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Why I prefer indie softwares

The greatest consumer software tools that exist out there are built by hobbyists and indie makers.

I prefer to write my notes on Obsidian. For scheduling tweets, I use Zlappo and Typefully. For creating AI interior renders, I use interior.ai.

One thing which is common among all these examples is that they are all built by hobbyists. I would like to call them “indie softwares”. These are businesses that are profitable from the very beginning, instead of optimising for “shareholder value”.

Most of them are used by handful of nerds, and have not peaked on the popularity index of SaaS startups yet. They are obscure. You can see them getting mentioned somewhere on X, and the demand keeps coming in through positive word of mouth.

The reasons why I prefer indie softwares over unicorns are a plenty. In the Substack essay aptly as the Tyranny of the Marginal User, Ivan Vendrov talks about how for startups after reaching a definite scale, the product becomes satisfying for the new user, and gradually become terrible for the existing user.

Take the example of OKcupid which later on got acquired by Match, only to have a steady decline in the usage to the point that it became unusable!

A friend and I were recently lamenting the strange death of OKCupid. Seven years ago when I first tried online dating, the way it worked is that you wrote a long essay about yourself and what you were looking for. You answered hundreds of questions about your personality, your dreams, your desires for your partner, your hard nos. Then you saw who in your area was most compatible, with a “match score” between 0 and 100%. The match scores were eerily good. Pretty much every time I read the profile of someone with a 95% match score or higher, I fell a little bit in love. Every date I went on was fun; the chemistry wasn’t always there but I felt like we could at least be great friends.

I’m now quite skeptical of quantification of romance and the idea that similarity makes for good relationships. I was somewhat skeptical then, too. What I did not expect, what would have absolutely boggled young naive techno-optimist Ivan, was that 2016-era OKCupid was the best that online dating would ever get. That the tools that people use to find the most important relationship in their lives would get worse, and worse, and worse. OKCupid, like the other acquisitions of Match.com, is now just another Tinder clone - see face, swipe left, see face, swipe right. A digital nightclub. And I just don’t expect to meet my wife in a nightclub.

This isn’t just dating apps. Nearly all popular consumer software has been trending towards minimal user agency, infinitely scrolling feeds, and garbage content. Even that crown jewel of the Internet, Google Search itself, has decayed to the point of being unusable for complicated queries. Reddit and Craigslist remain incredibly useful and valuable precisely because their software remains frozen in time. Like old Victorian mansions in San Francisco they stand, shielded by a quirk of fate from the winds of capital, reminders of a more humane age.

But why does this phenomenon occur? Shouldn’t software get better over time? Why is it getting worse despite billions of dollars in R&D and multiple version updates?

The logic goes like this —

If a software already has a billion users, optimising for revenue means optimising for DAU (Daily Active Users). If you’re optimising for DAU, and if your software products charge zero or a flat per-user fee, in order to operate on a margin, you optimise the product NOT for the billion existing users, but for the billion-plus-first user. If the billion-plus-first user is incentivised to not stop using the app, then it’s a success.

Wouldn’t neglecting the user experience of the existing users cause a loss?

Not necessarily, as the milk has already been churned through the one-time user fee. And by the time the loyal users leave, everyone in the team is already promoted, so who cares? The only thing worth caring about is the attention of the new user.

Here’s what I’ve been able to piece together about the marginal user. Let’s call him Marl. The first thing you need to know about Marl is that he has the attention span of a goldfish on acid. Once Marl opens your app, you have about 1.3 seconds to catch his attention with a shiny image or triggering headline, otherwise he’ll swipe back to TikTok and never open your app again.

An A/B test on the DAU performance with an addition of a new feature might be heavily influenced by the choices, the “billion-plus-first” user takes. Stickiness takes a priority over Loyalty.

Although there are some exceptions such as reddit, craigslist etc. which have kept their “core” intact, these are very rare.

Optimising for the “average user” leads to average products.

Most of the VC-funded SaaS businesses have succumbed to optimising their product for the “average” user to keep up the hockey stick growth. Monetising low value users through ad-spends becomes a priority for them.

Optimising for the “extreme” user lead to high-value products (which might not be as profitable for the shareholders).

Indie softwares are opinionated and highly niche.

The makers have skin-in-the-game while building these indie softwares.

If there is a fault or a bug, I can directly contact the indie maker on X. As the indie makers have a shared risk when the indie software fails, they take swift action. Compare this to a “faceless” customer support AI agent to whom bugs are shared.

The trust is more when you know the creator who has made it. It’s not just skin, there is soul in the game.

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