Pain Points from 100 Product Launches

If you've begun exploring AI and LLMs, you've likely seen your new ideas mirror products released on Product Hunt. After this happened to me one too many times, it led me to the question - is there a business opportunity in selling 'shovels' during this 'gold rush'?

Understanding Pain points

To find out, I teamed with Chris Robinson for an experiment: pinpoint the hardest parts of product development for builders launching on Product Hunt.

Why this method? Responding to comments and managing launch day activities are crucial for these builders on PH, suggesting this method could yield better response rates than cold outreach.

The Underlying Hypothesis

Here's what was speculated: With the workflows of LLMs in AI, developers build similar, non-core functionalities for their systems repeatedly.

This repetition hints at a potential market for offering these common solutions as outsourced services, letting developers concentrate more on their core value proposition, boosting efficiency and effectiveness.

Methodology: Asking the Right Questions

To verify this hypothesis, we designed a conversational survey with variations of the 'mom test' questions, including:

1. "What was the hardest thing you faced when building this?"
2. "What was the most painful and tedious part of this app to develop?"
3. "Did you have to build anything that you didn't anticipate?"
4. "Was there any part of the development process that you wish you could have bought versus built?"

Follow-up questions mainly focused on their solutions to these problems and satisfaction with the outcomes. We included all products and did not limit them to AI products.

Key Findings

From the responses, several common pain points emerged:

1. Integrating and optimizing APIs (19 mentions)
   • Difficulty in fetching data without being blocked or throttled
   • Challenges in working with APIs like YouTube, Vimeo, and OpenAI
   • Handling API changes or limitations
2. Data management and processing (11 mentions)
   • Combining different datasets and structures
   • Developing custom data caching
   • Understanding and managing the data flow in the user interface
3. User interface and user experience (11 mentions)
   • Creating intuitive and consistent layouts across devices
   • Balancing information density for easy consumption
   • Designing a seamless component templating system
4. Features and functionality (10 mentions)
   • Deciding which features to cut and focusing on essential ones
   • Implementing real-time rendering of depth data
   • Developing and optimizing email client
5. Learning new technologies and platforms (8 mentions)
   • Coding for different platforms like Apple and Android
   • Adapting to and experimenting with new technologies like OpenAI
6. Technical challenges and testing (8 mentions)
   • Handling edge cases and browser support
   • Testing performance and dealing with bugs
   • Ensuring code quality and reusability
7. Marketing and understanding user needs (6 mentions)
   • Listening to user feedback and adjusting product focus
   • Collaborating with marketing professionals on product development
   • Targeting a wide audience while taking into account user preferences
8. Balancing user feedback and refining the product (4 mentions)
   • Iterating on design and functionality based on user feedback
   • Bridging the gap between user expectations and product offerings
   • Managing user feedback via platforms like canny.io
9. Developing prompt engineering and AI capabilities (4 mentions)
   • Ensuring AI-generated content is accurate and relevant
   • Managing the cost of AI-driven features and services
   • Learning and experimenting with AI technologies like GPT-3.5turbo
10. Challenges in working with third-party services (3 mentions)
    • Stripe ecosystem limitations
    • Managing communication with external services
    • Adapting to third-party service restrictions

These findings show that during development, founders deal with integrating and optimizing APIs, managing data, honing UI/UX, and adding features. They also grapple with learning new technologies, navigating technical challenges, understanding user needs, developing AI capabilities, and working with third-party services.

The raw comment data and questions can be found here.

Interpreting the Insights

Hypothesis Verified: The challenges align with our hypothesis - common, non-core tasks impede focus on unique propositions.

OpenAI and API Issues: Numerous OpenAI-based product launches may explain the frequent API issues.

Confirmed High Engagement: The anticipated high engagement materialized, validating our approach.

Next Steps

The study continues. We'll reconnect with these builders in the coming weeks to understand post-launch challenges - covering growth, retention, and churn as products mature.