Robynn AI

Robynn AI

Ship Marketing like you Ship your Products.

4.8
39 reviews

615 followers

Robynn is an AI-native marketing platform for startup founders and solo marketers. Instead of re-explaining your brand to AI tools every session, you build a Brand Hub once—your product, voice, positioning, and competitive intelligence—and it becomes living context for every agent and workflow. Run end-to-end content pipelines, ad campaigns, and ABM programs that actually sound like your brand, without the enterprise marketing team.
SingleStore Kai gallery image
SingleStore Kai gallery image
SingleStore Kai gallery image
SingleStore Kai gallery image
SingleStore Kai gallery image
Free Options
Launch Team
Vy - Cross platform AI agent
Vy - Cross platform AI agent
AI agent that uses your computer, cross platform, no APIs
Promoted

What do you think? …

Jason Thorsness
🟣 Greetings Product Hunters! 🟣 SingleStore Kai for MongoDB enables apps written for MongoDB to work against SingleStoreDB - just switch your connection string! So what is SingleStoreDB? 🤔 Here are three things you should know: 1. SingleStoreDB stores documents in an optimized data structure that combines the best of row-store and column-store 🥞 and compiles queries using state-of-the-art LLVM to optimized machine code 🔥 2. It's really, really fast at large-scale aggregations and analytics 🚀 3. SingleStoreDB supports accelerated SIMD vector operations for AI-driven text and image queries ✨ Now all of these capabilities are available to MongoDB applications with no code changes required! Try it today at www.singlestore.com/cloud-trial/kai/ Some quick FAQ: 1. How fast is it? Our benchmarks show many aggregations are 100x to 1000x faster! Some more at singlestore-kai-real-time-analytics-benchmarks 2. How can developers use it? SingleStoreDB can replace or augment an existing MongoDB compatible database (this includes MongoDB, AWS DocumentDB, and Azure CosmosDB with MongoDB API). See introducing-singlestore-kai-for-mongodb for more details! 3. How can I test it out? Go here for a trial account and give it a try at singlestore.com/cloud-trial/kai/ I'm really excited to launch this - thanks for reading! Regards, Jason
Alfranio Correia
@jasonthorsness It is amazing the pace of innovation happening at SingleStore.
Riya Tarat
Madhukar Kumar
The fact that you can do vector functions for semantic search on JSON is phenomenal. This means you can add Gen AI features with LLM on your existing Mongo apps.
Abhinav Srivastava
Thanks everyone for your support so far! We believe this is the *first time* MongoDB apps will feel the speed of SingleStoreDB. You really get the scale of a distributed NoSQL database and the phenomenal analytics of an OLAP database. Icing on the cake is that SingleStoreDB can natively support vector functions that allow companies to build Generative AI applications!
Lav Pandey
Woww !!! SingleStore Kia for MongoDB looks promising. The Benchmark looks exciting! Can't wait to try it out and implement some use cases around it to bring event-based real-time data.
Madhukar Kumar
@lav_pandey Thanks!
Jason Thorsness
@lav_pandey real-time even data is an excellent use case for this! Can't wait to see what you'll build.
Brady Lewis🤖
This looks very helpful! Can’t wait to try it out. Congrats on the launch 🚀
Abhinav Srivastava
@brady_lewis1 Thanks much! Let us know if we can help you on the trial. Here's the link https://www.singlestore.com/clou...
Shubham Pratap Singh
Congratulations on the launch🎉
Abhinav Srivastava
Appreciate that, @shubham_pratap
Svitlana Palamarchuk
Wow, 100x faster analytics with SingleStore Kai sounds amazing! Just a quick question - how does the semantic search work in terms of complexity and accuracy? Great job on this!
Madhukar Kumar
@svitlana_palamarchuk Thanks. We will be posting a video from Jason on how to do this. Basically you can add another attribute in JSON that stores the embedding and then you can run DOT PRODUCT that returns the similarity match. Keep an eye on our website and our YouTube channel.
Jason Thorsness
@svitlana_palamarchuk semantic search compares the 'meaning' of chunks of text to a search query, as the text was understood by an LLM. We'll have a video showing some of this up soon based on this example https://github.com/singlestore-l... I tried it with book titles and descriptions (using OpenAI's text-embedding ada-002 model) and for example when I searched "funny astronaut stranded on mars has to survive, movie" I got the book The Martian as the top result, and when I searched "some guy rides along with a submarine captain classic french" I got 20,000 Leagues Under The Sea as the top result. Using it *feels* like using a search engine like Google or Bing, but in your own database! And thanks for the question and support!
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