ELI5-AI: Tiny, ad-free explanations of AI terms (because we all start somewhere)

Beginners
1

Hi everyone! :waving_hand:

I made a small side project called ELI5-AI—short for Explain Like I’m 5 (about AI).
It lives here on GitHub: onestardao/Explain-Like-Im-5-AI

It’s not a big framework or a fancy course. It’s just a lightweight, ad-free glossary that explains AI terms in plain language, with kid-friendly analogies and a quick note on why each concept matters.

No sponsorships, no tracking, no pitch—just something I wished I had when I started.

Why I built this

  • AI vocabulary grows fast. Newcomers often bounce off the jargon wall.
  • I like tiny learning units. One page = one idea, first in plain words, then a pinch of tech.
  • I want zero friction. No sign-ups, no ads, just reading.

What’s inside

The glossary is split into three layers. Think of it like three zoom levels:

  • Level 1 — Basics
    One-sentence definitions + kid-friendly analogy + why it matters
    (e.g., Transformer, Embedding, RLHF, Loss Functions, Benchmarks, Safety, Common errors)

  • Level 2 — Advanced (still ELI5)
    Same friendly tone, but with short Tech Notes you can actually use
    (e.g., RAG & chunking/ANN, PEFT/LoRA, Vector DBs, Inference vs Serving, KV Cache, Speculative Decoding, Flash Attention, Checkpointing)

  • Level 3 — Foresight (future concepts, one page)
    A compact overview of terms shaping tomorrow’s discussions
    (AGI, agentic LLMs, world models, mechanistic interpretability, superalignment, continual learning, multimodal reasoning, AI scientist, collective AI, emotional AI, neural editing, synthetic data, memory architectures)

All pages are short, skimmable, and bilingual (English + Traditional Chinese), so you can compare phrasing across languages if that helps.

How to use it

  1. Start at Level 1 when a term feels fuzzy.
  2. Hop to Level 2 for light tech notes and gotchas that matter in practice.
  3. Glance at Level 3 to see where the field might be heading—no hype, just context.
  4. Copy terms into your notes; everything’s MIT-licensed.
  5. Suggest a term via Issues if you spot a gap—beginners’ questions are especially welcome.

Who it’s for

  • People new to ML/AI who want a gentle path into the concepts.
  • Practitioners who need quick refreshers or a linkable reference for teammates.
  • Educators looking for analogies that land with non-experts.

What it is not

  • Not a course or certification.
  • Not comprehensive research notes.
  • Not monetized. No ads, sponsors, or affiliate links.

Just a small, tidy glossary you can read in a coffee break.

License & contributions

  • MIT License — use, share, remix.

  • PRs and Issues welcome. Keep entries short, kind, and practical.

  • If you add a term, please keep the ELI5 structure:

    • One sentence
    • Kid analogy
    • Why it matters
    • (Optional) Tech Notes :hammer_and_wrench:

What’s next

  • Add simple diagrams for tricky ideas (attention, vector search).
  • Expand multilingual support.
  • Collect community examples and common pitfalls per term.

If even one beginner finds AI a little less scary because of this, the project is a win.

:backhand_index_pointing_right: Check it out here: https://github.com/onestardao/Explain-Like-Im-5-AI

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