Cache Dify in your research assistant with Crowkis
Building research assistants on Dify? Add a semantic cache so overlapping literature and summary questions 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 research assistants on Dify? Add a semantic cache so overlapping literature and summary questions stop costing full price.
Building API documentation bots on the Gemini SDK? Add a semantic cache so the same endpoint questions from every developer stop costing full price.
Building AI search on LiteLLM? Add a semantic cache so popular queries hit again and again stop costing full price.
Building voice assistants on Haystack? Add a semantic cache so latency-sensitive, repetitive spoken queries stop costing full price.
Sub-millisecond retrieval: why in-process beats a network hop. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Input guardrails (CGUARD), scans prompts for injection and jailbreaks after normalizing leetspeak, whitespace, and zero-width evasion, model-free and stateless. Here's how Crowkis does it and why it matters for cost and safety.
A single time-to-live for every cached answer is a bug in disguise. Some facts are true for a decade; some are stale in minutes. Freshness has to know the difference.
Building contract analysis tools on Dify? Add a semantic cache so the same clause questions across documents stop costing full price.
Building customer support bots on the Mistral SDK? Add a semantic cache so repeat questions from every customer, all day stop costing full price.
Building ecommerce assistants on LiteLLM? Add a semantic cache so the same product and policy questions across shoppers stop costing full price.
Building email drafting tools on Haystack? Add a semantic cache so similar drafts requested over and over stop costing full price.
FinOps for LLMs: attributing AI spend. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Output guardrails (COUTCHECK), scans responses for PII, toxicity, and JSON validity before they ship, so the model's output is checked at the trust boundary. Here's how Crowkis does it and why it matters for cost and safety.
Most caches treat every hit as equally trustworthy, a binary yes. But LLM answers are probabilistic and time-sensitive. Crowkis scores its confidence before it serves.
Building onboarding assistants on Dify? Add a semantic cache so every new hire asking the same first questions stop costing full price.
Building coding assistants on the Mistral SDK? Add a semantic cache so the same explanations and boilerplate reasoning, dozens of times a day stop costing full price.
Building healthcare Q&A assistants on LiteLLM? Add a semantic cache so recurring policy and triage questions stop costing full price.
Building code review bots on Haystack? Add a semantic cache so the same review patterns across pull requests stop costing full price.
Evals without an LLM judge. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Model-free online evals (CEVAL), grades output with deterministic evaluators (toxicity, PII, relevance, JSON validity and more) and tracks the results over time, no LLM-judge. Here's how Crowkis does it and why it matters for cost and safety.