Cache Semantic Kernel in your code review bot with Crowkis
Building code review bots on Semantic Kernel? Add a semantic cache so the same review patterns across pull requests stop costing full price.
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.
Building code review bots on Semantic Kernel? Add a semantic cache so the same review patterns across pull requests stop costing full price.
Building research assistants on LangChain? Add a semantic cache so overlapping literature and summary questions stop costing full price.
i8 vector quantization, stores vectors in int8 for about 4x less memory, so more of your working set fits in RAM. Here's how Crowkis does it and why it matters for cost and safety.
HR policy, expense rules, deploy commands, VPN setup, every employee rediscovers them through your copilot, billed per discovery. Give the company one memory.
CEVAL runs deterministic evaluators, toxicity, PII, relevance, JSON validity and more, over an input/output pair, and tracks the results on /metrics.
CDEDUP works, and at 1,340 vectors it froze the whole server for 150 seconds in our harness. Here's the honest finding, why it happens, and what it means for how you should run dedup.
Building RAG document search on LangChain.js? Add a semantic cache so the same questions re-running retrieval over the same corpus stop costing full price.
Building legal document assistants on Ollama? Add a semantic cache so the same clauses and questions across matters stop costing full price.
Building data analysis agents on Semantic Kernel? Add a semantic cache so repeated tool calls and the same analytical questions stop costing full price.
Building contract analysis tools on LangChain? Add a semantic cache so the same clause questions across documents stop costing full price.
FinOps chargeback and savings receipts, meters spend across team, project, env, and cost center, and anchors savings receipts in an audit chain. Here's how Crowkis does it and why it matters for cost and safety.
Agents call the same tools with the same arguments constantly. CTOOLSET and CTOOLGET cache tool results keyed by tool plus exact arguments, so a deterministic call runs once and serves many.
After the 2026 gateway compromise, 'how many packages are in your hot path?' became a real procurement question. Our answer is a number: zero.
Your vector store finds the chunks fast. Then the model re-synthesizes the same answer from the same chunks, thousands of times. That second step is the bill.
CPROMPT stores named prompt templates with versioning, variable rendering, sticky A/B splits, and rollback, all from the CLI, all surviving restart.
Building internal copilots on LangChain.js? Add a semantic cache so employees asking overlapping questions of the same knowledge base stop costing full price.
Building education tutors on Ollama? Add a semantic cache so students asking the same concepts thousands of times stop costing full price.
Building HR assistants on Semantic Kernel? Add a semantic cache so the same policy questions from every employee stop costing full price.
Building onboarding assistants on LangChain? Add a semantic cache so every new hire asking the same first questions stop costing full price.
Model migration without a cold cache, canary and migration workflows carry cache value across a model upgrade, so a new model doesn't cold-start your hit rate. Here's how Crowkis does it and why it matters for cost and safety.