I've been writing software for a while. I'm comfortable at every layer of the stack. When AI started becoming something you could actually ship into production applications, I did what most engineers do: I built it myself.
First project, not bad. Pick a model, call the API, handle the response. Clean enough. I understood exactly what was happening at every step.
Then the requirements got more complex. I needed multiple models in the same workflow. I needed a document parsing layer upstream of the LLM. I needed the output to land in a database instead of just getting returned to the client. Suddenly I was writing a lot of code that had nothing to do with the problem I was actually trying to solve. Glue code. Wiring. Infrastructure that existed purely to move data between components that were never designed to talk to each other.
I accepted that as the cost of doing business. This is just what building AI features looks like, I told myself.
I used other tools that do similar, but the fact that I can run inside my VS code, it's a must for me
@roan_weigert I don't think I've seen a developer tool that empowers users to build production level AI infrastructure at scale that allows you to focus on build these application entirely within your IDE. Our idea is that less context switching means solving problems faster and building greater value at rocket speed!
Hey guys, I'm an engineer here at RocketRide, and one thing that still surprises me is how fast you can go from an idea to a working system. Connecting agents to tools, models, or entire workflows used to take a bunch of setup and glue code now it’s something you can do in minutes and actually iterate on with speed. What I personally love is how flexible it is. You’re not locked into one framework or provider, you can plug in the agents and tools you want and just focus on the app/workflow itself.
@dylan_savage You're absolutely right! Our shared goal is to lower that barrier for everyone and allow them to build working systems way faster and with ease!
I’m really glad that the C++ engine’s source code has been open-sourced as a monorepo. A unified, transparent infrastructure is the right way to build trust for both usage and development.
@stepmikhaylov Transparency is incredibly important. Being able to see what the code actually does is a great way to build trust in your systems. We strongly believe this will strengthen our position and give our users far more trust in us going forward.
I’m really excited to see RocketRide live 🚀
Coming from a UI/UX and developer experience background, what stood out to me immediately is how the interface lets me focus on the flow instead of the plumbing. I don’t have to keep rebuilding the same integrations or mentally track how everything connects. I can actually see what services and I using and how my pipeline is doing from start to finish, which saves a lot of time and removes friction. I can even let my coding agent, build a pipelines for me as it understands and has knowledge around how to interconnect and compose pipelines for my project.
There’s still a lot of exciting features and integrations to make it even more intuitive for developers at any level. The goal is to make getting started with AI feel obvious and seamless, not overwhelming.
Super excited for what’s coming next and would love to hear what everyone thinks!
@luisson10 This is exactly what we are trying to solve. We think building AI should be easy, and dare I say, enjoyable.
This is awesome! I am so happy to launch this product into open source after years of development. This product finally makes embedding AI into applications as easy as drag and drop. Looking forward to working with you all on improving this even more!
@rod_christensen1 A big thanks to you Rod, for the years of hard work and sleepless nights that lead us to this transformative technology. Open-sourcing was absolutely the right idea, and I look forward to being apart of it's growth in the coming future.