Systems | Development | Analytics | API | Testing

Build Embedded AI Analytics with Claude Code in 30 Min | LiveBuild | ThoughtSpot

Embedded analytics in 30 minutes: watch us embed an AI analyst and branded analytics into an app with ThoughtSpot's SpotterCode in Claude Code. Live Thursday, October 29 8am PT · 11am ET · 3pm GMT · 8:30pm IST What you'll see: Every step is real code against ThoughtSpot's Visual Embed SDK and REST APIs. No ThoughtSpot experience needed. Speaker: John Hegele, Principal Technical Architect, ThoughtSpot.

Connect Spotter to the Apps Where Your Team Gets Work Done

Your data warehouse can tell you that pipeline dropped. It can't tell you why, because the reason sits in a sales call, a support ticket, or a thread in your team chat. When your AI analyst can't reach those places, you end up doing the last mile yourself: copying numbers into Slack, pasting call notes into a doc, and switching tabs until the story makes sense. Spotter could already connect to any MCP server, but getting one in place meant hunting for a server URL and digging through setup docs.

Introducing Apache Kafka 4.4.0

By Omnia Ibrahim | Thursday, October 22, 2026 We are proud to announce the release of Apache Kafka 4.4.0. This release contains many new features and improvements. This blog post will highlight some of the more prominent ones. For a full list of changes, be sure to check the release notes. With 25 KIPs and 963 commits since 4.3.0, this release introduces many new features, improvements and bug fixes to all the components.

AI Workspace + Jira: Increase Jira test coverage with agentic workflows

Requirements change in Jira all the time, and QE teams have to catch every edit to update their test cases. Connect Tricentis AI Workspace agents to Jira and qTest, so any requirement change generates and updates test cases for you with the right context. In this walkthrough, Senior VP of Product Marketing Ryan Yackel, will show you how a Jira edit triggers an agentic workflow, pushes test requirements into qTest, and creates test cases with a human-in-the-loop checkpoint.

Data Integrity: Stop testing data by hand

Someone opens a spreadsheet, pastes data from two systems side by side, and compares row by row, hoping to catch every error before it reaches production. In conversations with enterprises, nine out of ten still test data this way. In this short video, Adnan Ćosić, Senior Product Marketing Manager at Tricentis, explains why "stare and compare" and custom scripts can't keep up with modern data landscapes, and how Tricentis Data Integrity automates data testing end to end so you can trust every decision.

How to measure productivity in the age of agentic testing

In this AI transformation roundtable, Nate Custer, Principal Technologist at TTC Global, shares how to measure productivity on an agentic quality engineering team. Drawing on a mainframe regression story, he explains why saving time only matters if you use it to close coverage gaps. He also argues that QA teams should track how often they have to fix AI-generated code, and why that measurement might mean more than speed or headcount.

Stop Reading JSON Logs: Let AI Fix Your Slow Impala Queries

Debugging slow SQL queries shouldn't mean drowning in thousands of lines of JSON. In this video, we test the new Impala AI Query Profile Analyzer natively in the web UI. Watch how generative AI investigates query execution, tests multiple performance hypotheses, and finds the exact 1-line SQL fix to eliminate extreme data skew across 27 million rows.

Kong Insomnia 13.4: See Like Never Before

Kong Insomnia 13.4 is all about giving you more visibility than ever before. Whether it's your git changes, your secrets, or how your users are using Insomnia, you’re able to see more than ever before and make better decisions than ever. We’ve gotten great feedback on the native git support we shipped in v13 a few months ago, and many teams have switched to a CLI way of working. But other teams love the Insomnia Git UI.

AI FinOps: Moving Beyond Cost Visibility to Control, Optimization, and Value

AI FinOps must not be an afterthought but should be a critical and mandatory component for any enterprise AI program.. The explosive growth of AI workloads from simple AI-assisted coding to product autonomous multiagent systems complicated by highly dynamic usage-based pricing has introduced next level risk in tracking and managing spend. As the Gartner report points out, 85% of enterprises have no automated controls to stop cost spikes.