Just to put things in context...

<!-- SC_OFF --><div class="md"><p>The news about Ulysses Sequence Parallelism enabling million-token contexts is fascinating for local LLMs. While the potential for deeper context

<!-- SC_OFF --><div class="md"><p>How do you guys deal with long context, for example while coding, when you’re going back and forth for adjustments or fixing some errors and

<!-- SC_OFF --><div class="md"><p>What are your thoughts/what is the consensus on local context compression model size?</p> <p>Are you guys using small MoE models to do this

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s2bne8/context_shifting_sliding_window_rag/"> <img alt="Context Shifting + sliding window + RAG"

<!-- SC_OFF --><div class="md"><p>Been building AI agents for a while and kept hitting the same wall: agents that work great at the start of a session and quietly fall apart by

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1wlss89/postulate_context_compaction_is_good_for/"> <img alt="Postulate: Context compaction is good for

<!-- SC_OFF --><div class="md"><p>Is this possible for Qwen3.8-27B in LM Studio? </p> <p>Something similar to what ChatGPT/Codex does. I use that as an example because it does it

<!-- SC_OFF --><div class="md"><p>After experimenting with llama.cpp, I'm wondering a thing.</p> <p>Let's say we have an LLM with a context size of 128k. Now let's say we want