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DZone.com Feed Recent posts on DZone.com Frame Buffer Hashing for Visual Regression on Embedded Devices Jun 9th 2026, 15:00 by Rajasekhar sunkara I run test automation for a graphics team that ships software to streaming devices. About a year ago, we changed how our visual regression suite stores and compares its references. The old approach kept around 18GB of PNG golden images in the test repo and ran a pixel-by-pixel diff on every comparison. The new approach stores around 19KB of MD5 hashes in a JSON file and compares hash strings. Storage dropped by roughly three orders of magnitude. Comparisons became effectively free. A category of flaky tests stopped being flaky. This article is about how that works, when it makes sense, and when it doesn't. It also covers the parts that surprised me, because the approach has real downsides and I want to be honest about them up front. ‍ Amazon Quick: AWS's Agentic Workspace, Explained for Engineers Jun 9th 2026, 14:30 by Jubin Abhishek Soni AWS has been building agentic infrastructure for some time now — Bedrock, AgentCore, Strands — mostly aimed at engineers who want to build their own agent systems from scratch. Amazon Quick is a different layer of the same bet: a ready-to-use agentic workspace that targets teams directly, without requiring custom orchestration code. This article walks through what Quick is, how its components fit together technically, how the MCP integration model works with real code, and where it sits relative to the rest of AWS's agent stack. ‍ Detecting Plan Regression in SQL Server Using Query Store Jun 9th 2026, 14:00 by Deepesh Dhake Weighted Baseline Regression Pattern for Query Store Plan regression in SQL Server usually shows up quietly. A query that has been stable for a long time suddenly becomes slower, even though there were no deployments, schema changes, or obvious infrastructure problems. In many cases, the issue is tied to an execution plan change caused by statistics updates, parameter sensitivity, changing data distribution, or normal optimizer behavior. Problem A common starting point is comparing "yesterday vs today" using average duration. While quick, this approach often does not reflect the full behavior of the workload ‍ Token Attribution Framework for Agentic AI in CI/CD Jun 9th 2026, 13:00 by Intiaz Shaik The Silent Killer No One Mentions Until the Bill Comes Most papers about "agentic AI in production" stop where the problem starts: price. Interacting with Claude or GPT requires a natural pace setter: you, reading the generated text. Take the same agent out of chat mode and drop it into CI/CD, nightly batch, webhook handling; the pacing goes away, and you're running the thing purely on computer time, at computer prices. The numbers look even scarier when you dig deeper. ReAct-style looped execution prepends all outputs to the following prompts, meaning you consume tokens at roughly O(n²). A PR review agent in a loop of three steps, which costs $0.04 in local development, can rack up a charge of $0.40+ once it gets stuck in its looping process. With hundreds of PRs a week, that could easily amount to five-figure surprises in your bill. ‍ Engineering Agentic AI for Production: A Distributed Systems Perspective Jun 9th 2026, 12:00 by Rajesh Kumar Pandey Agentic AI demos are everywhere. Production agentic AI is rare. The gap between the two is not a model problem; it is an engineering problem, and it is one that distributed systems engineers are uniquely positioned to solve. The instinct in most teams is to treat the LLM as the hard part. Fine-tune it, prompt it carefully, pick the right model size, and ship. But once an agent is running in a loop (planning, calling tools, observing results, replanning), the model becomes the least of your problems. What breaks production agentic systems is everything around the model: how context is managed across turns, how failures propagate through multi-step plans, how you observe what went wrong three tool calls deep, and how you prevent a single bad plan from doing irreversible damage. These are distributed systems problems wearing an AI costume. ‍ ‍ You are receiving this email because you subscribed to this feed at blogtrottr.com. By using Blogtrottr, you agree to our terms. If you no longer wish to receive these emails, you can unsubscribe from this feed, edit this subscription, or manage all your subscriptions.