jamesob
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local-llm
A comprehensive guide for building and configuring a high-end local machine to run state-of-the-art LLMs, with detailed hardware choices, BIOS tuning, and Docker-based model serving.
AI repository intelligence
FR-AI / ANALYSISWhy it is worth attention
It solves the common problem of losing session context during compaction in AI coding assistants, providing automatic state backup, recovery, and a configurable warning system without requiring manual user intervention.
Who it is for
- Developers using Claude Code or Codex for long-running coding sessions
- Teams that rely on AI pair programmers and need to maintain context across compaction
- Advanced users who want fine-grained control over compaction behavior and recovery notes
- Plugin developers interested in hook-based augmentation of AI runtime compaction
Use cases
- Automatically preserving the full task plan, decisions, and file state before a /compact command
- Recovering the agent's context exactly once after compaction, including skill invocations and editing history
- Receiving a proactive warning when context usage approaches a threshold, along with a brief state recap
- Manually capturing richer recovery notes via the /compact-plus skill before an upcoming compaction
Strengths
- Fully transparent operation—no extra commands or manual steps required for basic use
- Works on both Claude Code and Codex with the same hook-based architecture
- Incremental transcript processing and configurable squashing reduce LLM token costs
- Handles edge cases like thread-spawn subagents and runtime hook ordering differences
Considerations
- Requires a compatible LLM backend (claude -p or codex exec) and a subscription for the fallback model
- Configuration involves environment variables with multiple tunables, which may overwhelm casual users
- Relies on runtime-specific hook APIs that could change with future versions of Claude Code or Codex
README quick start
Usage
Description
Claude Code plugin: preserve and restore working state around /compact
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