Council: A Structured Dialogue Framework for Claude

Council — A Crucible is a structured dialogue framework designed to run within a single Claude context window. It uses persona framing to create four distinct modes of engagement: rigorous interrogation, generative action, lived experience, and unformed intuition.
How It Works
The framework operates through register instruction — precise persona descriptions that bias the model's output direction rather than commanding it directly. This approach allows the tool to serve as a thinking aid rather than a replacement for judgment, providing different cognitive and emotional registers on demand based on what the user is trying to accomplish.
Practical Application
The source describes the framework as a tool for thinking where "you think against it rather than into it, and the thinking gets sharper in the friction." It's designed to match different modes of engagement to specific user needs, whether that requires rigorous questioning, creative generation, experiential perspective, or intuitive exploration.
The project is available on GitHub at https://github.com/kpt-council/council-a-crucible.
📖 Read the full source: r/ClaudeAI
👀 See Also

AI Sandbox Manager: LXC-Based Sandbox for Codex with GPU Passthrough and Computer Use on Headless Linux
ai-sandbox-manager is an open-source LXC-based sandbox for Codex agents on headless Linux. It provides GPU passthrough, full sudo access, persistent environments, and computer use via CUA, all while isolating the agent from the host OS.

TradingView MCP Server Enables Claude to Backtest Trading Strategies
A developer has released an MCP server that allows Claude to backtest six trading strategies using Yahoo Finance data without API keys. Setup involves adding one line to the claude_desktop_config.json file.

Benching local Qwen 3.6 27B as a Codex validator co-agent
A developer built a reproducible eval suite to test Qwen 3.6 27B GGUF profiles (llama.cpp) as a sidecar validator for Codex, finding 128k context profiles necessary for long-context tasks and minimal accuracy loss with q8 KV cache.

KV Cache Reuse for Long Conversations on Apple Silicon Delivers 200x Speedup
A developer implemented session-based KV cache reuse for local LLM inference using Apple's MLX framework, achieving a 200x improvement in time-to-first-token at 100K context length. The approach keeps the KV cache in memory across conversation turns, processing only new tokens.