Marc Scholten discusses building Textcontent, an AI-powered marketing content generation tool used by over 1000 people across 300 businesses. The product started as an e-commerce ad generator but pivoted to general marketing content creation. Built by a 1-2 person team using Haskell, it leverages OpenAI's GPT models rather than
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Since our last interview , you’ve started working on a new product, TextContent. Could you please tell us about how the idea was born and what it looks like today?Why did you choose Haskell as the programming language for Textcontent?How large is your Haskell team?How does Textcontent utilize Haskell’s functional programming paradigm to implement machine learning algorithms, and what advantages does this approach offer over imperative programming styles?Could you describe the process of developing and training ML models within the Haskell environment? Haskell isn’t typically the first choice for ML projects; ecosystems like Python have a much larger choice of libraries. How do you overcome this challenge?What unique challenges does Textcontent face when developing ML models in Haskell, and how do you address issues related to the ecosystem, library support, or community resources?How does your team ensure code quality and maintainability in Haskell, particularly for complex AI algorithms?Sort: