What's one local AI workflow you wish you'd discovered sooner?
Mirrored from r/LocalLLaMA for archival readability. Support the source by reading on the original site.
There are a lot of posts about the models and benchmarks, but I am more interested in the workflows that people use. What is one workflow that really saved you time or made your local LLM more useful?
It could be anything—RAG, MCP, coding agents, organizing prompt, document indexing, automation or something else entirely. What was it, and why did it make such a big difference in your day-to-day workflow?
[link] [comments]
More from r/LocalLLaMA
-
Uncensored Multi-Model Releases, LongCat-Flash-Lite-Sparse with MTPs and LSAs, Qwen3.8-27B with MTPs, Qwen3.5-122B-A10B with MTPs, Qwen3-Coder-Next and Laguna-S2.1 with Vision, All Available in GGUF Format! Bonus: Links to my llama.cpp Fork for LongCat-Flash-Lite Support and…
Aug 30
-
Got MiniMax H3 video generation running in TensorSharp
Aug 30
-
Qwen3.8-Flash-Next NVFP4 2xDGX Spark config: 50t/s decode, 2,900t/s prefill
Aug 30
-
Don't Sleep on EXL3 Quants
Aug 30
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.