Launching today
RocketRide
Focus on your app. We do the AI.
12 followers
Focus on your app. We do the AI.
12 followers
Building AI features today means navigating a maze of competing frameworks, juggling provider API keys, and wiring together components that were never designed to work as a system. Most engineers spend weeks on infrastructure before writing a single line of application logic. RocketRide is an open source, developer-native AI pipeline tool that changes that. Pipelines are first-class JSON objects. Models are interchangeable. The entire workflow lives in your IDE.
@joshuarocket Exciting
Hey Product Hunt! I'm Ariel — software engineer with over a decade building production systems in C++, Python, Java, and Ruby, plus a research background in AI, agent-based simulation, and computer vision.
Over the years I've built the kind of systems that RocketRide now makes trivial to assemble. At a logistics company, I spent months building NLP pipelines to extract structured data from unstructured PDFs — parsing, regex, linear regression, Kafka streams, ERP integrations. It worked, but the plumbing consumed more engineering time than the actual intelligence. At a robotics lab, I built a 3D point cloud pipeline on a Jetson Nano — OpenCV, C++, frame-by-frame processing. Every time the input format changed, I rewired half the system.
With RocketRide, that kind of work is a .pipe file. I recently built a PR analyzer: GitHub diffs flow through a parser, get chunked, embedded into Qdrant, and become queryable through an LLM. The entire pipeline is a JSON config I can version-control, and swapping the LLM from Claude to GPT is a one-line profile change. I also wired up a text-to-audio pipeline — drop a file, parse it, run TTS, hear the output. Four nodes, zero glue code.
What hits different when you've done this the hard way:
Typed data lanes mean you don't debug data format mismatches at 2am. Text flows to text, documents to documents, questions to answers. The pipeline validates the flow before it runs.
The pipeline format is plain JSON. Every node and connection is inspectable and diffable. When a better embedding model drops, you update a config value — you don't rewrite your integration.
The MCP server makes your pipelines available as tools inside Cursor or Windsurf. I use this daily — my pipelines are callable from my IDE without any extra setup.
I've taught AI courses at university, published research on agent-based systems, and shipped production backends for years. The bottleneck was never the models or the algorithms — it was the glue. RocketRide removes the glue so you can focus on the problem you're actually solving.
Happy to answer questions about integration patterns, vector DB setups, or how to get from zero to a working pipeline.
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.
This is one of those launches that makes you immediately want to open your IDE and try it. Clean concept, strong dev focus, and the model-swapping flexibility is a huge win.