Most IRIS developers use $LISTBUILD every day — often without even noticing it.

It is not just a convenient function for building lists. It is also the default internal format used to store row data, global values, and many intermediate structures inside the database engine.

Despite this, the actual binary representation of $LISTBUILD values is rarely discussed. Most developers rely on its behavior, but never look at how the data is really stored.

This article focuses strictly on the binary layout of $LISTBUILD values, based on direct inspection via zzdump.

Introduction

Today, coding assistants like Claude, GitHub Copilot and Cursor have transformed the way developers write code. However, these tools are limited by being isolated from the systems and data sources that developers work with daily. This limitation can be overcome through the Model Context Protocol (MCP), an open standard designed to connect AI assistants to external data sources and tools in a secure and standardized way.

In this review article, we'll explore the current state-of-the-art regarding the MCP within the InterSystems ecosystem.

Hey Community, 

Yeah, I know we have a lot of tips & tricks articles - we even have a special tag for it, right? But I can not share my own collection. In this collection, you can find snippets for beginners, as well as some everyday ObjectScript constructions. In fact, it's also a way to learn something new about my favorite programming language. So, if you guys have anything to add, you are welcome!

Data privacy regulations such as GDPR, LGPD, and HIPAA demand that organizations know exactly where Personally Identifiable Information (PII) lives inside their databases. Yet in practice, most teams rely on manual inventories, tribal knowledge, or external scanning tools that require data to leave the database engine — a process that itself creates privacy and security risks.

This article presents an MVP that takes a different approach: it runs PII detection inside InterSystems IRIS using Embedded Python, analyzing data where it lives and never exporting it to an external process.

The recently published tutorial "Introduction to InterSystems Data Studio" inspired me to check out this product. And I think it’s an interesting look at how to manage a data fabric without deep-diving into complex code. It allows you to connect disparate data silos, transform the data through automated pipelines, and load it into a unified environment for analysis. So, I decided to write up an example of how you can use it. Basically, I'll walk you through the tutorial in case you don't have time to do it on your own. Though I would definitely suggest you actually follow the tutorial - it has lots of useful information.

To see how it works, I stepped into the shoes of a system administrator, logging in with the provided credentials to explore the interface. The layout centers on a few core pillars: defining where data comes from, cataloging its structure, and building automated "recipes" to move it into production.

And the very first step is to establish a connection to my data. 

Abstract

Solar irradiance forecasting is critical for grid stability in photovoltaic (PV) power plants. This article replicates and extends the methodology of Lara-Benítez et al. (2023) "Short-term solar irradiance forecasting in streaming with deep learning" replacing the original offline simulation with a fully operational streaming pipeline built on InterSystems IRIS. We leverage IRIS Interoperability Productions as the streaming backbone, Embedded Python to run MLP, LSTM, and CNN deep learning models, and IntegratedML as an AutoML baseline.

Introduction
InterSystems IRIS Adaptive Analytics is an optional extension that provides a business-oriented, virtual data model layer between InterSystems IRIS and popular Business Intelligence (BI) and Artificial Intelligence (AI) client tools. Adaptive Analytics is powered by AtScale, the AtScale documentation can be found at this link: https://documentation.intersystems.atscale.com

This article will showcase some AtScale features that can facilitate data analysis:

1. Cube creation
2. Excel visualization
3. Parallel Period
4. Queries
5. Snowflake
6. Security

7. Aggregates

* iris-certified-agents are not actually certified in anything

Studying for an InterSystems certification?  Eliminated your entire staff and need to make good on your commitment to AI to support your workload? New to InterSystems products or building your startup on InterSystems technologies?  Onboard these intersystems agents compatible with your model and agentic IDE of choice and speak InterSystems technologies like a protocol droid.

Quick Start  

So if you just want to cut to the chase, got get your agents @ https://github.com/sween/iris-certified-agents

Many organizations that operate systems built on legacy technology stacks are facing significant support and maintenance complexities. They are eager to modernize, but the transition is usually prohibitively complex and expensive. These challenges apply to virtually any legacy tech, while InterSystems-based systems have their own unique nuances.

Key modernization challenges include:

• Refactoring massive amounts of code, including identifying and removing obsolete "dead code."
• Managing complex business logic accumulated organically over decades.

We didn't start with a big AI strategy.

We had a legacy InterSystems Caché 2018 application, a lot of old business logic, and a practical need: build a new UI and improve code that had been running for years. At first, I thought an AI coding agent would help only with a small part of the work. Maybe some boilerplate, some REST work around the system, and a bit of help reading old ObjectScript.

In practice, it became much more than that.

When managing critical healthcare data through an integration engine you want to know the moment a queue starts backing up or a service drops. 

Unfortunately, this usually leads to an email inbox stuffed full of notifications that can sometimes seem impossible to maintain.

Well, I’ve not solved that problem. 

But I have created something that could help...

Hello, community.

I've been working with Intersystem Caché for two years, and right away I was excited about the rich ecosystem that Caché provides. However, I was disappointed with calls using #call, and I understand that it was a limitation of the time. Well, the frustration is that #call returns null by default, meaning an AJAX request is executed, but there's no hook to retrieve the return from that request. The only way to retrieve the data from that request is by building a callback on the server side using &js<>.

. . . you are not alone. 

Help is available.

This took me a while to figure out, and I assume there may be others struggling too. I made my way through all the Entra stuff to set up a client credentials workflow to send email through a Microsoft 365 account. I was able to successfully retrieve my token, but I couldn't ever get it to authenticate with the SMTP server using the %Net.SMTP class. There were two parts to fixing this.

First, the authenticator's access token needs to be more than JUST the access token. It has to be formatted as:

set smtp.authenticator.AccessToken = "user="_emailaddress_$C(1)_"auth=Bearer "_token_$C(1,1)

Redoc is an Open Source solution capable of rendering API specifications in OpenAPI 2.0 or 3.0+ as very beautiful and functional web portals. Currently, to have something similar, we need the ZPM SwaggerUI extension or we need to install IAM - InterSystems API Manager and then configure the IAM Developer Portal. Well, now the community has one more option, iris-redoc. This solution installs a web application on your IRIS instance that uses Redoc to present a beautiful web portal for your REST APIs:

In last post I talked about iris-copilot, an apparent vision that in near future any human language is a programming language for any machines, systems or products. Its agent runners were actually using such so-called 3rd generation of agents. I want to keep/share a detailed note on what it is, for my own convenience as well. It was mentioned a lot times in recent conversations that I was in, so probably worth a note.

Recently, a question on the Community was asked by @Vermon Ferre about storing data from inherited classes in different globals. So, I decided to simulate the following behavior: I created a superclass called Article.MainClass and two subclasses, Article.Class1 and Article.Class2. By default, when each class extends %Persistent IRIS creates independent storage structures for them. This will work as intended if the first class in the list of superclasses is %Persistent. But it also means that if there are any parameters in the main class, they will be lost, because only parameters from the first class in the list get inherited.

Before finding the best solution for Supply Chain, we need to understand what Supply Chain is. Well, regardless of the language used, the term is known by the name already mentioned. For your information, the translation in Brazilian Portuguese is "Cadeia de Suprimentos" (Supply Chain), sometimes also called "Cadeia Logística" (Logistics Chain), but we will not dwell on the nomenclature, but rather on the meaning.

During my work with customers and also following I often met the 
explicit or embedded question: What is this "GLOBALS" ?
I'll try to explain the history to unfold the context.

The PACELC theorem was created by Daniel Abadi (University of Maryland, College Park) in 2010 as an extension of the CAP theorem (created by Eric Brewer - Consistency, Availability, and Partition Tolerance). Both help design how to architect the most suitable operation of data platforms in distributed environments under the aspects of consistency versus availability. The difference is that PACELC also allows analysis of the best option for non-distributed environments, making it the gold standard for considering all possible scenarios to define your deployment topology and architecture.

The CAP theorem states that in distributed systems, it is not possible to have consistency, availability, and partition tolerance simultaneously, requiring a choice of two out of three, according to the following diagram.

Source: https://medium.com/nerd-for-tech/understand-cap-theorem-751f0672890e

Hi everyone,

I’m thrilled to share that after several years of deep diving into the InterSystems IRIS data platform, I have finally summarized my project experiences into a new book IRIS (Data Platform) Programming Technical Guide. It is published by Beihang University Press, a prestigious central-level comprehensive publisher renowned for its leading role in aerospace, science, and technology publishing.

Writing this book was a significant engineering challenge for me. My goal was to bridge the gap between "understanding the syntax" and "building a production-ready project.

Every great community is enriched by those who work tirelessly behind the scenes, crafting the knowledge and resources that help others grow. In the InterSystems Developer Community, one such person is @Derek Robinson — a dedicated educator, whose passion for teaching has helped shape how developers everywhere learn to work with InterSystems technologies.

👏 Let's take a closer look at Derek's journey in the InterSystems ecosystem.

Hi, every one. Again, it's me!!😀😀

Recently I was trying to organize some learning materials for InterSystems IRIS😆, and realized that the resources are actually quite scattered.🤐

So I put together a list here—grouped by categories—for anyone who:

• is new to InterSystems
• or wants to go deeper into specific areas

Also adding some of my own experience on what worked (and what didn’t, may be only not work for me🤫🤐).

1. For starters

If you don’t know where to begin, start here:

• https://developer.intersystems.com/
• https://developer.intersystems.com/resources/
• https://developer.intersystems.com/intersystems-iris-getting-started/

Table Partitioning Demo

This demo walks you through the new Table Partitioning feature in IRIS SQL, explaining what it does and how it works along the way. We'll only use a few dozen rows to prove the concept, but obviously the capability is focused on datasets many orders of magnitude larger.

If you'd like a shorter, higher-level introduction to Table Partitioning, check out the online learning module Managing Tables with Partitioning in InterSystems IRIS

ℹ️ Table Partitioning is included in IRIS 2026.1 as an experimental feature

Today I have published a new Open Exchange package for generation of Synthetic Data directly into IRIS.

 It can be a frustrating process to find decent datasets when you are looking to make a demo app. Maybe the dataset doesn't matter that much, but you still want it to appear somewhat genuine and with several linked tables that are usable directly within IRIS with the neat implicit joins with ->. Maybe you just want linked tables that are easily installable with IPM to benchmark queries, this dataset generation would be perfect.

Hi Community,

Take a look at these new videos in online learning, and get familiar with new features and interfaces in the latest release of InterSystems® products. All videos are also available in the Learning Services YouTube channel.

Plus, check out a video for new ObjectScript developers, and a guide for HealthShare® users upgrading their deployments.

• Goal: Learn about enhancements to the interoperability UI in version 2026.1 of InterSystems products.

We are very excited to launch a new AI powered CCR feature which should help our users save time and prevent calls to Support.  Today we are introducing the new CCR Transport Log Analyst (or CTLA) AI-Generated summary.  The goal of CTLA is to provide an analysis of errors that have occurred in the Transport Log for a specific CCR, and advise the user as to what steps could be taken to correct the situation, as well as what might have been done to prevent it in the first place. When there is an issue with integrating or deploying a CCR, all of the details are captured in the Transport Log.

This article introduces SHAP explainability methods as an approach to understand the reasons behind predictions in machine learning black-box models. It also includes a simple Jupyter notebook that you can use and modify to gain hands-on experience with these concepts:

https://www.kaggle.com/code/jorgeivnjh/explainability-in-ml-models

https://github.com/JorgeIvanJH/Explainability-in-ML-models

We will leverage these concepts for a future implementation in our Continuous Training Pipeline: https://community.intersystems.com/post/complementing-iris-mlflow-continuous-training-ct-pipeline

Introduction

In healthcare interoperability environments, InterSystems Health Connect typically contains critical components such as productions, business processes, operations, services, utility classes, routines, and other ObjectScript artifacts. Traditionally, many deployments of these components have been done manually, by copying classes, importing XML, or using administrative tools from the management portal.

For those of you that weren't at READY last week, you may have missed the exciting announcement that the Early Access Program for AI Hub is officially open. It was announced during an amazing demo from @Benjamin De Boe and @Jeff Fried, I recommend catching up with this demo when the recording is released!  I had the opportunity to play with AI Hub in advance, and thought I might share an introduction with the community.

InterSystems IRIS globals are one of the platform's core strengths: they store hierarchical data in a direct, ordered, and efficient structure. But when working from Python, manipulating globals can sometimes feel closer to a low-level API than to the natural habits of the language.

The iris-global-reference project provides a Python layer on top of IRIS globals. Its goal is simple: make access to globals more readable, more idiomatic, and easier to integrate into modern Python code, without hiding the underlying hierarchical model.

This article presents a straightforward approach to automatically and efficiently tune hyperparameters for machine learning models using Optuna as the optimisation framework. We explore how to use both Optuna’s native storage options and InterSystems IRIS as a database backend to track the progress of hyperparameter searches. We also show how MLflow can be used to monitor experiments and manage models through its tracking and model registry UI.

This article is based on this Kaggle Notebook, which you can run and directly edit yourself.

IRIS 2026.1 provided Partitioned Tables as a new option for large data sets
It's a great improvement as it offers standardization of this feature.

Though:
It was possible also before, matching requirements and leaving room for creativity.
less elegant with a bit more coding and fewer automatisms.