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speedscale

The Pod Was Cheaper. The Service Wasn't.

Aug 11, 2026 By Matt LeRay In Speedscale
A smaller Kubernetes pod can lower allocation cost while completing less work. Green status codes and matching schemas can hide it. This walkthrough combines OpenCost allocation data with proxymock behavior and performance evidence. A candidate passes only when behavior and throughput hold while unit cost falls.
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statusgator

Monitor outages with StatusGator MCP and Claude

Aug 11, 2026 By Colin Bartlett In StatusGator
When a service your organization depends on stops working, you need to know whether the problem is internal or caused by a third-party provider. Connecting StatusGator to Claude gives you a faster way to find out. You can ask Claude what is down, investigate provider incidents, review affected components, and analyze historical uptime using data from your StatusGator account.
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speedscale

Flamegraphs Find It. Replay Proves It.

Aug 11, 2026 By Matt LeRay In Speedscale
I made an API endpoint 13 times faster. Then I realized my first verification only checked the status, headers, and response schema. I had not checked the totals. I had made the bug faster. That is the problem with giving an AI coding agent one kind of evidence. A CPU profile can show where the application is slow, but not whether an optimization preserves behavior. A traffic replay can prove that behavior stayed stable, but not explain why the code burns CPU. This walkthrough gives the agent two independent witnesses: Together, they turn AI code verification into an experiment with two independent checks.
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AI SRE Agent Debugs a Lambda Timeout with the AWS MCP Server: AURA

Aug 11, 2026 By Mezmo In Mezmo
A scheduled Lambda quietly stops completing and nothing pages you. AURA finds the function, reads its logs, and comes back with a three-second timeout. The usual path is opening the console, tracking down the right log group, and reading CloudWatch by hand. Here AURA connects to AWS through the MCP proxy AWS publishes, run locally with uvx against an AWS CLI that is already configured, so there are no new credentials to issue.
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upsun

Don't build the autonomous AI factory first

Aug 11, 2026 By Upsun In Upsun
Here's a scene playing out in engineering teams right now. An engineer spends the weekend running four or five coding agents in parallel. Monday morning, a teammate opens their laptop to 53 changed files with 2000+ diffs and a message that says, more or less, "should be good to merge." Nobody asked for this much output. Nobody has time to review it properly. The team doesn't feel faster. It feels ambushed.
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elmahio

Solving bugs with elmah.io and Claude Code - a real-life example

Aug 11, 2026 By Thomas Ardal In elmah.io
I spend most of my day in Claude Code these days. Most of my development processes changed after having access to my own personal assistant. In this post, I'll show you a real-life example of how bug fixes are often done on elmah.io now. I hope it will inspire someone to optimize their workflow and get even more out of their elmah.io subscription.
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circleci

Starting your engineering career in the AI era: 6 takeaways for junior developers

Aug 11, 2026 By Jacob Schmitt In CircleCI
“We don’t need junior engineers anymore” has become one of those lines people repeat because it sounds obvious. The AI writes the code, so why pay someone to learn how to write it? On the latest Confident Commit podcast, Rob Zuber makes the case that this take is exactly backwards.
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circleci

AGENTS.md vs. skills: How to steer a coding agent

Aug 11, 2026 By Jacob Schmitt In CircleCI
Every team adopting coding agents hits the same question early: where do you put the instructions that tell the agent how your codebase actually works? Two answers dominate the conversation right now. One is AGENTS.md, a plain markdown file at the root of your repo. The other is skills, packaged instruction sets an agent loads on demand. Most of the debate treats this as a formatting decision. It isn’t.
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cloudzero

LLM cost management: a practical guide for teams that own the budget

Aug 11, 2026 By Lyne Carolyne In CloudZero
LLM cost management is the practice of tracking, allocating, budgeting, and governing large language model spend so every dollar maps to a feature, team, and business outcome. It has five levels: provider visibility, business allocation, unit economics, model governance, and a continuous optimization loop. It matters because 68% of companies say AI initiatives ran over budget last year, and per CloudZero's 2026 survey, 30% of finance leaders still reconcile AI spend manually.
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Cloud 66

What the Platform Team Actually Does When Everyone is an AI-Assisted Builder

Aug 11, 2026 By Kelley Schultz In Cloud 66
An AI model can write a fully functioning microservice in about fifteen seconds. If you hook it up to a pull request pipeline, it can generate migrations, write unit tests, and suggest refactors before your lead engineer has finished their first cup of coffee. We are entering an era of unprecedented code velocity. But code is not an application, and shipping is not operating.
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