Launching today
RocketRide
Focus on your app. We do the AI.
39 followers
Focus on your app. We do the AI.
39 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
@aviral_bhardwaj1 We are so excited! We think this is a huge deal! That may sound a bit biased, but we feel this way and shout it from mountain tops because we built this tool for us. And man has it changed not only the quality of value we produce but also the rate at which we do it.
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.
@dsapandora I know the struggle. Having a reliable system that makes it easy to build and debug is such a relief when building AI systems. That observability is something that has saved me so much time debugging my pipelines allowing me to move on to the part of the process that I actually love doing. Adding BIG value and not worrying about unpredictable bugs and setbacks is such a great feeling!
As a co-founder of RocketRide, I want to share a few data points about this open source developer tool.
The runtime engine was built, enhanced, refactored, rebuilt and tested over 100's of TB to Pbs of data processing for discovery, insights, analytics, transformations and data actions for companies from SMB to Enterprise. This has been nearly a decade of investments of several million $$ plus IP (with patents) that has now been made free and open source (MIT license) and soon to be donated to a true 3rd party, non-profit foundation for the world to use.
RocketRide (the new company) is 1 month old today. While it's true that most of us on our small team have been working exclusively on this project since the start of 2026, we became official RocketRiders on Mar 1. Our team is small (12), we heavily leverage AI across all roles, and most of us work from our office in downtown SF (near Union Square). So far in 1 month we've achieved 1100+ GitHub stars, 3400+ downloads and 750+ members in our growing Discord server -- good, but not viral (yet).
We believe we can make a real difference in most developers day-to-day work, and that we're filling whitespace in the market - the nexus of (1) an abstraction framework for removing low-value integration coding work and reducing time-consuming discovery and experimentation, (2) IDE-native integration to support a true "AI Command Center" (e.g. one place you can do everything), (3) open source to build trust, enable rapid customizations and better empower the era of AI coding agents, and (4) self-hosting, meaning you don't need to use anyone's cloud or swap in and out of browser tabs.
Others on our team and from our community have (or will) share their perspectives on the value of RocketRide and how it helps them build more robust and scalable AI applications. I invite you to join our community, check out our repo and try out our extension (or SDK). And yes, we're asking for your upvote in today's Product Hunt Launch.
Discord: https://discord.gg/7W6t4pQd
Repo: https://github.com/rocketride-org/rocketride-server
Website: https://rocketride.org/
Thanks for reading, and we'd love to hear what you think.
Hi Guys - as a non-developer I have never been able to leverage the full power of AI myself and I've been stuck using single LLMs to solve my issues.
With RocketRide I am currently working on a pipeline that monitors the entire US stock market 24/7, filters it down to high-conviction opportunities using financial benchmarking ratios, trading information, SEC insider filings and congressional STOCK Act trades, then runs the survivors through a 5-LLM consensus panel. Another LLM synthesizes all five opinions into a final signal and texts you on WhatsApp only when everything lines up as a STRONG_BUY based on certain criteria I have identified.
Still in prototype — back testing the signals. But I never would have been able to conceive of the idea without RocketRide!
@bchristensen Great point! Our drag and drop canvas makes it easy for people versed in non technical skill to build AI systems to assist them with their daily workflows.
Anirudh here from RocketRide. I'm working on a really interesting use case with this product serving as the backend.
With our connections in Europe, I'm working on a SaaS platform that can detect and extract highlights of a race car in hours long worth of race footage. We're aiming to change how sponsors evaluate their screen time on the race tracks, and RocketRide has been such a powerful tool for this. The entire pipeline was able to be built in less than 2 minutes, because all the nodes were already there, fully secure and robust.
This platform is a dream come true, and it's really great to work on something that will change the developer world the same way companies like Cursor and Anthropic have.
@anirudhk_tech I love the app you've built using RocketRide for the AI pipeline parts. Soon this app will be running in production on the rocketridel.ai cloud and will capture that market. let's gooo!
@anirudhk_tech This is an incredibly use case that highlights the capabilities of our tool. Thank you for sharing your experience building with RocketRide. I'd also like to point out that, although RocketRide ships with a large list of native nodes, we are open source and are so excited to see what our community introduces to the tool. We know it's going to be incredible, and look forward to what the future holds.
Hey guys! Shashi, here from RocketRide. We built RocketRide to make AI pipeline development more open, flexible, and developer-native - with portable JSON pipelines, support for multiple models and tools, and infrastructure teams can actually inspect, extend, and run themselves. A lot of work went into getting this out into the world, and it means a lot to see people engaging with it already.
One of my favorite moments using RocketRide was putting together an agentic code review + RAG pipeline in under ONE HOUR. Instead of spending that time wiring together model calls, retrieval, and tool logic manually, I could focus on the workflow itself and iterate quickly with nodes being just drag and drop. It made it much easier to review code with relevant context pulled in at the right step, and the JSON pipeline format & agent traces being tracked, kept everything transparent and observability maintained!(my fav. feature)
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!