º (masculine ordinal U+00BA) -> ° (degree U+00B0) in Q35, Q963, Q5359,
Q9014. DATA.md now records the better-database audit: the
Indian_Exams_PYQ repo's 223 shift PDFs are LFS-locked and image-only;
examgoal CDN returns 403; our examgoal-derived bank stays the best
structured source. Also explains the Hard-difficulty reality (user-vote
metadata, ~2-3 hard questions per shift is authentic) and confirms all
9,813 questions carry exam='jee-main' source metadata.
The chapter structure now matches the NTA 2026 Paper 1 syllabus exactly
(verified against the official PDF at cdnbbsr.s3waas.gov.in):
- Physics: 20 units (Units and Measurements, Kinematics, Laws of Motion,
... Electronic Devices, Experimental Skills)
- Chemistry: 21 units (Some Basic Concepts, Atomic Structure, ...
Principles Related to Practical Chemistry)
- Maths: 14 units (Sets Relations and Functions, Complex Numbers, ...
Trigonometry)
scripts/reorg_nta_units.py maps upstream subtopics by keyword; 73
stragglers placed manually. Removed the 24 hand-written demo questions
(Q1-Q24) from the live bank — trivial one-liners that diluted the
difficulty pool. Bank is now 9,813 pure PYQs (2002-2025, all
exam='jee-main' verified from source metadata).
Integer explanations end with the answer but phrasing varies enough
that regex extraction would mis-key questions. A wrong key is worse
than no question. NUM stays hand-added until NTA keys transcribed.
scripts/import_examgoal.py pulls the MIT-licensed examgoal-derived
14,973-question pickle and keeps the 9,813 fully-text MCQs with single
correct answers (2002-2025, all subjects). chapterGroup becomes our
chapter, chapter slug becomes our subtopic. Idempotent: skips stems
already present.
views.rs gains rich() — a read-side sanitizer matching the importer's
allowlist — and all six stem/option/solution render sites use it.
KaTeX auto-render (trust:false) added to head() for $$..$$ math.
db.rs gains nine CREATE INDEX statements; the chapter loader query
drops from ~5s to 6ms on the 9.8k-question bank. DATA.md rewritten
around the real pipeline with attribution.
scripts/expand_seed.py generates synthetic questions across the full JEE Main
syllabus taxonomy (15 chapters, 45 subtopics, correct MCQ/NUM split). Stems
are deliberately sparse so no-one mistakes them for NTA content.
DATA.md documents why there is no real scraper: NTA PDFs render questions as
images; pdftotext extracts only IDs. Every aggregator with structured data
built it through manual entry pipelines. The document lists realistic paths
to a real bank and the SQL schema for loading one.
