[ 68938980.000 ] posts.x: Full talk on measuring perceived latency instead of dashboard latency, and the async tool pattern that keeps a voice agent talking while it works:  |   [ 68938980.000 ] posts.x: A voice agent can measure fast and still sound slow to the person on the phone. @codeSTACKr, Staff Developer Advocate at LiveKit, takes that gap…  |   [ 68922900.000 ] posts.x: Full workshop on the six choices behind any agent benchmark score, and what Gemini 3.5 Flash, GPT 5.5, and Fable actually cost to run through it:  |   [ 68922900.000 ] posts.x: A model can look like the clear winner on a leaderboard and still be the worse pick once you check the bill. @WolframRvnwlf, an AI Evangelist at…  |   [ 68912460.000 ] posts.x: Full talk on where vector RAG hits its limit and how a knowledge graph closes it, including a live build in Neo4j Aura:  |   [ 68912460.000 ] posts.x: Vector search can answer anything a search engine could. It stalls once a question needs three or more hops across documents, things like tracing a…  |   [ 68869740.000 ] posts.x: Full talk on why agents need a persistent graph of what data actually means, not just access to the data, plus a worked example of an agent reaching…  |   [ 68869740.000 ] posts.x: Give an agent access to what a table's rows actually mean, and task success on complex data changes jumps almost 35 points over the same model…  |   [ 68847720.000 ] posts.x: Full talk on the document extraction pipeline behind JMail, from redaction cleanup to schema design:  |   [ 68847720.000 ] posts.x: Over three million pages of redacted, scanned, and handwritten government documents sat between an internet joke and a real extraction problem…  |   [ 68833800.000 ] posts.x: Full talk on building evaluators for a sample support agent and using a coding agent to mine production traces for failures:  |   [ 68833800.000 ] posts.x: An agent's dashboards can look perfectly green while its actual answers are wrong, and that gap between system health and response quality is what…  |   [ 68825580.000 ] posts.x: Full talk on owning versus renting GPU inference, the KV cache math behind it, and where quantization and speculative decoding actually help:  |   [ 68825580.000 ] posts.x: Speculative decoding is supposed to make inference faster. @aiDotEngineer posted "Agents That Own Their Inference" to YouTube, where @labeveryday, a…  |  

[ 68938980.000 ] posts.x: Full talk on measuring perceived latency instead of dashboard latency, and the async tool pattern that keeps a voice agent talking while it works:  |   [ 68938980.000 ] posts.x: A voice agent can measure fast and still sound slow to the person on the phone. @codeSTACKr, Staff Developer Advocate at LiveKit, takes that gap…  |   [ 68922900.000 ] posts.x: Full workshop on the six choices behind any agent benchmark score, and what Gemini 3.5 Flash, GPT 5.5, and Fable actually cost to run through it:  |   [ 68922900.000 ] posts.x: A model can look like the clear winner on a leaderboard and still be the worse pick once you check the bill. @WolframRvnwlf, an AI Evangelist at…  |   [ 68912460.000 ] posts.x: Full talk on where vector RAG hits its limit and how a knowledge graph closes it, including a live build in Neo4j Aura:  |   [ 68912460.000 ] posts.x: Vector search can answer anything a search engine could. It stalls once a question needs three or more hops across documents, things like tracing a…  |   [ 68869740.000 ] posts.x: Full talk on why agents need a persistent graph of what data actually means, not just access to the data, plus a worked example of an agent reaching…  |   [ 68869740.000 ] posts.x: Give an agent access to what a table's rows actually mean, and task success on complex data changes jumps almost 35 points over the same model…  |   [ 68847720.000 ] posts.x: Full talk on the document extraction pipeline behind JMail, from redaction cleanup to schema design:  |   [ 68847720.000 ] posts.x: Over three million pages of redacted, scanned, and handwritten government documents sat between an internet joke and a real extraction problem…  |   [ 68833800.000 ] posts.x: Full talk on building evaluators for a sample support agent and using a coding agent to mine production traces for failures:  |   [ 68833800.000 ] posts.x: An agent's dashboards can look perfectly green while its actual answers are wrong, and that gap between system health and response quality is what…  |   [ 68825580.000 ] posts.x: Full talk on owning versus renting GPU inference, the KV cache math behind it, and where quantization and speculative decoding actually help:  |   [ 68825580.000 ] posts.x: Speculative decoding is supposed to make inference faster. @aiDotEngineer posted "Agents That Own Their Inference" to YouTube, where @labeveryday, a…  |