Yes, I know that the unbundling of Reddit is an old hat. So I’ll skip the preamble and talk directly about an example and a concrete opportunity.
Let’s start with an example I came across five minutes ago.
r/ClubhouseInvites is a subreddit dedicated to the trading of invites to the now viral social audio platform Clubhouse. (I’m not bullish, in case you wondered.) The community already has 2.1k members and its creator was smart enough to create a dedicated website. He claims that he’s sold already more than 700+ invites at $30 a piece. Even crazier, just four days ago he was able to sell the website for $9,450 on Flippa. (h/t Stefan von Imhof.) While there are many lessons here, the guy who started the community clearly followed the steps in the Unbundling of Reddit Playbook in record speed and was able to make a nice profit.
So looking for exploding subreddits you can turn into a standalone platform is a great strategy. Here’s one opportunity I discovered this way.
The idea behind r/SurveyExchange is that you can post your survey for others to fill out and fill one of theirs in return. So like r/ClubhouseInvites it’s a marketplace and the subreddit is only a rather awkward workaround. If someone would create a dedicated platform, I’m sure it would find plenty of users in record time. After all, here’s what the subreddits growth chart looks like.
More Clubhouse ideas
Popular startup advice is to look for workaround. Here’s two:
Right now Elon Musk is talking on Clubhouse. But most people can’t get into the room since there is a 5000 people limit. So people have created “Overflow Rooms” where someone simply streams the audio from the room, while others stream on Youtube. These are very awkward workarounds as the sound quality is horrible and of course there’s no way to participate if you’re not in the room. While this is a somewhat singular event, I think more and more people will now see the potential in bringing Clubhouse content outside of the platform. Also I’m seeing some potential that rooms dedicated to discussions of what’s happening in other rooms (or on other platforms) become a regular thing.
A great example is that several people started to share real-time summaries on Twitter. Week in Clubhouse has been doing this quite successfully for quite a while now. Most people don’t have the time to spend hours on Clubhouse and also it’s easy to miss interesting events due to time-zone issues, poor discoverability or full rooms.
Blab was Clubhouse with video instead of audio. Firstly, I think that video really helps if you're interacting with strangers. So I'm not sure why Clubhouse would succeed where Blab failed. But more importantly, I first understood the appeal of Blab after coming across this recording on Twitter. Someone gives a lecture, people can call in to ask questions. That seems awesome! Is there any platform that does this currently? Most lectures I’ve seen use Zoom which is a rather poor experience for this kind of use case.
Enough Clubhouse talk, let’s move on to arguably the biggest opportunity that exists right now.
There’s precisely one thing I’m annoyed by every single day: Google’s search results.
In How management by metrics leads us astray I wrote:
Google’s search results are dominated by ads and many users now use workarounds to find what they’re looking for (“Best headphone reddit”).
As before, workarounds are a clear sign that there’s an opportunity.
Back in the old days, while Google claimed to use hundreds of ranking signals, everyone knew that there was just one thing that really counted: backlinks. And since you can make a lot of money by gaming Google’s algorithm, lots of people did just that. They built private blog networks and spammed the comment sections of every available CMS and forum software just to get a few additional backlinks.
It was an eternal battle and while spammy sites occasionally made it to the top of Google, the search results as a whole were still quite good. Google removed spammy sites through manual penalties and tweaked their algorithms to detect the latest black hat tactics.
Clearly from Google’s perspective this was an annoying game of cat-and-mouse. So they decided to solve the problem once and for all. Their solution: let’s introduce an incredibly strong bias towards established brands and “authority sites”.
Now the top search results are dominated by big sites like Wikipedia and Forbes.
While initially this improved the search results, established authority sites quickly recognized that it now was their turn to game the algorithm. Since Google now favors strong brands so heavily, they can publish whatever they want, and it will still rank at the top. So of course they started to churn out thousands of crappy articles so that they can dominate as many search terms as possible.
In almost every niche, Google search results are dominated by just a handful of big players while smaller players have no chance, no matter how good their content is. For example, whenever I’m searching for a machine learning topic, Google shows me the same kind of results on the first page:
Links to the official documentation of the package/software in question. (Rarely ever useful since most developers never seriously thought about the jobs to be done by documentation. But that’s a topic for another day.)
Some big Medium publication like Towards Data Science. (Sometimes useful but far from amazing. Shockingly, often the articles contain code screenshots.)
An article from Machine Learning Mastery. (Sometimes useful. He started with some good articles but then quickly realized that churning out as many articles as possible is a much better strategy and the quality of most articles is meh.)
What are the chances that the best possible content on every single machine learning topic is to be found in these results?
There is so much amazing stuff out there. It’s just that no one can find it.
This is exactly the problem I talked about right at the beginning. It does not just happen on social media platforms but on Google as well.
Now of course Google and Co. are aware of these issues. But the current state of things is simply favorable from their perspective. While the quality of the content on big sites (and big accounts) is far from amazing, it’s often okay and the chance that it contains viruses or other scams that could harm users is close to zero.
And unfortunately, it becomes a self-fulfilling prophecy. What’s the point of publishing amazing tutorials on your blog if no one is ever going to find them? Google plays a major role in the death of the indie web which made the internet such an amazing place in the good old days (~15 years ago).
Now I’m writing all of this just because I genuinely believe that the time has come to disrupt Google. Sure DuckDuckGo is growing rapidly, but their USP is privacy not better search results. I mean, I just searched for “duckduckgo growth” on DuckDuckGo and the result I was looking for was nowhere to be found on the first page.
And yes, I’m aware that this is an incredibly tough challenge. But “it always seems impossible until it's done“, right?
One idea to get started is what Daniel Gross proposes here. People are annoyed of all the fake reviews on big sites like Forbes and add faux-query modifiers like “reddit” to their search results to find genuine opinions on products.
So why not collect these typeahead logs, predict the correct operators for your query using standard machine learning algorithms, proxy Google’s results, and serve?
query("is anker charger") -> query("is anker charger (inurl:forum OR site:reddit.com OR ...))"
But of course, this would even further amplify the problem I outlined above. So an even better solution would be to start by focusing on just one specific niche and then figure out a way to bring the best possible content to the top.
For example, finding understandable explanation of scientific topics is still incredibly hard and as crazy as it may sound, most of the time your best bet is to look through traditional textbooks. Google always shows Wikipedia at the top even though understandable explanations are hardly ever found there. (Once more that’s a topic I’ll write more about in the future.)
So it would be amazing to find a way to classify explanations using terms like “beginner”, “expert”, “visual”, “rigorous”, “friendly” so that users can always find an explanation that matches their personal preferences and skill level.
I would start by using the amazing arXiv API to classify freely available papers and then built up from there towards the wild wild web.
But you could also start by focusing on product reviews since this is a niche where a lot of money is being made. Manually it’s often easy to distinguish between genuine reviews and fake reviews. So can you find a way to do it algorithmically? (Just for clarity, this is what I mean by fake reviews. They clearly never touched any of the products.)
All three examples are things I want to try myself at some point. But if someone reads this and gives it a try, that would be even better, and I’d love to hear about your experiments!
Surfacing Hidden Gems
The recommendation problem is not Google-specific but permeates almost all big platforms. (Notable exceptions are Hacker News and TikTok.)
For example, I find it incredibly frustrating to search for book recommendations. Often times, exactly as described in the previous section, I have to add a faux-query modifiers like “reddit” to find any useful discussion.
GoodReads calls itself "the world’s largest site for readers and book recommendations." However, the data provided is usually not very useful and the recommendations far from optimal. Most of the time, you'll only find dozens of list that tell you to read "Harry Potter" or some other book you already know about. Since it's primarily a popularity contest and most users have an entry-level taste, it's almost impossible to discover hidden gems or niche books on GoodReads.
So here’s an idea I had:
Use GoodReads bookshelves to match people with similar taste. Let’s say we find that Person A and Person B have almost exactly the same taste.
Then instead of recommending the most popular book Person A has on his bookshelf that Person B hasn’t, recommend the book from Person A’s shelf that almost no one has heard about.
Person B is already perfectly aware of all the popular books Person A has read. So there is no point in recommending it to him.
For example, if both are into entrepreneurship it makes little sense to recommend “Tools of Titans” to Person B since chances are high that he’s already aware of its existence.
I’m definitely into entrepreneurship and aware of “Tools of Titans” existence. But still, I’ll never read it no matter who recommends it to me. I made that decision a long time ago and see no reason to re-evaluate it.
Instead, we should recommend books with the highest probability that Person B hasn’t heard of it yet.
Each time someone I follow on Twitter recommends a book I’ve never heard of before, I immediately become curious. That’s the stuff I’m here for! In contrast, I have zero interest in yet another recommendation of Taleb’s Antifragile.
Since we’ve established previously that Person A and Person B have a similar taste, chances are high that he too will like the book even if it isn’t wildly popular.
This would be an amazing way to bring hidden gems to the surface.
To finish this already far too long post, as a case in point, here’s an amazing book I discovered recently that deserve more attention:
Fun While It Lasted by Bruce McNall.
It’s similar to but far more interesting than How to Get Rich by Felix Dennis or any of the Richard Branson biographies that get recommended over and over again.
👋 End Notes
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