Using an AudioLLM's local speaker tags to guide global diarization (and why a 0.

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1she2b2/from_1939_to_voice_clones_in_3_seconds_the_full/"> <img alt="From 1939 to voice clones in 3 seconds

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s2e0d5/built_an_opensource_llm_router_for_consumer_gpus/"> <img alt="Built an open-source LLM router for

<!-- SC_OFF --><div class="md"><p>One thing we’ve been noticing lately is that a surprisingly large percentage of day-to-day AI workflows no longer seem to require frontier-scale

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1t0ea9u/i_built_a_fully_local_meeting_ai_that_runs/"> <img alt="I built a fully local meeting AI that runs

<!-- SC_OFF --><div class="md"><p>Link to the paper:</p> <p><a

<!-- SC_OFF --><div class="md"><p>Curious what people here actually care about most when mixing local models with cloud models.</p> <p>I keep coming back to the same problem:

<!-- SC_OFF --><div class="md"><p>Most of my LLM cost was on the wrong tier of work.</p> <p>Classification, extraction, JSON formatting, summarization I'm going to review anyway.

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s44cfk/deploying_voice_models_across_multibackends_and/"> <img alt="Deploying voice models across