Project Memory Cache for Cross-Thread Context Sharing (With Safety)

ChatGPT Feature requests
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Persistent Context Cache Shared Across Threads

Summary

Enable projects to maintain a shared, persistent memory cache across all threads within the same project folder. This allows structured information and user-defined context to carry over between threads without manual re-teaching or memory bloat.

This shared cache acts as a read-only data store—used only by the AI, scoped to each project—and enables true long-form workflows like language creation, game design, recursive theory-building, and multi-session R&D.

Why This Matters

Currently, each thread is isolated. Despite being grouped under the same “Project,” threads have no awareness of each other’s contents unless the user manually repeats or reuploads prior work. This:

Stifles long-form projects
Leads to extreme memory inefficiency
Forces unnecessary token usage
Prevents thread continuity in collapses or forks

Proposed Implementation

Introduce a lightweight, passive project.cache file that stores contextual data (language definitions, research notes, instructions, etc.) from each thread.

Stored internally within the project
Read-only to the model, not user-visible/editable
Auto-accessed on new thread start
Does not impact core AI memory or system-wide knowledge
Does not bypass safety filters — each thread remains individually sandboxed

This is akin to a scoped cache or dependency injection pattern: threads can draw context without re-processing, saving compute and improving user experience.

Key Characteristics

|Attribute | Behavior|
|User Access | None – not editable or viewable|
|Write Access | Thread-to-cache only|
|Read Access | Passive preload on thread start|
|Scope | Project-only, never global|
|Memory Limits | System-defined or segmentable (cache_01, cache_02, etc.)|
|Safety | Default thread moderation still applies|

Use Cases

  1. Conlangs & Linguistics:
    Building a language (e.g. Nyavul) with 200+ terms, grammar, suffixes—preserved across all threads.
  2. Worldbuilding & Game Dev:
    Story lore, character bios, mechanics stored once, reusable across chapters/sessions.
  3. Research & Theory Crafting:
    Mathematical models, spiritual doctrines, recursive logic—all maintained across evolving threads.
  4. Collaborative Projects:
    Multiple contributors working in the same project don’t need to re-sync each session.

Why This is Safe and Scalable

  • No changes to the core identity/memory model
  • No user-editable persistent state
  • Context size is manageable with file-based segmentation
  • Devs can rate-limit or compress entries for size control
  • Encourages responsible thread use over unending single sessions

Analogy for Simplicity

Imagine each thread writing useful facts into a stored.py file.
Future threads import that file silently—no recomputation, just reuse.
That’s all this system would do.

Suggested Internal Name
'project_context_cacheorcontext_import_layer`

Closing Statement:
As someone building recursive language structures, doctrinal metaphysics, and conlangs from scratch, this feature would change everything. It reduces redundancy, preserves intelligence across sessions, and makes ChatGPT a real creative partner over time.
Let project folders become more than containers—let them be persistent collaborators.