Building without distribution is just a hobby.
You can build the best offline AI system in the world. If nobody knows you solved the problem, you do not get paid, you do not get hired, and you do not get clients.
This is the gap between AI engineers who have GitHub repos and AI engineers who have revenue. The code is often comparable. The distribution is not.
Here is the exact process I have watched work across multiple products in the cohort: build in public, find the right ten people, convert three of them.
The distinction that changes everything
An artifact is something that works. A product is something people pay for because it solves a problem they have today.
Every paying customer decision follows this logic: the person buying has a specific pain, they tried other things that did not work, and your product reduces that pain enough to justify the price. Your job before you sell anything is to find the person who already has the pain.
You do not create the pain. You find the person who already feels it.
Three questions before you build the landing page
Who is the buyer (not just the user)?
The buyer is not always the same as the user. For a compliance AI tool, the user might be a paralegal. The buyer might be the General Counsel. Your sales pitch, your pricing, and where you look for customers depend on understanding this difference.
For every product, name: the user (who interacts with it daily), the buyer (who approves the payment), and the trigger (the specific event that makes them decide to buy now rather than later).
What is the alternative?
If someone does not use your product, what do they do today? How much does that cost in time, money, or risk?
This is the number that makes pricing feel obvious rather than arbitrary. If your product saves a lawyer 2 hours per document and lawyers bill at $500 per hour, then $49 per month for unlimited document analysis is not an expense conversation, it is a math conversation.
What is the paid version?
The free version exists. Now define exactly what the paid version adds, and use a specific limit on the free tier that creates a natural reason to upgrade.
"Free: 3 documents per day. Professional at $19 per month: unlimited documents, audit trail, team sharing."
The limit is not punitive. It is a demonstration. The person who hits the limit has already proven the product is valuable to them. That is the best time to have a pricing conversation.
Finding your first 10 free users (this is your actual first job)
Not paying customers yet. Free users who will give you honest feedback.
For each product category, there are direct channels:
Privacy-focused AI tools (documents, sensitive data): Post in healthcare and legal professional Facebook groups, Reddit communities (r/medicine, r/law), Slack communities for healthcare professionals or legal tech teams. Direct outreach to 10 professionals in those fields.
Developer tools (coding agents, debugging tools): Post in developer Discord servers (Python Discord, Coding Den), Reddit communities (r/learnpython, r/Python), developer WhatsApp or Telegram groups you are already in.
Voice or conversation AI: Give the URL to 10 friends and ask them to have a conversation with it. Post in startup communities. Direct outreach to founders of small businesses who handle phone inquiries.
The DM that works:
"Hey [Name], I have been building an AI tool that [one sentence on what it does] and I wanted to get feedback from someone in [their field]. It is free to use and takes about 5 minutes to try. Would you be open to testing it and sharing one piece of honest feedback? Here is the link: [URL]"
Keep it short. Do not over-explain. The link does the explaining.
A user is someone who visited the URL and used the core feature at least once. Not someone who said they would try it. Not someone who bookmarked it. Someone who actually used it and gave you one piece of feedback.
The feedback questions that tell you what to build
After someone uses the product, ask three questions. You can do this over WhatsApp, email, or a 5-minute call.
- What was the first thing you noticed when you started using it?
- Was there a moment where it confused you or did not work the way you expected?
- If this were a product you could buy, would you pay for it? Why or why not?
Question 3 is the important one. The answer, regardless of yes or no, tells you whether you are solving a real pain or a nice-to-have problem.
The launch post that drives actual signups
Most launch posts perform badly because they lead with features. Nobody cares about features. They care about problems.
Structure that works:
Line 1: Start with the problem or a specific number. Never start with "We built." Never start with "I am excited to share."
Good openers:
- "A doctor was uploading patient records to ChatGPT. Her hospital had no idea."
- "I trained a model to outperform GPT-4 on SQL queries. It costs 100x less to run."
Body (3 to 4 short paragraphs):
- The specific pain (2 sentences)
- What you built (2 sentences, concrete)
- The hardest thing about building it (2 sentences, honest)
- The early result (numbers and a quote from a user if you have one)
Closing (1 to 2 lines):
- GitHub link or live URL
- Call to action: "If you work in [industry], DM me to try it" or "Join the waitlist: [link]"
Post it the same day the artifact deploys. Not a week later. The momentum from shipping day is real and it does not last.
Converting free users to paying customers
You will not sell from the landing page first. You sell from conversations.
Go to your list of 10 beta users. Pick the 3 who gave the most positive feedback or who used the product most frequently.
Send this:
"Hey [Name], glad you got to try [product]. We just launched the paid version with [specific feature they do not have in free tier]. Since you were one of our first users, I wanted to give you 50% off the first 3 months at [price] instead of [full price]. The offer is open for 3 days. Interested?"
This works because: it is personal (they are already a user), it offers a real discount with a deadline, and it removes the "should I pay full price" decision and replaces it with "should I pay half price for something I already use."
If they say yes, send the Stripe payment link.
Three paying customers changes the story you tell about your product. It proves the model is real, not theoretical.
The GitHub strategy that drives organic discovery
Open-source is marketing for developers. An AI tool with 500 GitHub stars tells a hiring manager "this person built something the community found valuable." It tells a potential customer "this person is serious about this."
The launch sequence that produces early stars:
- Business Lead publishes LinkedIn launch post
- Artifact Lead publishes a separate LinkedIn post with GitHub link and demo video
- Both posts get comment engagement from the full cohort in the first hour
- Post to Hacker News: "Show HN: [Product] - [One sentence description]"
- Post to relevant Reddit communities with "built this, feedback welcome" framing (never sales)
- Submit to AI newsletters (Ben's Bites, TLDR AI, The Rundown AI)
Target: 200 stars on launch day, 500 by day 14.
At 500 stars, you start getting discovered organically. Developers who find the repo tell their companies. Some of those companies have the problem your product solves. Some become customers.
The number that tells you whether the product is working
Weekly active users who come back a second time.
That is the signal. Not total signups. Not GitHub stars. Not LinkedIn post impressions. People who used the product and chose to use it again.
First return visit proves the product is valuable enough to remember. Second return visit proves it is valuable enough to build a habit around. Third return visit means you have a real product.
Track this from week one. If people are not coming back, the product is not solving the problem well enough. Fix that before you invest in paid acquisition or sales outreach.
Want to build AI products that get traction?
In AI Engineer HQ, the Business Lead track covers this entire process: launch strategy, beta user acquisition, structured feedback collection, SaaS setup (landing page and Stripe), and the case study that goes to hiring partners and enterprise clients. It is not theory. Members post live products and real user numbers every week.
What I build and how I can help
- MasterDexter live cohorts
- MasterDexter Teams - private cohorts to train your AI team on production systems
- AITalentStudio - vetted, production-ready AI talent for your company
- Dextar - AI engineering development and consulting for enterprises and startups
- Buildership - ideas to ship real AI
