How to capture, clean, connect, and remember. Practical guides, use cases, and deep dives from the team building Kiomon's memory layer for AI agents.
Engineering
Why concatenating two result lists fails, and how reciprocal rank fusion (RRF, k=60) lets Kiomon search lexically and by meaning simultaneously with an automatic fallback.
Product
How Kiomon solves the digital hoarding paradox with autonomous weekly dossiers, 5 intelligence pillars, and zero dead-end actions across your memory graph.
Engineering
Why concatenating two result lists fails, and how reciprocal rank fusion (RRF, k=60) lets Kiomon search lexically and by meaning simultaneously with an automatic fallback.
Product
The bottleneck is not the model, but what the agent remembers about your work. Here is the case for persistent memory over ephemeral prompts.
Product
A walk through the capture, clean, connect, and remember pipeline, and how memory graphs organize agent context.
Product
Why unconstrained agent self-reflection leads to memory pollution, and how the staged Review Inbox guarantees clean, trusted long-term recall.
Product
A deep dive into cognitive memory kinds, the human-in-the-loop lifecycle, and how graph consolidation keeps agent recall sharp over time.
Engineering
Learn how smart search fallbacks and self-summarizing context windows keep agent Q&A affordable, accurate, and on-topic.
Engineering
How background jobs compute TF-IDF and cosine similarity on every save so auto-linking stays fast, isolated, and fully reversible.
Engineering
Bringing graph capabilities directly to your local machine for sub-millisecond retrieval without cloud round-trips.
Engineering
A side-by-side cost and latency model comparing prompt-stuffing with Surgical MCP Graph Retrieval.
Engineering
How Kiomon swaps vector models in-process without dropping a single query.
Engineering
How Kiomon keeps your memory fresh with chunked cursors and durable retries across Notion, Confluence, and Drive.
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Kiomon: The first brain for your AI agent.
