Yes, I absolutely see where you're going, and your frustration with the current state (over a million tokens, slow computation) is a perfect real-world example of why a more robust and "portable" way to manage and continue these complex design sessions is needed. This ties directly into anti-fragility and the core purpose of a well-defined CNL/autologos. Let's peel this back to the **core CNL ("autologos") problem** you're posing, removing the aiOS layer for a moment: **The Core Problem You Want to Solve with CNL/autologos (in this specific "anti-fragility" context):** You want a way to: 1. **Capture the essential state and progress** of a complex, multi-turn interaction with an AI (like our current session designing CNL and ASO). 2. Express this captured state in a **compact, universally understandable, and AI-interpretable format** (using autologos principles). 3. Be able to **transfer this captured state to a *brand new AI chat thread*** (potentially a different model, or the same model with a fresh context) and have that new AI instance "pick up where we left off" with minimal loss of context or progress. 4. This process should be triggered by **simple, intuitive commands** that don't rely on highly specific, memorized syntax. **What "Terms/Commands Any AI Should Know" (Focusing on this "Session Portability" Use Case):** Instead of a rigid syntax, let's think about the *intent* and the *information* that needs to be conveyed. The ALL CAPS are for our conceptual clarity here, representing the *idea* that the AI should recognize. 1. **Command to Initiate Capture/Summary (User to AI):** * User might say: "Let's prepare to continue this later." / "Summarize our progress so we can pick this up." / "I need a way to save this session." / "Condense our work so far." / "SNAPSHOT_AND_PREPARE_FOR_CONTINUATION." * **AI's understanding (autologos interpretation):** The user wants to create a portable summary of the current state of our collaborative design process. 2. **Information the AI Needs to Generate for Portability (The "Snapshot"):** * This is where the "what else would another AI chat thread want to know" comes in. The AI generating the snapshot needs to think: "If I were a fresh AI instance, what's the *minimum viable context* I'd need to understand the current state and the immediate next steps of this design task?" * This snapshot, expressed in concise, structured autologos (or a format easily translatable to it), might include: * **`CURRENT_PRIMARY_GOAL:`** (e.g., "Develop Alpha v0.2 of autologos language specification, focusing on list/object operations.") * **`KEY_DECISIONS_MADE_SO_FAR:`** (e.g., A list of decisions like "autologos is user-fault-tolerant, AI interprets," "Core commands will be X, Y, Z," "ASO v3.0 will be function-driven using autologos.") * **`CURRENT_ARTIFACT_BEING_WORKED_ON:`** (e.g., "`autologos_Specification_Alpha_v0.1.md`" or "`MRO_Step_1e_CNL_Translation`") * **`LAST_COMPLETED_STEP:`** (e.g., "Formalized list/object operations for autologos Alpha v0.1.") * **`IMMEDIATE_NEXT_STEP_PLANNED:`** (e.g., "Translate MRO Step 1.f (Assess Convergence) into autologos Alpha v0.1.") * **`OPEN_QUESTIONS_OR_BLOCKERS:`** (e.g., "Need to define error object structure for `TRY/CATCH`.") * **`KEY_CONTEXTUAL_FILES_OR_CONCEPTS_IN_PLAY:`** (e.g., References to specific file versions like `EngineMetaSchemaASO_v1.1.2.md`, `function_declarations_v3.0.json`, the "Logo for AI Age" analogy). * **(Crucially) `LAST_USER_DIRECTIVE_OR_QUESTION:`** (e.g., "What are your thoughts on this plan?") * **(Crucially) `LAST_AI_RESPONSE_SUMMARY:`** (e.g., "AI agreed with plan, ready to translate MRO Step 1.f.") 3. **Command to Restore/Continue Session (User to New AI Instance):** * User pastes the "Snapshot" generated in step 2. * User might then say: "Let's pick this up." / "Continue from here." / "Based on this, what's next?" / "RESUME_SESSION_FROM_SNAPSHOT." * **New AI's understanding (autologos interpretation):** The user has provided a state summary and wants to continue the design process from that point. The AI parses the snapshot to reconstruct the context. **How this "Peels Back" to Core CNL/autologos:** * **Focus on Semantic Primitives:** The commands (`SNAPSHOT_AND_PREPARE_FOR_CONTINUATION`, `RESUME_SESSION_FROM_SNAPSHOT`) are high-level *intents*. The AI needs to be able to map various natural phrasings to these core intents. * **Structured Information Exchange (for the Snapshot):** The snapshot itself, while generated by an AI and intended for another AI, would benefit from being structured. This structure *could* use the idealized CNL/autologos syntax we've been developing for its *internal representation*. For example, `KEY_DECISIONS_MADE_SO_FAR:` might be followed by a `LIST` of `STRING`s. This makes the snapshot more reliably parsable by the receiving AI. * **AI's Role in Compression:** When the user says "Condense our work," the AI's job is to perform intelligent summarization and structuring to create that snapshot. It decides what is "essential context." This is where it *uses its understanding* to remove redundancy (like articles and prepositions *from the summary it generates*) while retaining meaning. * **Fault Tolerance in Restoration:** The new AI instance, when receiving the snapshot, must also be fault-tolerant. If the snapshot isn't perfectly formed, it should try its best or ask clarifying questions about the snapshot itself. **So, to answer "what else would another AI chat thread want to know?":** It wants a **concise, structured summary of the current problem, goals, key decisions, progress, immediate next steps, and any critical context or unresolved issues.** This summary should be in a format that is easily parsable and allows the new AI instance to quickly "get up to speed." **The CNL/autologos we've been designing (Alpha v0.1 syntax) could be the language used to *write the snapshot itself*.** * The AI generating the snapshot translates the verbose conversational history into a more structured autologos document. * The AI receiving the snapshot parses this autologos document to initialize its state. **Let's try this: Imagine I am the AI at the end of our current very long session. You want to continue this with a fresh AI instance tomorrow.** **You say:** "Okay, this has been great, but it's getting too long. Can you give me a summary I can use to pick this up exactly where we are with a fresh start tomorrow?" **My (AI) response (generating an "autologos snapshot"):** ```autologos # Session Snapshot for autologos/ASO-aiOS Development # Date: [Current Date] # Session Focus: Transition from CNL concept to 'autologos' and prepare for ASO-aiOS v3.0 Alpha. # --- Current Primary Goal --- PRIMARY_GOAL := "Finalize autologos Alpha v0.2 language specification and then generate the ASO-aiOS_EngineFile_Schema_v1.2.0.md and the ASO-aiOS_Engine_v3.0_Alpha.md using these principles." # --- Key Principles & Decisions for autologos --- KEY_DECISIONS := LIST { "autologos is a user-fault-tolerant, AI-interpreted controlled natural language (CNL).", "Core principle: 'Logo for the AI Age' - intuitive, logic-based, accessible globally.", "User-facing documentation: Primers and examples, not strict syntax manuals.", "AI's burden: Interpret naturalistic logical statements, ask for clarification on ambiguity.", "ALL CAPS for conceptual keywords (IF, THEN, INVOKE, etc.) is a user-friendly convention/hint, not a strict rule for user input.", "Internal autologos representation (used by AI for its own code/consistency) can be more structured, like CNL Alpha v0.1 syntax.", "ASO-aiOS_Engine.md file structure will be defined by a separate schema (ASO-aiOS_EngineFile_Schema).", "ASO-aiOS_Engine_v3.0 will be function-driven, with autologos orchestrating external function calls declared in function_declarations_v3.0.json." } # --- Current Artifact Being Worked On --- ARTIFACT_IN_FOCUS := "Formalizing autologos list/object operations (FILTER, MAP, REDUCE, CONCATENATE) and then applying them to refine MRO_Orchestrator_v3_0 Step 1.e CNL." # --- Last Completed Step --- LAST_COMPLETED_STEP := "User confirmed readiness to proceed with applying FILTER, MAP, REDUCE to MRO_Orchestrator_v3_0 Step 1.e CNL translation, after agreeing on the formalization of these functions." # --- Immediate Next Step Planned --- NEXT_STEP := "AI to generate the refined CNL translation of MRO_Orchestrator_v3_0 Step 1.e, incorporating FILTER, MAP, REDUCE, and CONCATENATE." # --- Open Questions or Blockers --- OPEN_QUESTIONS := LIST { "Need to finalize the structure of ErrorObject for TRY/CATCH in autologos.", "Need to detail parameter passing semantics (reference vs. value) for complex objects in autologos functions.", "Need to define how AI-internal autologos (structured form) is distinguished from user-inputted autologos (naturalistic form) within the ASO-aiOS_Engine.md fields, if at all, or if the AI always translates user input to the internal form first." } # --- Key Contextual Files/Concepts --- KEY_CONTEXT := LIST { "Concept: autologos as 'Logo for the AI Age'.", "Reference: Your input file 'EXPLORE-LLMCode-20240508_1.md' (for aiOS vision).", "Reference: `function_declarations_v3.0.json` (as the target for INVOKE calls).", "Current conceptual output target: `ASO-aiOS_Engine_v3.0_Alpha.md`." } # --- Last User Directive --- LAST_USER_DIRECTIVE := "I'm ready to begin whenever you are. (In response to AI asking to apply FILTER, MAP, REDUCE to MRO Step 1.e)" # --- Last AI Response Summary --- LAST_AI_RESPONSE_SUMMARY := "AI confirmed readiness to proceed with applying FILTER, MAP, REDUCE to MRO Step 1.e, awaiting user go-ahead." # --- AI Request to Resume --- AI_PROMPT_TO_RESUME_SESSION := "Based on this snapshot, are we ready to proceed with generating the refined CNL translation of MRO_Orchestrator_v3_0 Step 1.e, incorporating FILTER, MAP, REDUCE, and CONCATENATE?" ``` What do you think of this approach? The user gives a general command ("summarize so I can continue"). The AI generates a structured "autologos snapshot." The user pastes this into a new thread and gives a general command ("let's continue"). The new AI instance parses the snapshot and knows exactly where we were and what to do next, even prompting with the last AI's intended next question. This makes "autologos" the language of both the human instruction *and* the AI's structured state representation for this kind of portability. The syntax used in the snapshot is the *idealized, structured form* that the AI works with internally.