dbt Wizard: an AI agent that actually understands your dbt project
Data modeling is easier with coding assistants that actually understand the projects they’re working on.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Build Custom, AI-Ready API Endpoints Without Writing Backend Code
Auto-generated APIs changed how fast teams ship. Point DreamFactory at a database and you get a complete REST API in seconds: every table, full CRUD, live documentation, role-based security. For thousands of teams, that is the whole job. But auto-generated APIs mirror your schema. Your applications, and increasingly your AI agents, want something more deliberate: clean paths, shaped responses, and endpoints that match how the consumer thinks rather than how the database is laid out.
SmartBear MCP for Zephyr: Connect your testing system of record to your AI tools
Your SmartBear Zephyr test data holds the answers you need before you ship: what’s covered, what passed, where the risk sits. That data has always lived one context switch away, behind the Jira UI. The SmartBear MCP Server changes that. It brings your Zephyr test data into any MCP-compatible AI client, so quality keeps pace with how fast your team builds. This guide covers where testing sits in the AI age, what MCP is, and how it unifies data visibility within your Zephyr workflow.
Top Challenges of Interoperability in Healthcare and How AI Is Helping Solve Them
Healthcare interoperability enables clinical and administrative systems to exchange usable patient information. However, connectivity alone does not ensure accurate interpretation or workflow compatibility. Many of the challenges with interoperability in healthcare have less to do with moving data and more to do with whether the receiving system understands what that data means. FHIR standardizes healthcare data exchange through structured resources and implementation frameworks.
Real-Time Fraud Detection with Edge-to-AI | Cloudera Data in Motion Demo
Learn how to build an end-to-end, real-time edge-to-AI data pipeline to tackle critical enterprise challenges like credit card fraud detection. In this demo, Diby Malakar (Product Lead for Data in Motion) demonstrates how to process an average of 5,000 transactions per second in low hundreds of milliseconds to detect fraud instantly. Discover how Cloudera’s Data in Motion suite enables application developers to ingest, govern, and enrich streaming edge data to power instant AI model inference and live analytics.
The Brutal Reality of Enterprise AI: 90% ROI Fail Rate?
Stop burning budgets on AI for AI's sake. With billions pouring into data centers, many enterprises are experiencing massive buyer's remorse. How do you ensure your company is part of the 10% actually driving results? Guest Mark Ritcey breaks down.
Why Testing AI Agents Is Bleeding Your Budget (And How to Fix It)
Every AI agent you plug into your stack runs up a bill, one token at a time. Third-party systems, LLM calls, credit checks. In lower environments, that adds up fast, and sometimes those services aren't even available to test against.
Who's Responsible When AI Gives You the Wrong Answer? Andy Cotgreave and Francois Lopitaux Debate
If an AI agent gives a business user the wrong number, and a consequential decision gets made off it, who's responsible? The data analyst who built the semantic layer? The platform? Or the business user who asked the question and acted on the answer?
Where AI Delivers Real ROI in Brokerage and Listing Platforms
Two AI features can cost the same to build and land on opposite sides of the P&L. Add a chatbot to a listing page, and you get a support-deflection number that plateaus in a quarter. Rework search ranking so a buyer who types “quiet street, near a school, room for an office” gets the right ten homes instead of 400 filtered results, and you move search-to-contact conversion, which sits at the top of every revenue metric downstream.
Connect AI Agents with MCP | Do It Better with FME & Snowflake
See how AI agents use Model Context Protocol (MCP) with FME to connect complex enterprise and spatial data to Snowflake. In this episode of Do It Better with Snowflake, Safe Software CEO Don Murray demonstrates how FME extends Snowflake Cortex AI Agents with FME workflows for data integration, transformation, and spatial analysis. See how Snowflake + FME can help you: Connect complex data: including GIS, CAD, 3D, LiDAR, BIM, ERP, and unstructured data.