AI + TMDB: 3 Passes to Match Torrent Posters — Prompt Iteration With Real Numbers
The article discusses the development of a three-pass AI pipeline to match torrent folder names with movie titles from TMDB. The pipeline uses regex for initial extraction, followed by AI for verification and candidate selection. Improvements were made based on real data, significantly reducing false positives and negatives in title matching.
- ▪The initial regex and TMDB search successfully matched titles 80% of the time.
- ▪The AI pipeline consists of three passes: extraction, verification, and candidate selection.
- ▪Improvements were made by analyzing real data, which helped reduce false skips and negatives.
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| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/ohugonnot/ai-tmdb-3-passes-to-match-torrent-posters-prompt-iteration-with-real-numbers-bl7 |
| Publication time | Sat, 30 May 2026 09:00:04 +0000 |
| Retrieval time | 2026-05-30T09:12:09.024Z |
| Last seen | 2026-05-30T09:12:09.024Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | KlcG93s_sght |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3833552) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Odilon HUGONNOT Posted on May 30 • Originally published at web-developpeur.com AI + TMDB: 3 Passes to Match Torrent Posters — Prompt Iteration With Real Numbers #ai #promptengineering #claudecode #tmdb ShareBox displays shared folders as a Netflix-style grid with TMDB posters. The problem: folder names come from torrents. Naruto.INTEGRALE.MULTI.VFF.1080p.BluRay.x264-AMB3R needs to match "Naruto" on TMDB — not "Naruto Shippuden", not "Naruto the Movie".
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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