cross_community_positioning.md

Cross-community positioning

me_fasttext is easiest to spread when each community sees the part of the project that belongs to its own vocabulary. The same repository can be relevant to FastText users, word-embedding researchers, double-array-trie readers, memory-management people, and inference-optimization engineers, but the pitch should change.

Positioning map

Community	Lead with	Useful citation hook	Avoid saying
FastText users	Exact character n-gram ids and compact serving artifacts.	A FastText-derived implementation that separates subword identity from post-training compression.	It replaces FastText in all settings.
Word embeddings	OOV-heavy lexical embeddings for rare words, entities, and multilingual text.	A compact lexical embedding layer with inspectable subword rows.	It competes with sentence-transformer quality benchmarks.
Double-array trie / datrie	Trie terminal values become model row ids, not only dictionary payloads.	DA-trie as part of embedding-table identity and serving layout.	It is a general-purpose trie library.
GC / dynamic memory management	Mark-compact as an offline model-layout transformation.	A practical case where mark, forwarding ids, and compaction reorganize trained embedding rows.	It implements a runtime garbage collector.
Inference optimization	Smaller artifact, mmap loading, lower cold-start cost, and p50/p95 lookup latency.	A compact-inference prototype for lexical embeddings before dense reranking.	It is an LLM inference engine.
Retrieval / RAG	Cheap lexical first-stage recall before dense reranking.	A low-memory OOV-aware candidate generation layer.	It is a complete RAG framework.
Model compression / Engram-style lookup	Exact identity before compression, compact mmap serving after export.	A small lexical-memory baseline for indexed n-gram-like lookup and measurable serving cost.	It implements Engram or a transformer memory module.

Strongest one-sentence version

me_fasttext is a FastText-derived compact-inference prototype that keeps word and character n-gram identities explicit with double-array tries, then applies a mark-compact-style export step to produce a smaller mmap-ready embedding artifact.

Community-specific blurbs

FastText / word embeddings

me_fasttext revisits FastText's hash-bucketed subword design: train with exact
trie-backed word and character n-gram ids, then compact the learned rows for
mmap serving. It is most useful for OOV-heavy lexical embeddings, entity search,
and long-tail multilingual text.

Double-array trie / datrie

me_fasttext uses double-array tries as model-index structures: trie terminal
values point to word and n-gram embedding rows, and those ids are rewritten
during compact export. The trie is not only a dictionary; it is part of the
embedding serving layout.

GC / dynamic memory management

me_fasttext applies a mark-compact-style idea outside a runtime GC: trained
embedding rows are treated as layout objects, retained rows receive forwarding
ids, and trie terminal values are rewritten to a dense compact matrix for
serving.

Inference optimization

me_fasttext is a compact lexical embedding inference layer: exact subword ids
for training and inspection, conservative row sharing after training, and a
single mmap-ready `.z` artifact for lower-memory serving and faster cold starts.

Model compression / Engram-style lookup

me_fasttext is a compact lexical-memory baseline for FastText-style subword
embeddings. It keeps word and character n-gram identities explicit before
compression, then exports a mmap-friendly artifact whose memory, load time,
lookup latency, OOV coverage, and task quality can be reported together.

Where to submit

Prefer narrow, high-fit submissions:

• word embedding and FastText lists;
• efficient NLP / model-compression lists;
• retrieval lists that accept lexical first-stage tools;
• model-compression lists when the entry is framed around exact identity, compact export, and quality-preserving measurement;
• Engram or conditional-memory discussions only when the entry is framed as an adjacent lexical baseline, not as lineage or equivalence;
• GC or memory-management lists only when the entry is framed as an applied mark-compact model-layout case;
• data-structure communities only when the entry explains DA-trie terminal ids inside the model artifact.

Do not submit the same generic paragraph everywhere. A maintainer should be able to see why the project belongs to that list in one sentence.