Cache the Gemini SDK in your meeting-notes summarizer with Crowkis
Building meeting-notes summarizers on the Gemini SDK? Add a semantic cache so similar summaries requested repeatedly 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 meeting-notes summarizers on the Gemini SDK? Add a semantic cache so similar summaries requested repeatedly stop costing full price.
Building RAG document search on LiteLLM? Add a semantic cache so the same questions re-running retrieval over the same corpus stop costing full price.
Building legal document assistants on Haystack? Add a semantic cache so the same clauses and questions across matters stop costing full price.
Prompt injection: detection at the infrastructure layer. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Knowledge-graph memory, stores subject-relation-object edges you can traverse multi-hop, so 'who works at the customer's company?' is a graph walk, not a guess. Here's how Crowkis does it and why it matters for cost and safety.
LangChain's built-in caches are great until a user rephrases the question. Swap in a semantic cache and the paraphrases start hitting, without changing a line of your chains.
Building sales enablement tools on Dify? Add a semantic cache so reps asking the same product questions stop costing full price.
Building SQL generation tools on the Gemini SDK? Add a semantic cache so the same schema questions and query shapes stop costing full price.
Building internal copilots on LiteLLM? Add a semantic cache so employees asking overlapping questions of the same knowledge base stop costing full price.
Building education tutors on Haystack? Add a semantic cache so students asking the same concepts thousands of times stop costing full price.
Freshness vs speed: the LLM cache tradeoff. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Bi-temporal (time-travel) memory, answers what the agent believed at a past instant using validity windows, so you can reconstruct 'what did we know then?'. Here's how Crowkis does it and why it matters for cost and safety.
A cache that forgets everything on restart isn't much of a memory. Crowkis writes durably first, so a crash, a deploy, or a reboot never costs you a single learned answer.
Building knowledge base assistants on Dify? Add a semantic cache so the same lookups across a team all day stop costing full price.
Building devops copilots on the Gemini SDK? Add a semantic cache so the same runbook and incident questions stop costing full price.
Building chatbots on LiteLLM? Add a semantic cache so high-volume conversational traffic that repeats constantly stop costing full price.
Building multi-agent systems on Haystack? Add a semantic cache so a swarm of agents asking overlapping questions stop costing full price.
Why Rust for AI infrastructure. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Auto fact extraction from conversations, pulls durable facts out of a transcript deterministically, dropping questions, greetings, and filler, with no model call. Here's how Crowkis does it and why it matters for cost and safety.
In any shared cache, one crafted answer could get served to thousands. So every write runs a five-stage gauntlet before it's ever eligible to be reused.