6,234 karma · joined July 2, 2008
You can reach me at:
- matthew.h.mazur@gmail.com - http://mattmazur.com - Twitter: @mhmazur
The submissions are then ranked the exact same way HackerNews articles are ranked on the homepage, following this algorithm: https://medium.com/hacking-and-gonzo/how-hacker-news-ranking... - in short, it's based on how many votes the prompt has received and how long since it was posted.
Here's the launch thread, posted several minutes before you asked this: https://news.ycombinator.com/item?id=33923907
You'll find a lot of great examples on the site (and please share more as you find them).
Love the suggestions for tweaking this behavior - added them to my todo list. Thank you for the feedback!
[1] http://mattmazur.com/2015/03/17/a-step-by-step-backpropagati...
It also becomes easier the more you do it. I've been doing side projects for many years.
1. Affiliate sales: when you click on a domain and register it through GoDaddy, Namecheap, etc, I get a % of the purchase price.
2. Premium accounts: Lean Domain Search only showed the top 150 results for free. You could pay $79 once for two months of access or $199/year for access to all 5,000 results.
Each was bringing in around $1,000-$1,500/month with the premium accounts growing quickly.
After the acquisition, we made the site free but left the affiliate links in place. The site has grown a lot since then [1] but the affiliate revenue still brings in the same $1,000 - $1,500/month because GoDaddy (the main driver of affiliate revenue) made some changes to its affiliate program that led to lower payouts.
[1] http://mattmazur.com/2015/07/03/lean-domain-search-at-3%C2%B...
Here's the original HackerNews launch post from 4 years back for context: https://news.ycombinator.com/item?id=3470977.
I'm happy to answer any questions about Lean Domain Search, the deployment, etc.
My tentative plan is to start with Coursera's Neural Network course and implement as many things as possible (probably in Python) along the way. By the end of 2015, I'd like to build a deep learning application from scratch that does something interesting.
While online resources will likely be my primary source of information, I'd love to have a study partner (or partners) to discuss concepts and bounce ideas off of. If OP or anyone else is interested, drop me a note at matthew.h.mazur@gmail.com and we'll figure out the best way to do that.
As a side note, I built a few a-life/AI visualizations in JavaScript over the past year, including a simple neural network which might interest anyone reading this: http://www.emergentmind.com/.
http://www.amazon.com/Artificial-Life-Frontier-Computers-Bio...
It's slightly dated, but I thought it was a really good introduction.
A few ideas:
- If you're willing to consider limiting the TLDs to just .coms, you can then use the .com zone file to check the availability of domains much much faster than methods. Using this technique Lean Domain Search is able to check the availability of 5,000 domains in about a second. I wrote about how to get access to the VeriSign zone file here [2]. Parsing it so your app can access it in real time is tricky, but I can go into more detail over email if you want to explore that route.
- One thing to think about is whether figuring out the search volume is that important for your users. If it's a challenge getting that data from SEMrush or if it's slow or if it just clutters up the interface, consider dropping it and focusing on the quality of the suggestions. More information is not necessarily better for users (it depends what problem you are solving and for whom).
- If you want to a quick list of prefixes and suffixes to pair the user's search term with, I previously analyzed all registered .com's to figure out what the most common ones are. That list is available here if you're interested [3].
- I wound recommend identifying a specific pain point that a specific group of users have that isn't addressed well by existing tools and focus all of your efforts on solving that. The more focused you make your app, this better.
Props on getting users to tweet about the app to show more suggestions. I used to do that with Lean Domain Search and it works really, really well.
I'm happy to chat over email if you'd like. See my profile for contact details.
Best of luck with this -- congrats for launching!
[1] http://www.leandomainsearch.com
[2] http://www.leandomainsearch.com/blog/16-how-to-get-access-to...
[3] http://www.leandomainsearch.com/top-domain-name-prefixes-and...
The last project (The Evolution of Color) has similarities to the color visualizations described in this post: it uses genetic algorithms to evolve a population of colors towards a fittest color as determined by the closeness of the bits that comprise the colors.
I have a few other projects planned so if you're into this stuff you're welcome to sign up for the mailing list to get notified when they are launched:
http://www.emergentmind.com/newsletter
I'm always open to ideas for new projects so if you have anything you'd like to see implemented or want to work on something together feel free to shoot me a note.
When the biomorph gets too big for its canvas, I automatically reduce its branching depth until it's not too large. If you keep clicking on children that make it evolve towards larger entities, eventually it will shrink and look something like the biomorph in your screenshot.
Someone suggested that rather than automatically reduce its size, I should just make them unclickable (ie, you can only choose a child that fits within the canvas).
What do you think? Any other ideas?
Edit: I just updated the implementation so that instead of automatically reducing the child biomorph's size if it's too large, it simply becomes unclickable (you'll also get an notice if you try to click it). Should be a bit better -- thanks for the nudge!
I have other JavaScript-based emergence and artificial life projects planned so if you liked these, you should sign up for the mailing list to get notified when new projects are released: http://www.emergentmind.com/newsletter
If anyone reading this finds these topics fascinating as well I'm always down for a chat: matthew.h.mazur@gmail.com or @mhmazur - cheers!