How do you guys deal with long context in LLM models?

<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

<!-- SC_OFF --><div class="md"><p>Hi,</p> <p>I taught Computer Science for 30 years in a French School of Electrical Engineering, Computer Science Department.</p> <p>I recently

<!-- SC_OFF --><div class="md"><p>I organize everything by topic (work, study, documents, buying, recipes, etc.), and each topic stays in one long chat. Over time, those chats

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1tbzr64/qwen36_just_stops/"> <img alt="qwen3.6 just stops"

<!-- SC_OFF --><div class="md"><p>we've been building an open source red teaming tool for AI agents. wanted to share what we keep finding because i don't think enough people are

<!-- SC_OFF --><div class="md"><p>we run an agent thing in production and we use langfuse for traces.</p> <p>last month our agent started refusing requests it should have

<!-- SC_OFF --><div class="md"><p>I build context/harness optimization tooling, so provider-side serialization quirks actually matter to me. If you're optimizing over prompts, you