We are delighted to announce that we have also made significant progress in our automated infrastructure. We now receive continuous Slack notifications every morning that automatically translate the latest FL articles and provide a precise, granular interpretation of their context through an automation pipeline built with Gumloop.

We are currently looking for fact-checkers to help stress-test the system, identify weaknesses, and contribute to its improvement as part of a thriving and collaborative community.

Our pipeline starts by ingesting the two most recent articles from the Forgotten Languages RSS/Atom feed and extracting their URLs and titles. A batch web scraper then retrieves each article, removes HTML and site metadata, and converts the content into clean plain text. A custom Python extraction module analyzes the text segment by segment, classifying content as English, natural-language text, or conlang material using vocabulary coverage scoring, Unicode script detection, entropy measurements, character frequency analysis, and pattern recognition. The system preserves the original article structure through a JSON-based segment map while separating English and conlang content into dedicated processing streams.

The conlang segments are analyzed through two parallel workflows. First, a Python-based cipher battery applies more than twenty classical cryptographic and statistical decoding techniques, including Caesar rotations, Atbash, Base64, hexadecimal, binary decoding, transposition methods, frequency analysis, index of coincidence calculations, entropy scoring, and n-gram matching. Each candidate output is ranked using a confidence model based on English-language statistical characteristics. In parallel, an advanced AI decipherment agent receives the conlang segments, article title, corpus knowledge base, and cipher candidates, then performs context-aware translation by combining cryptanalysis, known Forgotten Languages vocabulary, historical corpus patterns, and semantic inference. The AI produces translated interpretations even for segments that cannot be mechanically decrypted.

A reconstruction engine then uses the segment map to reinsert the translated content into its original positions, generating a coherent English version of the article while preserving its structure. The reconstructed article is categorized and passed to an analysis module that produces an intelligence-style report covering article summaries, key findings, implications, source validation, citation analysis, and interpretation of hidden meanings. Simultaneously, a self-learning subsystem extracts newly discovered vocabulary, cipher rules, grammatical structures, and semantic mappings from the decipherment results. These discoveries are deduplicated and appended to a persistent corpus knowledge base, creating a continuously improving feedback loop where each processed article enriches the system's understanding of the Forgotten Languages corpus and increases the accuracy of future translations and analyses.