Hacker News

If AI were built for kids, what would it look like?

Asking that question led us to creativity, and more specifically, the power of kids’ imaginations. We wanted to let kids combine the power of their ideas with AI tools but we needed to make sure we did it safely and in the right way.

Enter Stickerbox, a voice powered sticker printer. By combining AI image generation with thermal sticker printing, we instantly turn kids' wildest ideas into real stickers they can color, stick, and share.

What surprised us most is how the “AI” disappears behind the magic of the device. The moment that consistently amazes kids is when the printer finishes and they are holding their own idea as a real sticker. A ghost on a skateboard, a dragon doing its taxes, their dog as a superhero, anything they can dream of, they can hold in their hand. Their reactions are what pushed us to keep building, even though hardware can be really hard.

Along the way the scope of the project grew more than we expected: navigating supply chains, sourcing safe BPA/BPS free thermal paper, passing safety testing for a children’s product, and designing an interface simple enough that a five year old can walk up and just talk to it. We also spent a lot of time thinking about kids’ data and privacy so that parents would feel comfortable having this in their home.

Stickerbox is our attempt to make modern AI kid-safe, playful, and tangible. We’d love to hear what you think!

P.S. If you’re interested in buying one for yourself or as a gift, use code FREE3PACK to get an extra free pack of paper refills.

Of course I have a demo vid: https://www.youtube.com/watch?v=c_fq0TzlsXI

This will be my last post to HN about this. I always like to try a few titles to see if any hit.

https://graphite.com/blog/graphite-joins-cursor

https://archive.md/ShO4E

https://www.nbcnews.com/news/us-news/live-blog/brown-univers...

https://www.masslive.com/news/2025/12/were-catching-serial-k...

  So I wrote tinypdf: <400 lines of TypeScript, zero dependencies, 3.3KB minified+gzipped.

  What it does:
  - Text (Helvetica, colors, alignment)
  - Rectangles and lines
  - JPEG images
  - Multiple pages, custom sizes

  What it doesn't do:
  - Custom fonts, PNG/SVG, forms, encryption, HTML-to-PDF

  That's it. The 95% use case for invoices, receipts, reports, tickets, and labels.

  GitHub: https://github.com/Lulzx/tinypdf
  npm: npm install tinypdf

I built Misata because existing tools (Faker, Mimesis) are great for random rows but terrible for relational or temporal integrity. I needed to generate data for a dashboard where "Timesheets" must happen after "Project Start Date," and I wanted to define these rules via natural language.

How it works: LLM Layer: Uses Groq/Llama-3.3 to parse a "story" into a JSON schema constraint config.

Simulation Layer: Uses Vectorized NumPy (no loops) to generate data. It builds a DAG of tables to ensure parent rows exist before child rows (referential integrity).

Performance: Generates ~250k rows/sec on my M1 Air.

It’s early alpha. The "Graph Reverse Engineering" (describe a chart -> get data) is experimental but working for simple curves.

pip install misata

I’d love feedback on the simulator.py architecture—I’m currently keeping data in-memory (Pandas) which hits a ceiling at ~10M rows. Thinking of moving to DuckDB for out-of-core generation next. Thoughts?

https://lite3.io/design_and_limitations.html

See also Show HN: Lite³ – A JSON-Compatible Zero-Copy Serialization Format in 9.3 kB of C - https://news.ycombinator.com/item?id=45992832 (no comments, but a good writeup)