Two famous passages rewritten in the register of ASD-STE100 (Simplified Technical English, https://www.asd-ste100.org), the aerospace industry's controlled language for maintenance documentation.
These are exhibits for the paper Follow Your Strengths: Writing Papers with LLMs (https://github.com/timm/how2rite). The standard gives useful discipline (short sentences, active voice, one meaning
ezr2 — explainable multi-objective optimization in one file, zero dependencies, pure Python stdlib. Build a large pool of candidate rows, recursively split it by far-point projec
skape: a multi-objective optimizer that spends labels like they are expensive — because they are. Each level it labels a few rows, projects the whole pool onto a FastMap line (p
gistsite: one short file that turns a GitHub user's gists into a
static catalog. It keeps only the curated ones -- gists whose files
include a ,.md README h
xomo: one short file that runs COCOMO-II (effort), COQUALMO (defects) and Boehm's risk tables (schedule/cost risk) as a Monte-Carlo over four classic NASA/JPL case st
nuff: one tiny file of reusable Python tricks — attribute-dicts, typed CSV, pretty-print, seeded randomness, non-parametric stats, minimal column summaries, and row distances. Pure
Example text-mining datasets: four software-engineering
systematic-review "reading" corpora (Hall, Wahono, Radjenovic,
Kitchenham). Each ships two ways — NAME.csv (processed feature
table) and NAME_raw.csv (raw abstracts). Self-describing CSV
headers; data only, no code. Feeds the textmine demos in
ezr.
ezr — explainable multi-objective optimization. Two files, ~1100 lines, zero dependencies, pure Python stdlib. An experiment in "how low can you go?": active learning labels a few
KAH: one file, ~50 short Lua functions that kept reappearing across my other Lua projects: lists, strings, random, csv, stats (incl. effect-size tests + confusion matrix), objects, tests. No dependencies beyond Lua 5.3+. Every function: one line of comment, a few lines of code, 65 columns max.
Example classification datasets: 73 CSV files (anneal, audiology, COMPAS, diabetes, soybean, vote, ...) with self-describing headers — the klass column ends in '!', so no separate schema files are needed. Data only, no code.
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