A very difficult way to earn 300

September: idea

When I was young and naive, it seemed to me that you could do and succeed in anything. However, with experience, it turned out that finding a good idea is still a task, especially if you plan to commercialize it somehow. The topic of choosing ideas and testing hypotheses deserves a series of articles or a book, for example, I recommend reading "The Mom's Test".

We were inspired by the success stories preached by Y-Combinator and decided that we would have a systematic approach to finding an idea, the rules of the game are as follows:

• meet 1-2 times a week for brainstorming sessions;

• for each "good idea" we check the product market fit using user interviews and market research;

• the idea must necessarily have the ability to quickly assemble an MVP

• launch on the western market.

We spent about a month in search mode. The main pool of ideas was related to AI, it seems this was mainly due to professional deformation, as I specialize in ML, and in general, it is a hot topic. In general, during this time we had:

• about 15-25 more or less adequate ideas

• 4 were interesting

• 3 of them we rejected at the research stage

• one we chose.

Of the interesting ideas that were discussed, we can note: an assistant for a teacher to answer students' emails, a tool for managing time spent with children, and a Photoshop with only text prompts. But we settled on the idea of AI photos for resumes. The product should work as follows:

• The user uploads about a dozen of their photos, which can be selfies, vacation photos, etc.

• We generate 40-60 photos for resumes, LinkedIn, and social networks. Of these, 3-10 are more or less normal, 1-2 the user likes and attaches to the resume.

We liked the following in this idea:

• a new niche, neural networks have only recently learned to do this;

• there is a demand in the market, people are already paying for photos in photo studios. I also looked through the subreddit r/photoshoprequest, and there were many such orders;

• there are several competitors, at that time they were all launched not so long ago, the only hype story was Lensa;

• the project is feasible.

October: implementation

First of all, we started to delve into the technical part (Dreambooth, FaceSwap/Ractor, deepfake, IP-Adapter/FaceID and other gadgets), hastily making a prototype on ourselves, making a test purchase from competitors.

From a technical point of view, the product consisted of 3 large parts:

• the generation of photos itself, ML on a server with GPU;

• landing page, dashboard, and payments

• backend and database that glued everything together;

• later a mobile application for iOS was added.

With the landing page and backend, everything is more or less clear: design in Figma, purchase of a domain name, client-server API, Flask. There were 2 interesting points:

• accepting payments on the site is quite troublesome if you do not have the opportunity to use Stripe;

• you can quickly make logos and graphics in SD/Midjourney + minimally refine in GIMP/Photoshop. Saves a lot of time.

All this was done in 2 weeks.

However, there were a lot of problems with the photo generation itself. In terms of generation quality, we lagged significantly behind competitors. Briefly what the problem was (if you are interested in how it works in detail - ask), there are 3 methods of how generation could work:

1. We take one or more user photos, understand the age, nationality, body type, etc. from them. We generate an image of a person with parameters similar to the user, and paste the face.

2. We take user photos (5-20 pieces), retrain the model on user photos, get a checkpoint that can generate new images with the user.

3. We take a user photo, calculate the face descriptor, generate an image of a person in such a way that the descriptor of the generated face matches the user's face.

The first approach did not work, the face was pasted well, but it was impossible to reproduce the body type and physique, the similarity was lost. The third approach had similar problems, plus it was also clear from the faces that it was not a real photo.

We managed to make only the 2nd option work, had to add a lot of hacks and post-filters, it took 4 hours of GPU time with 32 Gb of memory to retrain the model for one client. But it worked (!!!), and we managed to tweak the photorealism so that our solution outperformed competitors in 90% of cases (at the time of measurements)

November: marketing

We dedicated the whole of November to marketing, set ourselves a goal of 20 sales. Our marketing plan was as follows:

• through friends; if someone posted photos on Instagram → it led to 2-3 purchases through friends of friends;

• cold sales; in LinkedIn and Instagram, we wrote to 10-20 bloggers a day with an offer to try the product for free → free users were easily found, but we did not get any significant purchases;

• organic traffic; Instagram and TikTok shot videos and made content → did not get clients, got less than 10 subscribers;

• advertising on Instagram and Google, need to spend money → spent $150, got 2 clients on whom we earned $30;

• various clever marketing on Reddit → got 30 one-time clients;

• started making an iOS app with a focus on ASO [link].

As a result, in advertising, attracting a client cost 2.5 times more than we could earn on it, but we got some organic traffic and managed to get clients and feedback through Reddit, LinkedIn, Instagram, and friends.

As a result, in 3 months we made a net profit of $300 for two. This takes into account the costs of renting servers, advertising budgets, and purchasing photos from competitors.

Total

• Since the events described in this article, almost a year has passed, ProfilePhoto is in working condition, but not developing: the server bill per month is $60, it brings us $70.

• For me, it was a valuable experience to launch a pet project from start to finish, although it is a cliché to say, but "I learned a lot".

• The niche of AI avatars turned out to be too accessible, if in September we had about 10 competitors, then in November-December 2023 about 50.

• The product usage scenario is a one-time purchase. It is unclear how to retain the user. There is a network effect, but it was not possible to harness it.

• Despite the development of diffusion networks, there has been no qualitative leap in the quality and similarity of photo generation over the year.

• We were tried to be bought 😉, a guy wrote, offered $1000 for everything, we asked for $5000, agreed on $3000 and he disappeared.