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Show HN: Yila – A Codex for Scientific Research

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A unified AI workbench that bills itself as a "codex for scientific research," Yila automates literature discovery, reading, synthesis, analysis and presentation so researchers spend less time on formatting and more on insight. It runs multi-database literature searches, triages papers, produces structured deep reads and evidence cards, and can synthesize 50+ full papers into a citation-backed literature review. Features include chat-with-PDF (responses cite page numbers), bilingual line-by-line reading, a citation generator covering 2,800+ styles, and curated task templates used for lab meetings, thesis defenses, conference talks and other common research workflows. Institutions such as Yale, Oxford and Imperial College are listed among users, and outputs are described as verified and source-traceable.

Beyond reading, Yila provides a data-analysis workbench and design assistant: uploading a CSV/XLSX triggers profiling, a recommended analysis path, statistical tests, polished charts and reusable code. It can generate journal-ready scientific figures from a single natural-language prompt, auto-format manuscripts to journal or thesis templates, and produce editable PPTX slides and conference posters. Additional tools include a publication figure package, manuscript section builder and typesetting checker. The platform emphasizes provenance ("every claim traced to a source"), privacy by default, and turnkey agents that execute end-to-end research tasks.

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