Systems | Development | Analytics | API | Testing

Snowflake CoCo: Welcome to the Agentic Enterprise

Jun 22, 2026 By Snowflake Inc. In Snowflake
When business questions move faster than answers, teams need more than dashboards. They need AI agents that can break silos, add context, and turn trusted enterprise data into action. Meet Snowflake CoCo — built to help data teams and business users move from reactive reporting to strategic action. In the Agentic Enterprise, everyone can become a strategic force, shaping what the business does next.
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Codemagic

React Native New Architecture and OTA Updates: What Teams Need to Know in 2026

Jun 22, 2026 By Codemagic In Codemagic
The React Native New Architecture is no longer optional. From React Native 0.82 onwards it is mandatory, the legacy architecture is gone, and every team still running it is now carrying technical debt that will need to be resolved. For most teams, the migration conversation quickly turns to tooling. Does our CI/CD pipeline still work? Does our crash reporter still integrate correctly? Do our analytics tools need updating?
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Agentic Data Engineering: Self-Healing Pipelines for Real-Time Insight

Jun 22, 2026 By Cloudera, Inc. In Cloudera
Brittle pipelines and SLA firefighting hold data teams back. Agentic data engineering introduces autonomous AI agents that detect failures, fix code, and re-run pipelines—with humans in the loop guide critical decisions. This video explains how Cloudera Data Engineering and Cloudera AI enable self-healing pipelines.
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ClearML

Pre-Packaged Inference, Production-Grade: AMD AIMs with ClearML

Jun 19, 2026 By Adam Wolf In ClearML
Running production LLM inference on a new accelerator family is a layered problem. The model matters. The runtime that exists for the GPU you have matters at least as much. So does the precision mode that works without losing accuracy, the inference engine that hits your throughput targets, and the secure endpoint the rest of your stack can actually call. The entire stack underneath the model is where most of the real engineering work lives and where the cost of getting it wrong shows up first.
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yellowfin

Explainable AI in Customer-Facing Analytics: How Yellowfin Turns Predictions into Action

Jun 19, 2026 By Hagop Panosian In Yellowfin
Predictions alone are no longer enough. A churn score is not useful if no one trusts it, and a risk score does not help if the next step is unclear. The same goes for a recommendation engine. People need to know why a model made a call, and what action comes next. That is the core shift in explainable AI for analytics. The work has moved from “what happened?” to “why did it happen, and what should I do now?” Customer-facing analytics depends on that shift.
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bugraptors

Architecting Reliable AI: The Complete Technical Framework for Multi-Agent System Testing

Jun 19, 2026 By Prateek Goel In BugRaptors
The conversation around AI validation has rapidly outgrown simple prompt engineering and single-turn model checks. While the industry spent the last few years establishing baseline protocols for individual AI agent testing, enterprise automation has already advanced to the next engineering frontier: the Multi-Agent System (MAS).
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Navigational Perception in Legal Information Environments

Navigational Perception in Legal Information Environments

Jun 19, 2026 By SystemsDigest In SystemsDigest
Legal digital environments operate within a unique informational context where clarity, trust, and accessibility must coexist with complexity. Unlike many commercial websites that focus primarily on transactions or engagement, legal platforms often serve as information systems that help users understand unfamiliar situations, evaluate options, and make important decisions. To support this process, legal environments rely on layered information architecture, where content is organized into interconnected informational nodes.
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The Hidden Cost of AI Testing: Stop Burning LLM Tokens in Your CI/CD Pipeline

Jun 18, 2026 By Perforce In Perforce
AI testing against live LLM APIs can quietly drive massive token costs across development, QA, and CI/CD pipelines. Every test execution consumes real tokens—at production rates—creating hidden, variable costs that scale with your AI adoption. In this video, discover how leading enterprises are eliminating LLM token spend using service virtualization. Learn how BlazeMeter intercepts API calls, simulates realistic AI responses (completions, embeddings, and large payloads), and enables full-scale testing without invoking live models.
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