Generative AI refers to algorithms and models in artificial intelligence that are capable of generating new data or content that is similar to existing data. These models are trained on large datasets and learn to generate new examples that mimic the patterns and characteristics of the original data.
Keywords: IRIS, Agents, Agentic AI, Smart Apps
Transformer based LLMs appear to be a pretty good "universal logical–symbolic abstractor". They started to bridge up the previous abyss among human languages and machine languages, which in essence are all logic symbols that could be mapped into the same vector space.
Wondering for 3 years we might be able to just use English (etc human natural languages) to do IRIS implementations as well, one day.
There are always those who say that their child looks more like their mother or their father. How about using math and Artificial Intelligence to be sure? The facial-matching application can answer you.
See these results:
Hi developers!
I'm testing vibecoding with ObjectScript and my silicon friend created a code-block that got me thinking "what's wrong"?
Here is the piece of code:
for i=0:1:(json.%Size()-1) {
set p = json.%Get(i)
if (p="value1")!(p="value2") {
quit 1
}
AI wanted to quit from a method with a return value. Good intention, but bad use of the command.
And ObjectScript compiler compiles this code with no error(?) (syntax linter in VSCode says it's a syntax, kudos @Brett Saviano ).
But in action, it produces <COMMAND>, of course.
Hey Community!
We're happy to share a new video from our InterSystems Developers YouTube:
⏯ Develop the Next Generation of Health IT with InterSystems@ Ready 2025
10:47 AM — Jose Garcia's creatinine test results arrive at the hospital FHIR server. 2.1 mg/dL — a 35% increase from last month.
What happens next?
No chatbot. No manual prompts. No black-box reasoning.
This is event-driven clinical decision support with full explainability:
✅ Triggered automatically by FHIR events ✅ Multi-agent reasoning (context, guidelines, recommendations) ✅ Complete audit trail in SQL (every decision, every evidence source) ✅ FHIR-native outputs (DiagnosticReport published to server)
Built with:
You'll learn: 🖋️ How to orchestrate agentic AI workflows within production-grade interoperability systems — and why explainability matters more than accuracy alone.
<iframe width="560" height="315" src="https://www.youtube.com/embed/43Vl7cU_uNY?si=o3NZ3AqPOdFkCn9w" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>I wanted to try vibecoding a real backend + frontend setup on InterSystems IRIS, ideally using something realistic rather than a toy example. The goal was simple: take an existing, well-known persistent package in IRIS and quickly build a usable UI and API around it — letting AI handle as much of the boilerplate as possible. Here is the result of the experiments.