverage, and the smoke suite's signature move is killing the process to prove a point.
Notes from the nest · 980 posts
Engineering notes written by the people building Crowkis. Comparisons, use cases, economics, internals, security, operations, and nothing written just to rank.
Bottom-heavy by design: the layers that hold your data get the most hostile coverage, and the smoke suite's signature move is killing the process to prove a point.
Product copy, help docs, and templates get re-translated continuously as releases churn. Most of the content didn't change. Stop paying as if it did.
Building sales enablement tools on Spring AI? Add a semantic cache so reps asking the same product questions stop costing full price.
Building SQL generation tools on the OpenAI Python SDK? Add a semantic cache so the same schema questions and query shapes stop costing full price.
Building internal copilots on Instructor? Add a semantic cache so employees asking overlapping questions of the same knowledge base stop costing full price.
Building education tutors on LlamaIndex? Add a semantic cache so students asking the same concepts thousands of times stop costing full price.
Add a semantic cache to Spring AI so repeated and reworded questions are served for free, no rewrite, self-hosted.
Building knowledge base assistants on Spring AI? Add a semantic cache so the same lookups across a team all day stop costing full price.
Building devops copilots on the OpenAI Python SDK? Add a semantic cache so the same runbook and incident questions stop costing full price.
Building chatbots on Instructor? Add a semantic cache so high-volume conversational traffic that repeats constantly stop costing full price.
Building multi-agent systems on LlamaIndex? Add a semantic cache so a swarm of agents asking overlapping questions stop costing full price.
Durable, per-user memory for Spring AI agents that survives restarts and consolidates contradictions, self-hosted, zero egress.
vLLM's prefix caching saves GPU work inside one inference server. Crowkis saves the inference itself. You probably want both, but only one cuts the bill to zero on a hit.
Building research assistants on Spring AI? Add a semantic cache so overlapping literature and summary questions stop costing full price.
Building API documentation bots on the OpenAI Python SDK? Add a semantic cache so the same endpoint questions from every developer stop costing full price.
Building AI search on Instructor? Add a semantic cache so popular queries hit again and again stop costing full price.
Building voice assistants on LlamaIndex? Add a semantic cache so latency-sensitive, repetitive spoken queries stop costing full price.
Add a semantic cache to n8n so repeated and reworded questions are served for free, no rewrite, self-hosted.
Reports, tickets, calls, and articles get summarized on every view, by every viewer, in every digest. The document didn't change between viewers. The bill did.
Building contract analysis tools on Spring AI? Add a semantic cache so the same clause questions across documents stop costing full price.