AI Chatbots Need Accessibility Testing Too
AI chatbots are transforming how people interact with digital products, but they also introduce new accessibility challenges.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Cloudera Partner Ecosystem: Simplifying Data & AI Across Hybrid Environments
Something major is coming for our customers and partners... Michelle Hoover shares how Cloudera and our partner ecosystem are helping simplify data and AI across environments while driving stronger business value and outcomes. What You'll Learn.
Snow Report: What's Happening At Snowflake in August
The August Snow Report is LIVE Two GA launches for CoCo, warehouse tuning that handles itself, and World Tour in 23 cities. What's new CoCo Desktop is GA A native IDE inside your governed Snowflake environment, so it knows your data models and access policies from day one. Up to 2X faster on complex tasks with 51% fewer tokens than third-party assistants. Cloud Agents are GA Run agentic workflows from your browser in Snowsight. Start with a prompt, close your session, and the agent keeps working on Snowflake's managed infrastructure.
How to fix API contract drift with AI agent skills | SmartBear Swagger Contract Testing
Your AI coding agent can already write code. Can it verify a contract, run provider checks, and tell you whether it's safe to deploy? This video shows you how to set up SmartBear Swagger Contract Testing drift AI Agent Skills, so your agent can spot API drift and write the missing tests for you, without needing the context multiple times.
Claude + MCPs Isn't a Semantic Layer: What Breaks When You Analyze Your Business Without One
Ask Claude for your average sales cycle today. Ask again next Tuesday. Same prompt, same MCPs, same playbook. There is a real chance the two numbers do not match. The reason is not exciting, and it costs you decisions. If you have wired Claude to your CRM, product, and finance tools through MCPs and given it a detailed playbook, you are already ahead of most companies asking whether AI can analyze the business. The gap between that and running the company off the answers is real, though.
Agent Product Use Case - Slack & Jira Discrepancy Workflow
Customers and champions report problems in Slack, but if nobody files the ticket, the issue disappears before it ever reaches Jira. In this video, we use AgentSpot to build a Slack to Jira Coverage Workflow that reads your champions channel every morning, cross-compares it against your Jira backlog, and emails you a report of every issue raised in Slack that no one has filed yet. What is AgentSpot? AgentSpot is an Agentic Workforce Platform for building workflows that make decisions, take action, and deliver results across every system your business runs on, grounded in your data.
Why Do AI Tools Give Different Numbers for the Same Question?
You can ask two AI tools the same question about your data and get different answers, even when both have access to the same system. There are several reasons this can happen. Each one might run a different model, or have a different set of tools available. The data you thought was the same might not actually be the same. Or one tool might have more context about my account than the other. I want to focus on what happens after I rule those things out: each tool still has to decide what my question means.
How DreamFactory Helps Schools Use AI Safely on Their Own Data
Every school, college, and university is being asked the same question right now: Can we use AI on our own data without putting student records at risk? The promise is real, including personalized learning, faster advising, and smarter operations. So is the fear. AI that touches student information runs straight into FERPA, breach risk, and a simple trust problem: once data leaves your control, you can't govern it.
One interface for API contracts, test coverage, and production signal: Build AI quality workflows with the SmartBear MCP Server on Amazon Bedrock AgentCore
For decades, the software development lifecycle (SDLC) gave engineering teams a common framework. Requirements became designs. Designs became code. Teams tested, deployed, monitored, and improved it.