find-connections

name	find-connections
description	Find connections between a draft blog post or book summary (provided as a file) and the existing corpus of posts and book summaries, and flag concepts that already have a cheat sheet. Use when the user points at a file under blog/_posts/ or _books/ and asks to find related/previous posts and books, surface cross-link opportunities, or check which concepts are covered in the _docs cheat sheets.

Find Connections

Given an input file (a draft blog post or a book summary), discover how it relates to the rest of this blog and produce concrete cross-linking suggestions. Three outputs:

1. Related posts — earlier posts that share concepts/themes.
2. Related book summaries — book summaries that share concepts/themes.
3. Cheat-sheet concepts — concepts in the input that already have a cheat sheet under _docs/, so the author can link to or stay consistent with them.

Corpus locations

• Posts: /Users/kunigami/github/blog-draft-posts/blog/_posts/ (~280 files, YYYY-MM-DD-<slug>.md)
• Book summaries: /Users/kunigami/github/blog-draft-posts/_books/ (<vanity>.md)
• Cheat sheets: /Users/kunigami/github/blog-draft-posts/_docs/ (organized in topic subfolders: math/, cpp/, python/, …; each file is layout: doc with a title like "X Cheat Sheet")

Procedure

1. Read and characterize the input

Read the input file fully. Extract:

• Title and tags (from frontmatter). For a post, also read the date from the filename (YYYY-MM-DD) and the vanity.
• Key concepts — a list of the substantive terms: named theorems/algorithms, technical terms, mathematical objects, tools/libraries, people, and domain topics. Skip generic words. This list drives all searches, so be thorough (aim for ~10–25 terms, including synonyms/aliases, e.g. "eigenvalue" ↔ "eigenvector", "DAG" ↔ "directed acyclic graph").

Search for the concrete nouns, not the abstract topic. The author writes about specifics; the abstraction is usually your summary of his point, and it often appears nowhere in the corpus. So for every theme in the input, also list the proper nouns that theme would be expressed through — tool and product names, book titles, author surnames, library and function names — and grep those too.

Real example: for a post about relying on AI instead of textbooks, searching textbook, self-study, and reading habit found nothing, because the relevant posts say "ChatGPT" and "Ahlfors". The whole cluster (The Weierstrass ℘-Function, [Book] Complex Analysis, Functionals, Sobolev Spaces) was missed until grep -ri chatgpt surfaced it — one of those posts lists ChatGPT as its only reference.

Names worth adding to the concept list whenever the input touches AI, learning, or tooling: chatgpt, claude, codex, copilot, gpt-, llm, vibe.cod. For a math or CS post, add the surnames of the authors whose books he's working through (ahlfors, cummings, susskind, …). Reference lists (* [N] … at the end of posts) are a good place to harvest these.

2. Find related posts and book summaries

Search the corpus for the key concepts with local grep (or rg) over blog/_posts/ and _books/ — one search per term/alias. Also match on shared tags.

Search full post bodies, not just tags/titles — this is where the best connections come from. The strongest links are often posts whose tag/title gives no hint of the overlap; only a body-text search finds them. Real example: the Delaunay post's best match was "On Lifetime", tagged opinion, whose body happens to discuss triangulation and convex-hull algorithms — a tag/title-only search would have missed it entirely. So treat tags as a secondary signal and always grep the content.

Tooling note: do NOT use the search_files MCP tool or any Meta infrastructure here — this is a plain local git repo, not a Biggrep-onboarded one, so search_files errors out. Use grep -rl -i "<term>" blog/_posts _books to find candidate files, then read excerpts to confirm. A case- insensitive search that ORs a few aliases is efficient, e.g. grep -rl -iE "map-?reduce|mapreduce" blog/_posts _books.

Then confirm candidates — a keyword hit is not a connection. Read the relevant excerpt of each candidate and keep only those with a genuine conceptual/thematic relationship. Note why they connect (shared theorem, prerequisite concept, same tool, contrasting approach, continuation of a series, etc.).

Chronology: when the input is a post, prefer earlier posts ("previous" posts the new one can reference). Still surface relevant later posts, but label them as such. Book summaries have no date — include any that fit.

Rank by strength of connection (shared core concept > shared tag > incidental mention). Useful helpers (all local, no external infra):

• Titles of candidates: grep -h "^title:" blog/_posts/<file>.md
• Posts sharing a tag: grep -rl "tags:.*<tag>" blog/_posts

3. Flag cheat-sheet concepts

For each key concept, check whether _docs/ has a matching cheat sheet — match against cheat-sheet titles and section headings (##/###), not just filenames. List all titles with grep -rh "^title:" _docs, find a file with grep -rl -i "<term>" _docs, and inspect its headings with grep -E "^#{2,3} " <file>. Report every concept in the input that is also covered by a cheat sheet, with the cheat-sheet file and the specific section, so the author can cross-link or align terminology/notation. A topic-level tag match with no overlapping section is a weak flag — say so rather than overstating it.

4. Produce the report

Output three sections. For each related item include the connection reason and a ready-to-paste link in this blog's link style.

Link formats:

• Post blog/_posts/YYYY-MM-DD-<slug>.md → [Title]({{blog}}/YYYY/MM/DD/<slug>.html) (slash-separated date; e.g. 2026-06-01-velox-vectors.md → {{blog}}/2026/06/01/velox-vectors.html).
• Book _books/<vanity>.md → [Title]({{site.url}}/books/<vanity>.html).
• Cheat sheet _docs/<topic>/<name>.md → {{site.url}}/docs/<topic>/<name>.html (verify the exact URL against _config.yml / an existing link if linking).

Report shape:

## Related posts
- **<Title>** — <one-line why> · <link>

## Related book summaries
- **<Title>** — <one-line why> · <link>

## Cheat-sheet concepts to flag
- **<concept>** → `_docs/<topic>/<file>.md` (section "<heading>")

Keep connection reasons to one line. If a section has no hits, say so explicitly rather than padding with weak matches. Surface the strongest connections first; it's fine to cap each list at the ~10 most relevant and note how many were dropped.

Notes

• Don't link a post to itself, and don't propose a post that doesn't exist — every suggestion must correspond to a real file you found.
• Be conservative: a shared keyword in passing is not a connection. When unsure, read more of the candidate before including it.