February 13, 2026
HN goes bilingual, comments go ballistic
Show HN: Data Engineering Book – An open source, community-driven guide
Bilingual AI data playbook tops HN; language spat and recruiter cameo ensue
TLDR: A free, bilingual guide on preparing AI training data shot to the front page, earning praise for accessibility while sparking debate over a Chinese post topping HN. One recruiter plugged “petabyte” jobs and a minor image-language nit surfaced, turning the launch into a mini culture moment about access and inclusivity.
An open‑source, community‑built book on wrangling data for AI hit Hacker News—and the comments stole the spotlight. The guide, in Chinese and English, walks through cleaning messy web text, aligning images/audio/video, and building real projects. Read it free here and see the English README here. In simple terms: it shows how to prep the “fuel” (data) that powers large language models, and explains RAG—retrieval‑augmented generation, a search‑plus‑chat trick.
Community mood? Applause, side‑eye, and jokes. A hero posted the English link within minutes, earning thanks from non‑Chinese readers. Then the spark: “How is possible a Chinese publication gets to the top in HN?” The thread split between global‑first “HN is international” energy and mild gatekeeping. A recruiter parachuted in to pitch “10–100s of petabyte processing” jobs, drawing the classic “not now, bro” chorus. Another user nitpicked that chapter figures are in English while a README image isn’t. Verdict: people love that it’s free, bilingual, and practical; the drama is pure HN—translation wins, culture clash, and an opportunistic job plug.
Key Points
- •Open-source, bilingual (EN/ZH) book provides a comprehensive guide to LLM and multimodal data engineering across the full lifecycle (pretraining, fine-tuning, RLHF, RAG).
- •The book comprises six parts, 13 chapters, and five end-to-end projects with runnable code and architecture designs.
- •Topics include data acquisition/cleaning from Common Crawl, tokenization, multimodal alignment, synthetic data generation, scaling laws, and data quality evaluation.
- •A modern tool stack is specified: Ray Data, Apache Spark, Parquet, WebDataset, Trafilatura, KenLM, MinHash LSH, CLIP, ColPali, img2dataset, DVC, LakeFS.
- •Setup and contribution details are provided (Python 3.8+, MkDocs-based site, MIT license, GitHub repository with CI/CD and bilingual content).