Lately, it's nearly impossible to go a day without encountering headlines about generative AI technologies and their applications like ChatGPT or AutoGPT. AI has become red hot again, and its hotness is astonishing: suddenly almost everyone wants to jump on the AI bandwagon.
CXSCOOP, the leading media featuring latest information technology for customer experience and customer relationship management, published a story about Juji. Senior Tech Reporter, Sandra Radlovacki Vukanovic, recently featured Juji in an article titled "New Juji Tool Helps Companies Automate Chatbot Building".
In her article on Axios, journalist Joann Muller highlights the changing face of customer service chatbots. She underscores that improvements in AI technology have made chatbots more personable and effective in providing assistance.
We are thrilled to announce that Juji has been featured in a New York Times article titled “'No-Code' Brings the Power of AI to the Masses”. The article by Craig S. Smith explores the rising trend of no-code software that enables anyone to utilize artificial intelligence without needing to understand complex programming.
In a recently published
article
in InfoWorld, our esteemed CEO Dr. Michelle Zhou addressed the rapidly evolving landscape of AI technology and discussed a breakthrough that could be a major game-changer across industries - the advent of No-Code Reusable AI. This article provides a profound perspective and insight into the future of AI. Today, we're going to dissect and delve into the key points of the discussion.
Juji, an AI-driven conversational technologies company, has made an open-source
contribution to the world with
Datalevin, a versatile, lightweight, and
fast embedded database engine. In addition to being powered by the Datalog query
language, it also acts as a Clojure native Key-Value (KV) store. This transactional database solution is agile and dynamic, suitable for handling complex data structures.
In my Clojure/north 2020 talk on "diffing-based software architecture patterns", I mentioned that Juji is using Editscript, a library I developed, to diff Clojure data structures. During the Q&A session of the talk, someone brought up another Clojure diff library, called deep-diff2, which I was unaware of. Then on Youtube, a comment asking the difference between Editscript and deep-diff2 appeared again. This prompted me to do an investigation on Clojure data diff libraries. Given how the Clojure community places such an emphasis on data oriented programming, a comparison of data diff alternatives appears to be of interest.
We all know that user interface (UI) development is an iterative process. It is important that we can iterate quickly based on user feedbacks. At Juji, we have been constantly searching for solutions that enable faster iterations for our Juji Studio product. Around the end of last year, we did a major revamp of Juji Studio UI. By all accounts, this change made a huge difference in term of usability of Juji Studio. More importantly, we can now iterate much faster than previously possible. What's more, we did the wholesale changes in less than one month! Here is how we did it.
When people first use Juji, they are often amazed by how easy it is to create an intelligent chatbot with the platform. This reaction of pleasant surprise is particularly pronounced for people in the know, i.e. technical people who have actually done relevant work before. I am talking about the CTOs, the NLP researchers, and the employees of big technology firms.
