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kong

Kong Gets a New Look with Electric Agentic-Era Rebrand

Sep 21, 2026 By Juliette Rizkallah In Kong
We're pulling back the curtain on a project we've been working on for some time now: the next evolution of the Kong brand. You may have noticed some changes recently to our site, swag, or socials. But today it's official: we're announcing Kong's rebrand and introducing our new mascot, Karl. Without further ado, let's dig in. Along the way, we'll give some insight into the "why" behind it all, while looking back at how things have changed since the early days of Kong. We were the API connectivity company.
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Claude and Codex Are Slowing Your Engineering Team Down #speedscale #devops #aicoding #claude #codex

Sep 21, 2026 By Speedscale In Speedscale
While AI coding tools dramatically slash development time, they are quietly inflating testing and maintenance burdens because teams no longer fully understand their codebase. Discover how leading engineering teams are shifting focus to testing and context packages to eliminate bottlenecks and unlock true AI efficiency.
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RAG in Quality Engineering: Ship Faster, Test Smarter | Janani Balasubramanian

Sep 19, 2026 By The Test Tribe In The Test Tribe
How can Retrieval-Augmented Generation (RAG) help Quality Engineering teams ship faster and test smarter? In this TTTribeCast session, Janani Balasubramanian explores the practical applications of RAG in Quality Engineering and how teams can use organizational knowledge, testing data, and engineering context to improve the way they design, prioritize, and execute testing.
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speedscale

Team-Based DLP: Give Each Group Its Own Redaction Rules

Sep 18, 2026 By Matt LeRay In Speedscale
A shared Kubernetes cluster rarely belongs to one team. Payments runs checkout in one namespace, search runs search-api in another, and a risk team runs a scorer somewhere else. One Speedscale forwarder captures API traffic for all of them. Redacting that traffic before it leaves the cluster is what makes it safe to use for testing (the background is in The PII Testing Dilemma). Until now, that forwarder ran exactly one DLP rule. Every team that needed a field redacted had to edit the same JSON document.
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appian

Best Practices for Modernizing Your Payment Investigation Process with AI

Sep 18, 2026 By Tom Pirone In Appian
AI agents are proliferating faster than most institutions can govern them and the primary challenge is quickly becoming an "accountability gap." Disconnected pilots rarely scale into accountable, auditable operations. The financial services industry is currently at a tipping point: banks must bridge the gap between initial AI enthusiasm and operational reality.
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AI Adoption: What Goes Wrong & How Leaders Fix It | Brenn Hill

Sep 18, 2026 By The Test Tribe In The Test Tribe
In this interactive AMA session, Brenn Hill, AI executive and author of The Delivery Gap, explores why many AI adoption initiatives fail to create lasting impact despite growing investment and enthusiasm. Drawing from his experience helping engineering organizations adopt AI at scale, Brenn unpacks the common pitfalls that hold teams back and shares practical strategies for engineering leaders to drive meaningful adoption. The session will cover how to align AI with business goals, measure success beyond hype, and build a culture that enables sustainable AI-driven transformation.
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End-to-End Test Orchestration using MCP Servers | Raghunath Chilkuru

Sep 18, 2026 By The Test Tribe In The Test Tribe
Most QA teams are still switching between requirement docs, their local codebase, and CI/CD dashboards to get automation done. This session is about closing that gap -using AI not as a code generator you prompt occasionally, but as something closer to an actual QA teammate working inside your IDE. ​Key Takeways:​A working understanding of MCP architecture - how to configure and run local or cloud-based MCP servers to connect your IDE with enterprise tools.
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ClearML

From Experiment Tracking to AI Factory: What Changes When AI Becomes a Shared Enterprise Capability

Sep 18, 2026 By Adam Wolf In ClearML
The term “AI factory” is usually introduced as a hardware story: racks of accelerators, high-speed networking, and validated reference designs. That part is real, but it is not the part most organizations struggle with. The harder shift is operational. Moving from AI as a set of individual experiments to AI as a shared, governed capability that many teams depend on changes how compute is allocated, how environments are built, who owns the platform, and how the work is governed.
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