With AI innovation moving beyond the speed of light, your time to develop is now more precious than ever. That’s why we’ve built the GenAI Launchpad – your secret weapon to shipping production-ready AI apps, faster.
memories.sh — One layer for memories, skills, and rules across any agent
One layer for memories, skills, and rules across any agent
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Hey Product Hunt members!
This is Dave Ebbelaar, founder of Datalumina. Today, I’m excited to introduce our latest product, the GenAI Launchpad. This is a project we’ve built after years of working in the trenches of data and AI development, and it’s something I’m incredibly proud of.
At Datalumina, we’ve spent countless hours helping companies implement LLM-based AI solutions, from optimizing internal workflows to automating customer interactions. Through these experiences, we noticed a recurring problem: We were spending too much time on project setup, structure, integrations and not enough on actual AI engineering. That’s why we created the GenAI Launchpad—a streamlined, modular repository designed to accelerate AI projects and get your ideas into production faster.
The GenAI Launchpad is not another agent framework, it's an end-to-end project repository that covers everything from initial setup to deployment. Think of it as your fast track to building scalable, production-ready AI applications, without all the headaches of configuring infrastructure from scratch.
Here’s what makes GenAI Launchpad stand out:
✔️ Event-driven architecture
✔️ Docker-based deployment
✔️ Database integration + RAG
✔️ AI pipeline & routing logic
✔️ Structured output with LLMs
✔️ Standardized project structure
✔️ Prompt management system
✔️ Built in pure Python
Who is this for?
→ AI Engineers: If you’re tired of setting up yet another project environment, the GenAI Launchpad will let you get straight to the fun part—coding and innovating.
→ Startups: Need to move fast and deliver results? Our standardized, yet flexible project structure helps you build and deploy without the overhead, so you can iterate and scale quickly.
→ Enterprise Teams: Want to bring AI into your operations but don’t have a dedicated team? The Launchpad offers a standardized structure that your engineers can quickly pick up and customize, reducing the backlog and getting projects live faster.
We’ve seen firsthand how the GenAI Launchpad speeds up development and simplifies workflows, and we can’t wait for you to try it out. Whether you’re an individual developer or part of a larger team, this toolkit is designed to help you build smarter, faster, and with full control over your projects.
We’re excited to bring this to the Product Hunt community, and we’d love to hear your feedback! Feel free to ask questions, share your thoughts, or just say hi in the comments below.
Thanks for checking out the GenAI Launchpad!
— Dave Ebbelaar, Founder of Datalumina
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Hey hunters!
We're very excited to be able to showcase our work to the world.
The idea came about after many conversations with clients looking for ways to scale their AI teams effectively and deploy reliable solutions quickly. As Datalumina’s Head of Operations, I’m excited to introduce the GenAI Launchpad—a tool that embodies a deterministic approach to AI development to address these needs. Clients often need tested frameworks to bring their ideas to life in a stable, production-ready environment, and that’s exactly what the Launchpad delivers.
The Launchpad is built around a deterministic approach to AI application development—a principle that’s becoming crucial for the success of LLMs in production. One of the significant challenges in deploying AI agents has been their non-deterministic nature. Each agent action carries a risk of failure, which compounds as tasks grow in complexity. The result? Increased unpredictability and challenges in creating truly reliable, production-ready agents.
The Launchpad embraces the concept of structured, deterministic workflows, which is supported by experts in the industry. By generating clear, reproducible plans, we’re able to achieve a higher level of predictability and control. This structured approach makes it easier to test, debug, and improve AI performance over time, leading to LLMs that are both dependable and adaptable. We believe that success in deploying AI isn’t just about innovation—it’s about ensuring these systems can consistently meet real-world demands, and that’s what we aim to deliver with the GenAI Launchpad.
Why did you choose Python and Javascript?
Because I believe for apps nothing comes close to Next.JS
Although I would get if you wanted the code to be simple and therefore choose Python.
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Maker
Hi Jasper,
The GenAI Launchpad is designed specifically as a backend framework aimed at AI engineers and data scientists, so our focus isn’t on frontend implementation. That’s why we didn’t go with Next.js or other front-end frameworks, even though they’re excellent choices for building applications. Python was our primary language choice because it’s the preferred tool for most AI and data science workflows.
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Can you provide more details on the choices you made for the framework?
Ont he graph in the video it says you are using FastAPI but also Alembic so you're using SQLAlchemy with FastAPI? Also the repo says there is some Jinja code.
I am curious, and would potentially buy it. But really want to make sure I know what's inside it, before I do.
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Maker
Hi everyone!
I’m Joris Ebbelaar, one of the software engineers who helped build the GenAI Launchpad. We’ve combined our AI and software engineering expertise to create a streamlined project template that we now use across all of our projects. It’s helped us speed up development and, because we’ve standardized so much, it keeps maintenance low and deployment straightforward.
If you’re tired of reinventing the wheel every time you start a new AI project, the GenAI Launchpad might be exactly what you’re looking for. Looking forward to hearing what you all think and answering any questions you might have!
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Having watched most of Dave’s AI development videos I know he always makes sure to write clean and maintainable code.
Plus the tech stack is really my cup of tea.
Will definitelly be using this.
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Congrats on the Launch! To all the team at Datalumina.
Lets put it to the test today for a fresh use case I am working on.
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@brian_mulder, I'm keen to follow your Launchpad-Implementation progress, as most product creators do not demo the step-by-step use of their product, on real-world projects.
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