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Developer uses Claude Haiku for AI-powered torrent poster matching

By PulseAugur Editorial · [1 sources] ·

A developer details a three-pass AI pipeline built using Anthropic's Claude Haiku to improve the matching of torrent folder names to movie and TV show titles on TMDB. The pipeline first uses regex, then employs Claude Haiku for title extraction and verification, and finally uses the model to select the correct title from a list of TMDB candidates. Iterative prompt refinement, guided by performance metrics on 290 real entries, significantly reduced errors in title extraction and verification, with a key insight being the handling of season-specific folder names. AI

IMPACT Demonstrates practical application of LLMs for data cleaning and matching in niche use cases, highlighting the importance of prompt engineering and iterative refinement.

RANK_REASON The cluster describes a specific application of an existing AI model (Claude Haiku) to solve a niche problem, rather than a new release or significant industry event.

Read on dev.to — Claude Code tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Developer uses Claude Haiku for AI-powered torrent poster matching

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The cluster describes a specific application of an existing AI model (Claude Haiku) to solve a niche problem, rather than a new release or significant industry event.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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product, other
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High
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131 days old
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COVERAGE [1]

  1. dev.to — Claude Code tag TIER_1 English(EN) · Odilon HUGONNOT ·

    AI + TMDB: 3 Passes to Match Torrent Posters — Prompt Iteration With Real Numbers

    <p><a href="https://www.web-developpeur.com/blog/sharebox-peer-programming-ia" rel="noopener noreferrer">ShareBox</a> displays shared folders as a Netflix-style grid with TMDB posters. The problem: folder names come from torrents. <code>Naruto.INTEGRALE.MULTI.VFF.1080p.BluRay.x26…

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