Design note: memory management for AI embeddings

[initial-d]

I wrote down the design framing that motivates me_fasttext beyond the short "memory-efficient FastText" description.

The central idea is to treat a large FastText-style subword model as a structured memory-layout problem:

1. preserve exact word and character n-gram identities with trie-backed ids;
2. train before any storage-sharing decision is made;
3. use structure and learned vector similarity to choose conservative row sharing;
4. rewrite retained rows and trie terminal values into a compact mmap serving artifact.

This makes the project a bridge between classical dynamic memory management, trie-backed lexical identity, and FastText's character n-gram statistics.

Canonical notes:

• Design essay: https://github.com/initial-d/me_fasttext/blob/main/docs/memory_management_for_ai_embeddings.md
• Chinese retrospective: https://github.com/initial-d/me_fasttext/blob/main/docs/technical_retrospective_zh.md
• Benchmark protocol: https://github.com/initial-d/me_fasttext/blob/main/docs/benchmark_protocol.md

Feedback is welcome, especially from people working on embedding storage, lexical retrieval, trie dictionaries, mmap serving, or memory-efficient NLP systems.