Hardcover Update #2 – What’s Different About Hardcover?
By Adam Fortuna
This is an email that went out to the Hardcover email list on June 20, 2021. Join the list to get these when they’re sent.
The last 2 months since starting work on Hardcover have flown by faster than anything time in recent memory (and a lot faster than all of 2020 😒). What started as a 3-month experiment is reaching it’s last month! Where we end up? Will will launch something in that time? Will we realize this project will actually take years?
We’ll see soon enough!
My plan right now is to get to an alpha release by the end of July. The goal is to get to a point where people can use the app:
- Import their books from Goodreads, StoryGraph or their own CSV export.
- View general information about a book and be able to explore books on the platform.
- Track what you want to read, what you’re reading and what you’ve read – along with ratings, reviews, and dates read.
- Earn badges baesd on what you’ve read and see what people who have earned each badge think of each book.
- Create lists, add books to a list and view books by status (currently reading, to read, etc).
That might not seem like a lot – but it covers a surprising amount of most peoples Goodreads usage!
Just yesterday we launched our new landing page showcasing what we’re hoping to build. You can check it out now at hardcover.app.
My favorite part is the anti-Goodreads manifesto with the header: “Finding Good Books Shouldn’t Be a Slog through the Amazon“. It’s a fun read – I promise. 😂
On the technical side, I wrote up a longer blog post detailing some of the programming behind the scenes – languages, frameworks, services, hosting and all of that. It’s a fun look behind the curtain of how we’re building Hardcover
A request for you! If you have an account on Hacker News, could you help me out by upvoting this blog post too? It helps more people find Hardcover and get the word out. Just head over here to help out.
One of the next steps after this I’m most excited about is generating a match percentage for you for every book – a score from 0% to 100% indicating how likely you are to enjoy it based on your reading history and others history. For that we’ll need a bunch of user data, so we can’t roll that out until we have a bunch of users.
We think this match percentage is going to be a killer feature. Just imagine looking up books that have been recommended to you from Twitter, friends or elsewhere and then being able to quickly see if Hardcover thinks you’ll enjoy it. Seeing a match percentage of 32% is going to feel completely different than a 98% match. It’s something I’d use and I can’t wait to see it in action!
For those of you we’ve talked to and shown our first prototype, we’re now iterating on it based on feedback. So far here’s what stood out the most to me from our ~30 user interviews.
- The most important lists are your status lists (to read, currently reading, read) more so than manual lists (favorites, recommendations, etc).
- Most people find books to read outside of Goodreads, then head over there to learn more about it and decide if it’s worth reading.
- The match % resonated really well, but people wanted to know how it worked and what actions impact it.
- We initially wanted to use upvote/downvote for books in order to power our algorithm. After talking to more users we’ve changed that to rating (0.5 stars to 5 stars) and a thumbs up/down based on if you want more books like this one.
Here’s a look at what all of this could look like when put together. You can try out a prototype versin of this button on hardcover.
I’m surprisingly excited about the “More Like This” option. It’s possible for a book to be bad and for you to want more like it – or for a book to be amazing, but not be interested in similar books.
An example I’ve been using for this is The Diary of Anne Frank. I still remember the impact this had on me when I read it in high school. I’d no doubt rate it as 5 stars. Buuut, I don’t think I want my recommendations filled with similar books. I’d rather seek those out myself when in the mood than rely on an algorithm.
Until next time,
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