Systems | Development | Analytics | API | Testing

ably

Vercel AI SDK in production: when DefaultChatTransport needs a session layer

Jun 22, 2026 By Madeleine Quinn In Ably
You've built an AI chat app on the Vercel AI SDK. It works in development. The model responds, the stream comes through, and the UI updates cleanly. Then you ship to production, and the transport layer starts showing its edges. Most of these failures are quiet: things that work in demos and break in ways that are hard to pin down until you know where to look. They share a common cause: DefaultChatTransport is built for HTTP, and HTTP has structural properties that some production requirements exceed.
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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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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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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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Streaming highlights from Databricks Data + AI Summit

Jun 18, 2026 By LensesIO In Lenses
Join Tun Shwe and Jeremy Frenay as they stream live from the floor of the Databricks Data + AI Summit! They’ll break down the biggest announcements, key takeaways, and cutting-edge trends shaping the intersection of AI and data streaming. Register to get an insider look at the future of data AI streaming.
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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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keboola

Guessing AI vs. Verifiable AI: Why the Difference Matters in Finance

Jun 18, 2026 By K In Keboola
I asked Claude what the cash position would be at year-end. The answer was about 30% off. A CFO said this at a finance leaders breakfast in Prague. Almost every CFO in the room had a version of the same story. The problem is not the model. Claude is not bad at maths. The problem is what the model was reasoning over - raw financial data with no governed definitions, no intercompany rules, no agreed methodology for what 'cash position' means at that specific company.
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appsignal

Vibe Coding Economically: Which Framework Is the Cheapest? (Rails vs Django vs Laravel)

Jun 17, 2026 By Tarun Singh In AppSignal
Token costs used to be something most developers ignored. They simply dismissed them as theoretical. Now, they’re showing up in your Cursor/Claude Code bill, in every pasted error, in that package the AI pulled in without asking, or in that clarification round you didn’t plan for. Most developers choose a framework based on what they've used before, what the job description asks for, or simply whatever was used on their last project.
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Ep 78 | Mastering Enterprise AI: Why Some Projects Succeed While Others Fail

Jun 17, 2026 By Cloudera, Inc. In Cloudera
AI may be the most capable intern your organization has ever hired. However, interns still need guidance and clear direction. Enterprise AI is proving no different. In this episode of The AI Forecast, Paul Muller sits down with Michael Gray, CTO of Thrive, to explore the patterns and anti-patterns emerging from real-world enterprise AI deployments. Drawing on his experience helping organizations implement AI at scale, Michael offers a practical framework for evaluating AI maturity, helping leaders understand where adoption breaks down and what it takes to build momentum across the organization.
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