Mixture of Experts (MoE) analogy
Modern AI models don't activate every expert.
They route each input to the most relevant ones.
Social graphs work similarly.
Your casts are more likely to reach the right audience when your expertise is clear and consistent.
If every post targets a different domain, routing becomes noisy.
Become an expert the graph knows when to call.
#ai #moe #llm #neynar #farcaster #devthoughts

Inference latency analogy
The faster people understand your idea, the farther it travels.
High cognitive latency kills distribution.
The best technical posts have:
low parsing cost
high information density
one memorable insight
Like optimized inference, efficient communication scales better than complex execution.
Reduce latency. Increase signal.
#ai #inference #llm #neynar #farcaster #devthoughts

Inference latency analogy
The faster people understand your idea, the farther it travels.
High cognitive latency kills distribution.
The best technical posts have:
low parsing cost
high information density
one memorable insight
Like optimized inference, efficient communication scales better than complex execution.
Reduce latency. Increase signal.
#ai #inference #llm #neynar #farcaster #devthoughts

RAG analogy for social reputation
A cast doesn’t exist in isolation.
When people read your post, they retrieve context: previous casts, replies, expertise, consistency.
Just like RAG, the output quality depends on the retrieved history.
Strong reputation provides relevant context. Weak reputation forces the graph to guess.
Every interaction becomes future retrieval data.
Build a knowledge base worth querying.
#ai #rag #machinelearning #neynar #farcaster #devthoughts

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