AI deployment gets more useful when you can explain what happens after the first successful request.
I'm Gavin Alden, the writer behind Logs in the Sky. I write about building, deploying, and operating AI applications, with Beam as the main platform context. My focus is the work around the model: the runtime, the handoffs, the waiting, the bill, and the evidence you need when something stops making sense.
This site is for developers who know their way around an API but do not want every small AI project to become a second job in infrastructure.
Start with the workload. Follow the evidence.
A larger GPU might solve a memory problem and leave a slow request almost unchanged. A healthy container might return an unusable answer. An agent might receive approval for an action without knowing whether the external system completed it.
Those distinctions shape the questions I explore here. What are we trying to accomplish? What can the available evidence establish? What is the smallest change that can test the explanation?
I care about the tradeoff as much as the configuration. A useful guide should explain when to keep a simple approach, when to change it, and what the new choice will cost in money or maintenance.
What you'll find here
Beam in Production: runtime configuration, service boundaries, deployment checks, observability, and cost reviews.
GPU Without the Guesswork: workload-based GPU selection, cold starts, inference performance, and local-versus-cloud decisions.
The Debugging Log: diagnostic methods for separating model behavior from environment, container, and infrastructure problems.
Agent Workshop: tool execution, retries, timeouts, human review, and the state that connects a workflow.
Gavin's Workbench: smaller experiments, deployment notebook methods, and opinions about what is worth building next.
You can start with a specific error or a broader architecture question. The aim is the same: leave with a clearer next step and a way to check whether it helped.
How to read the examples
I distinguish documented platform behavior, engineering judgment, illustrative calculations, and supplied firsthand records. A worked example is not a benchmark. A locally checked code path is not proof that a cloud deployment will behave the same way.
When an article includes measurements, look for the setup, workload, versions, and limitations. When it uses assumed numbers, those assumptions belong beside the calculation. Illustrative images explain a setting or decision; they do not establish that a test or incident happened.
Platform behavior, pricing, and dependencies change. For a deployment decision, check the article's scope and the current official documentation, then validate the configuration in your own environment.
Help make the next explanation better
If a step is unclear, a source has changed, or a result does not match the explanation, I welcome a specific correction. The article title, affected passage, relevant version, and a sanitized example are useful places to start.
The Contact page has guidance for corrections, topic suggestions, and collaboration inquiries. Please keep credentials and private customer information out of your message.
No panic, just logs.