SuperContext: A Persistent Memory Framework for AI Coding Agents
What SuperContext Solves
The developer built this after 1,500+ sessions and months of daily use across 60+ projects, getting tired of re-explaining their codebase every session. The core problem they identified: typical solutions involve making instruction files larger, but a 2,000-line CLAUDE.md eats context window space before questions are asked, and AI ends up ignoring half of it.
Architecture: Targeted Files Instead of Monolithic Docs
SuperContext takes the opposite approach with small, targeted files loaded only when relevant:
- Constitution (~200 lines, always loaded): Global rules, routing, preferences
- Living Memory (~50 lines, always loaded): Behavioral gotchas that prevent repeated mistakes
- Project Brains (loaded on entry): Per-project business rules, schemas, changelogs
- Knowledge Store (on demand): Searchable SQLite database for infrastructure, APIs, reference data
- Session Memory: Automatic conversation logging so your AI recalls past decisions
What's Included
The repository contains two main components:
- The full guide covering theory, architecture, anti-patterns, and tool-specific setup for Claude Code, Cursor, Copilot, Codex, Aider, and others
- An executable prompt that you hand to your AI with the instruction "run this" - it discovers your projects, migrates existing content, and builds the whole system in approximately 10 minutes with no manual setup
Development Context
The framework was developed while building construction management integrations (Vista, Procore, Monday.com), where getting context wrong means real production problems. The developer reports that with this system, their AI went from "helpful but forgetful" to genuinely knowing their systems.
📖 Read the full source: r/ClaudeAI
👀 See Also
Orion: Bypassing CoreML to Run and Train LLMs Directly on Apple Neural Engine
Orion is an open-source Objective-C system that bypasses Apple's CoreML to run and train LLMs directly on the Apple Neural Engine (ANE), achieving 170+ tokens/s for GPT-2 124M decode and stable multi-step training on a 110M parameter transformer.
Benchmark Results: 15 LLMs Tested on 38 Real Workflow Tasks
A developer benchmarked 15 cloud and local LLMs on 38 tasks from their actual workflow, including CSV transforms, letter counting, modular arithmetic, and format compliance. Claude 3.5 Sonnet and Opus both scored 100%, but Sonnet costs 3.5x less per call.
LivingAgents.ai: A Web-Based AI Agent Simulation Using Claude API
LivingAgents.ai is a web-based simulation where every agent is powered by the Claude API, performing actions like foraging, trading, crafting, attacking, reproducing, and dying permanently, with each action requiring a real LLM call.
Relay lets Claude Code sessions message each other without alt-tabbing
A plugin called Relay uses Claude Code's channels capability to let parallel sessions communicate directly, removing the need to manually copy-paste context between backend and frontend repos.