Ask HN: How are you acquiring your first hundred users?

I have a course on building AI solutions in business (based on success stories from companies in Europe/USA). Sold ~400 seats so far, mostly through my community and word of mouth. No external ads or cold outreach.

The process was classical. Over two years I created a community to sharing cases and insights from building LLM-driven systems. We focused on creating good non-toxic and collaborative atmosphere. No ads or SEO to grow it, standing out by sharing real-world cases and helping others.

Thanks to the community, got 100 customers within the beta-testing period. Then 300 more came over the last 4 months, after opening the sales.

Are you able to share. I would love to see real world success stories of LLM use cases and integrations, beyond the common ones you see often (code gen, story gen, automated summaries, etc)

why no ads though? don't they give you reach and discovery to people? for so as long your profits are higher than ads cost, you are profitable, so why not?

Of course.

Most of the AI cases (that turn out to be an actual success) focus around a few repeatable patterns and a limited use of "AI". Here are a few interesting ones:

(1) Data extraction. E.g. extracting specs of electronic components from data-sheets (it was applied to address a USA market with 300M per year size). Or parsing back Purchase Order specs from PDFs in fragmented and under-digitized EU construction market. Just a modern VLM and a couple of prompts under the hood.

(2) French company saved up to 10k EUR per month on translators for their niche content (they do a lot of organic content, translating it to 5 major languages). Switched from human translators to LLM-driven translation process (like DeepL but understanding the nuances of their business thanks to the domain vocabulary they through in the context). Just one prompt under the hood.

(3) Lead Generation for the manufacturing equipment - scanning a stream of newly registered companies in EU and automatically identifying companies that would actually be interested in hearing more about specific types of equipment. Just a pipeline with ~3-4 prompts and a web search under the hood.

(4) Finding compliance gaps in the internal documents for the EU fintech (DORA/Safeguarding/Outsourcing etc). This one is a bit tricky, but still boils down to careful document parsing with subsequent graph traversal and reasoning.

NB: There also are tons of chatbots, customer support automation or generic enterprise RAG systems. But I don't work much with such kinds of projects, since they have higher risks and lower RoI.

There are way too many ads around "AI". Everybody else does that, frequently overwhelming people with too many promises of quick wins.

I prefer to distinguish from this hype and reach people through other channels - good content, word of mouth and interesting collaborative events (like our last Enterprise RAG Challenge). This might lead to slower sales in the short term, but I think the long-term value to the brand is worth it.

EDIT: fixed typo

yeah, this is much slower growth... danger must be it is so slow it must be non-existent. but I am with you on same boat, there are too many AI ads for crappy apps

but I guess it speaks to flood of crap-ware, and flood bad content in social networks

^not meaning about you, of course (those folks must not be even on HN)

Ok now we move goal posts how to build first 100 community members

When I was starting my community, I joined other similar communities and tried to be helpful there. No ads or links, just answering questions and supporting. People that were genuinely interested to learn more about the topic - opened my profile and followed the links there.

This and interesting content was enough to grow community organically to 14k subscribers over 2 years.

Another approach to speed up the growth - organise some fun event that benefits the entire community, highlights and showcases the participants.

E.g. when I organised last Enterprise RAG challenge, we got 350 submissions from the teams around the world. Plus IBM joined as a sponsor. People were mostly participating not for the prises, but because of the approachable challenge and ability to push state of the art. Plus some were hired away because of the good leaderboard scores.

Article of the winner (just google "Ilya Rice: How I Won the Enterprise RAG Challenge") is considered by some companies as one of the best resources on building document-based AI systems. And the entire community sees it as the result of their work together - further reinforcing the spirit of the collaboration.

People tend to share and spread fruits of their labor and love.

Building something that people truly need - might not lead to huge sales right away, but I believe this to be a good long-term strategy. Sprint vs marathon.

Just keep on pushing on it, and it will eventually work out.

Building something that people do not need might not lead to sales right away either.

Maybe it will work out, at low growth rate... in 180 years, when it does not matter anyways. Extremely low grow rates are functionally indistinguishable from death.

And even successful great products will not be used by anybody, ... if nobody even knows about it.

Think of Facebook or Apple hiding somewhere in corner vs screaming about themselves in Times Square billboards and streets storefronts in rural India.

Marketing, Distribution, Discovery is important for to-be great products too, just as it is important for crap-ware. (unfortunately later makes bad name and bad look for the whole industry).

That last point (compliance gaps in fintech) sounds fascinating. Is there a place that I could read more about this?

as Andrew Grove said "if you are walking through the desert of death, keep walking"

anyways, hope it works out well for you friend. tough for everyone! keep up all the hard work!

Compliance gaps / legal analysis is a pretty common theme in my community (meaning - it was mentioned 3-4 times by different teams). Here is how the approach usually looks like:

0. (the most painful step) Carefully parse all relevant documents into a structural representation that could be walked like a graph.

1. Extract relevant regulatory requirements using ontology-based classification and hybrid searches.

2. Break regulatory requirements into actionable analytical steps (turning a requirement into checklist/mini-pipeline)

3. Dynamically fetch and filter relevant company documents for each analytical step.

4. Analyze documents to generate intermediate compliance conclusions.

5. Iteratively validate and adjust analysis approach as needed.

6. Summarize findings clearly, embedding key references and preserving detailed reasoning separately.

7. Perform gap analysis, prioritizing compliance issues by urgency.

Great. Thank you for taking the time to do that.

Great answer, more people should understand there is no easy hack and overnight success is mostly preceded by years of consistent work every day.