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--- |
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license: mit |
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language: |
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- en |
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task_categories: |
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- text-retrieval |
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- question-answering |
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- text-classification |
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tags: |
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- ledger |
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- event-log |
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- state-machine |
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- retrieval |
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- rag |
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- prompt-engineering |
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- agent |
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- workflow |
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- llm |
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- education |
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- evaluation |
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- jsonl |
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pretty_name: MindsEye Lab Memory Ledger |
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size_categories: |
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- 1K<n<10K |
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--- |
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# MindsEye Lab Memory Ledger |
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A queryable educational dataset for the MindsEye/MindScript ecosystem. |
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This dataset is a public ledger of architectural states, template cards, repository mappings, and constraint tests. It is designed to function as: |
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- **Learning content** — teaches MindsEye architecture through structured examples |
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- **Evaluation suite** — validates state machine constraints |
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- **Retrieval store** — searchable knowledge base for RAG + agent systems |
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- **Demo backend** — powers live portfolio queries using the HF datasets-server API |
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--- |
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## Dataset Structure |
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The dataset uses a **single-table design**: multiple logical tables are stored together, separated by a required `"table"` field. |
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| Logical Table | Purpose | |
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|---|---| |
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| `ledger_events` | Append-only event log of state transitions & interactions | |
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| `cards` | Template cards (CONCEPT, FLOW, SPEC, TEST, BUILD) | |
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| `repo_registry` | Repository → State mappings | |
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| `tests` | State constraint validation tests | |
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### Why single-table? |
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Because it makes the dataset easy to query using the Hugging Face datasets-server endpoints: |
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- `/filter` (SQL-like filtering) |
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- `/search` (full-text search) |
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- `/rows` (pagination) |
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--- |
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## Core Concepts |
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### 1) Append-only ledger |
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`ledger_events` is meant to model an immutable history. Nothing “updates” the past — new events extend the timeline. |
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### 2) State machine learning |
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States like `CORE_OS`, `LEDGER_CORE`, `DATA_SPLITTER`, `SQL_CORE`, `LAW_N`, etc. are used to anchor knowledge and tests. |
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### 3) Cards as teachable units |
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Cards are compact “knowledge objects” that can be retrieved by state, tag, or keywords. They can also serve as system prompts / templates. |
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### 4) Tests as constraint enforcement |
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Tests encode the “laws” of the system (determinism, append-only, reversibility, routing constraints). |
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--- |
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## Schema Overview |
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### `ledger_events` row example |
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```json |
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{ |
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"event_id": "evt_2026_01_10_000001", |
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"ts": "2026-01-10T01:12:33+02:00", |
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"turn": 1, |
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"actor": "user", |
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"event_type": "STATE_INIT", |
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"state": "CORE_OS", |
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"repo_context": ["minds-eye-core"], |
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"command": null, |
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"input_text": "Initialize MindsEye Lab", |
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"output_ref": null, |
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"tags": ["init", "core"], |
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"table": "ledger_events" |
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} |
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