Three startups arrive with radically different models, but the episode’s question narrows to one: when does a project stop being a personal struggle and become a business that can stand on its own?
The first question is not whether the idea works. It is whether it can become more than a meme. In front of the judges, the line between a serious project and a side project stays blurry, and each startup is measured by the same yardstick: is there a market, or just the founder’s energy? The episode keeps pushing on that point, and each outcome is different. One is read as a hidden media company, another as B2B software with defensible margins, and a third as an attempt to generate discovery and data in a space where uncertainty still rules.
The first startup is the kind that gets an identity check before a market test. Jaser makes it clear he did not come for traditional fundraising. He brought an app for finding the best toilet; in reality, he brought a question about what he is actually building. A product? A community? Or a meme that is slipping out of his hands? The question that keeps coming up in the conversation is whether it can survive without showing a face, or whether it only exists while it remains a creator project.
I have to decide whether I want to be an entrepreneur, or whether I’m just playing at being one.
There’s no proven business model, and there are plenty of ideas. But nothing has actually been implemented or taken all the way through.
4:03
The app is called Where is the toilet, and it promises to find the best nearby restroom. Jaser frames it as an answer to a small inconvenience, then quickly broadens the scope: accessible toilets for people with disabilities, changing tables for new parents, and profiles with reviews and technical details. He says the project has already reached 200,000 downloads and 29,000 reviews organically, but revenue is almost zero and the team is small. Not everyone is full time.
We’re basically building, to put it very simply, the Trip Advisor of toilets.
3:10
Michele Pagani reads the case more as a media company and community than as a software startup. For him, the real value lies in ongoing storytelling, in the people’s stories tied to the format, and even in merchandising or brand campaigns for companies like Scottex. In that frame, the app is a showcase, not the economic core.
What I see here is less a startup and potentially a media project and community.
9:24
Here the question is not AI’s promise, but its industrial strength. Liablix arrives with a very clear idea: reconstruct traffic accidents from photos and insurance data, and sell it into an existing market as vertical software for a field plagued by slowness, fraud, and manual work. The question remains: can it scale without turning into a small consulting shop wearing a SaaS costume?
This is a very large market, and the tools used today still don’t solve it.
26:08
We want to automate the analysis of these claims and deliver it at scale.
26:42
The startup runs on an AI and physical-simulation model. In theory, the software extracts data from photos, rebuilds the scene in 3D, compares damage, searches for similar cases, and generates automated analysis. But in practice, the judges quickly moved to a more grounded question: how much of this is truly scalable, and where does it still depend on human labor that the platform itself should be replacing?
Our software recreates dynamics in 3D. So you upload a photo and it rebuilds the scene.
32:27
Right now we don’t deliver that directly through the platform. What we have is a kind of technical investigation on demand.
33:45
Mira Search makes a simple, ambitious claim: the problem in the premium secondhand market is not lack of products, but lack of access to discovery. Sorge tries to turn that shortage into leverage: a search engine that makes used items easier to find than new ones, user data collection, and stronger bargaining power with marketplaces and sellers. The weakness appears quickly, though: it has to persuade consumers, boutiques, and platforms all at once, while the model depends on affiliate revenue and very high volume.
We’re using quite a lot of resources, but I’m not here to complain about how much. The secondhand market is different, because for people who want quality, quality isn’t inside big platforms like Vinted.
46:58
Mira is an aggregator for the secondhand world. The goal is to make secondhand search easier from the user’s point of view than Newwire.
48:05
The pitch quickly moves beyond search. Mira connects to niche marketplaces, vintage sellers, boutiques, and brands’ pre-owned divisions, while also generating data that does not currently exist because the platform owns no inventory of its own and functions in real time as a Discovery Layer. That is where the startup finds its real margin: not by selling directly, but by becoming the layer that reveals what is hidden in Google’s long tail.
We don’t have our own inventory. We aggregate existing online inventory. We’re a Discovery Layer, so our competitors are Google and OpenAI.
49:11
Data becomes interesting when it becomes personal. The real value is not just in what is searched, but in the demand we can understand, in the user conversations.
57:53
The most interesting moment in the presentation is the pivot away from a generic marketplace. Mira says it started with lending and swapping, but the real problem turned out to be discovery — especially for buyers looking for premium items, where quality matters and quantity alone is not enough. That led to a Shopify app too, aimed at connecting small sellers more easily and reducing dependence on specific crawlers.
In the end, the doubt running through the whole episode becomes explicit: having an idea is not enough. Being arguable is not enough. What matters is whether the startup can endure as a company. In the end, Liablix wins because it is seen as more credible than the other two, both in product terms and in entrepreneurial direction. Mira Search, meanwhile, is credited with a good vision but falls short of the decisive leap. The frame is the same: believing in secondhand is not enough if you cannot monetize value robustly.
I think the idea of making Second Hand as easy as First Hand is very strong. And if you stay true to that mission, you’re bound to get somewhere.
1:05:00
I also agree that the data side is not the hidden value. But as you say, they’ll probably find a way somewhere.
1:05:00
The final turning point comes when the last voice separates praise for the vision from the actual ranking. Mira Search is recognized for entrepreneurial skill, but the prize goes to the startup that felt most clearly readable as a company, not just as inspiration. That also fits the episode’s own standard: discipline matters more than sympathy for the project.
Mira Search clearly showed strong entrepreneurial skill and a clear vision for its product, but the startup that left the strongest impression on the judges in this third episode is Liablix.
1:06:07
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1:06:07
AI-assisted summary of ascensore's podcast, verified against the original transcript.