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Every year, we ask thousands of readers (and authors) to share their 3 favorite reads of the year.

Now you can enter a book/author you love and see what books readers loved who also loved that book/author.

Try it here: https://shepherd.com/bboy/2025

This goes wide and doesn't try to limit itself to the genre, so you get some interesting results.

What do you think?

Background:

I want better recommendations based on my reading history. I'm incredibly frustrated with what is out there.

This system is based on 5,000 readers voting on their 3 favorite reads from 2023 to 2025. So, this covers ~15,000 books and is a high-quality vote. We wanted to keep the dataset small for now while we play with approaches.

We are building a full Book DNA app that pulls in your Goodreads history and delivers deeply personalized book recommendations based on people who like similar books (a significant challenge).

You can sign up to beta test it here if you want to help me with that:

https://docs.google.com/forms/d/1VOm8XOMU0ygMSTSKi9F0nExnGwo...

The first beta is coming out in late January, but it's pretty basic to start.

Past Show HNs as we've built Shepherd:

https://news.ycombinator.com/item?id=40084193

https://news.ycombinator.com/item?id=38600246

https://news.ycombinator.com/item?id=26871660

Thanks, looking forward to your comments :)

Ben

Z80-μLM is a character-level language model with 2-bit quantized weights ({-2,-1,0,+1}) that runs on a Z80 with 64KB RAM. The entire thing: inference, weights, chat UI, it all fits in a 40KB .COM file that you can run in a CP/M emulator and hopefully even real hardware!

It won't write your emails, but it can be trained to play a stripped down version of 20 Questions, and is sometimes able to maintain the illusion of having simple but terse conversations with a distinct personality.

--

The extreme constraints nerd-sniped me and forced interesting trade-offs: trigram hashing (typo-tolerant, loses word order), 16-bit integer math, and some careful massaging of the training data meant I could keep the examples 'interesting'.

The key was quantization-aware training that accurately models the inference code limitations. The training loop runs both float and integer-quantized forward passes in parallel, scoring the model on how well its knowledge survives quantization. The weights are progressively pushed toward the 2-bit grid using straight-through estimators, with overflow penalties matching the Z80's 16-bit accumulator limits. By the end of training, the model has already adapted to its constraints, so no post-hoc quantization collapse.

Eventually I ended up spending a few dollars on Claude API to generate 20 questions data (see examples/guess/GUESS.COM), I hope Anthropic won't send me a C&D for distilling their model against the ToS ;P

But anyway, happy code-golf season everybody :)

Sadly, it currently does not have its own index but rather uses the Brave search API. Once I'm in a financial position that it's possible, I would absolutely love to build a completely new index from the ground up which is open source, as well as an open source ranking and search algorithm, to back it.

I posted on Reddit and got an amazing amount of feedback which I implemented a number of feature requests, so I would really like your ideas, critiques, and bug reports as well. Thank you and sorry for the long post!

[1] https://github.com/UnmappedStack/nilch

https://en.wikipedia.org/wiki/Mouse_(programming_language)

https://archive.org/details/byte-magazine-1979-07/page/198/m...

https://www.amazon.com/MOUSE-Language-Microcomputers-Peter-G...

The Mouse Programming Language on CP/M - https://news.ycombinator.com/item?id=41400380 - Aug 2024 (14 comments)