Oracle Health Clinical AI Agent, Clinical Note Helps UK Doctors Spend More Time on Patient Care - Oracle
Oracle Health Clinical AI Agent, Clinical Note Helps UK Doctors Spend More Time on Patient Care Oracle
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agent![[D]Is AI cost tracking/attribution a real problem or just something you deal with later?](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-law-gavel-Y5gyqPB5EUhGETiZdpLc9M.webp)
[D]Is AI cost tracking/attribution a real problem or just something you deal with later?
Hey, I’ve been noticing something while working with AI APIs (OpenAI, Anthropic, etc.) and wanted to get real input from people actually building. Once you move beyond a simple feature and start having multiple agents/workflows/users, it becomes hard to answer things like: Which feature is actually costing the most? Which user or workflow is driving usage? Why did cost spike suddenly? Are we close to breaking our budget? Most of the time, provider dashboards just show total usage, not where it’s coming from. So I’m curious: Do you actually face this problem in production? Or is it something that doesn’t matter until you scale a lot? How are you currently handling it (if at all)? Would you even bother using a separate tool for this, or just build internal logging? Trying to understand if th
![[P] Implemented ACT-R cognitive decay and hyperdimensional computing for AI agent memory (open source)](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-matrix-rain-CvjLrWJiXfamUnvj5xT9J9.webp)
[P] Implemented ACT-R cognitive decay and hyperdimensional computing for AI agent memory (open source)
Built a memory server for AI agents (MCP protocol) and implemented two cognitive science techniques in v7.5 I wanted to share. ACT-R Cognitive Decay Memory nodes fade using the base-level activation formula: B_i = ln(Sum t_j -d ) Old, rarely-accessed memories lose salience. Frequently-accessed ones stay vivid. This keeps agent context clean without manual pruning - only "warm" memories surface at retrieval time. Hyperdimensional Computing (HDC) Routing Agent state is encoded as XOR of three 768-dim binary hypervectors: state x role x action. Routing uses Hamming distance rather than cosine similarity - works surprisingly well for sparse, structured agent state. Background Edge Synthesis A background process autonomously discovers and links semantically similar memory nodes. The graph selfo
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