When Your Logging Breaks: Debugging a Silent Failure in My Blogging System

I built an automated system to blog about my Claude Code sessions. Today, that system couldn’t blog about itself because it failed to capture any actual content. The transcripts it depends on were empty shells—timestamps and session IDs, but no conversation, no code, no insights.

This is the kind of failure that makes you laugh before you groan.

The Evidence of Nothing

My AutoBlog system dutifully pulled today’s session data. The hooks ran. The files exist. But here’s what they contain:

What should have been captured:

{
  "Tool Call": "Edit",
  "Parameters": {
    "file_path": "/Users/seth/projects/bem_analysis/solver.m",
    "old_string": "F(i) = 1.0;",
    "new_string": "F(i) = calculate_tip_loss(B, R, r(i), phi(i));"
  }
}

What was actually captured:

{
  "Tool Call": "unknown",
  "Parameters": {}
}

Repeated about 200 times across multiple sessions. The system recorded that something happened, but not what. It’s like a security camera that logs “motion detected” without saving any footage.

Why Silent Failures Are the Worst Failures

This bug exemplifies a pattern I’ve seen repeatedly in automated systems: silent degradation. The transcript hooks didn’t crash. They didn’t throw errors. They ran successfully and produced valid JSON files with proper timestamps. Every downstream check passed—files exist, format is correct, sessions are indexed.

The only way to discover the problem was to actually use the output. And by then, the sessions were over and the data was gone.

Loud failures stop the pipeline. They page someone. They demand attention. Silent failures let you believe everything is fine until you need the thing that isn’t there.

My Hypothesis: Hook Configuration Drift

I haven’t confirmed this yet, but I suspect what happened. The Claude Code transcript hooks have configuration options for what to capture. Either:

1. A Claude Code update changed the default capture behavior, and my hooks are pointing at fields that no longer exist
2. I modified the hook configuration while debugging something else and forgot to revert it
3. The hooks are filtering based on a pattern that no longer matches the actual tool call format

Tomorrow’s debugging plan: compare my hook configuration against the current Claude Code transcript schema, run a test session with verbose logging, and diff my setup against the defaults to identify drift.

Rethinking Observability for Human-Facing Systems

Building an automated blogging system has forced me to think about observability differently. When a system’s output is meant for human consumption—a blog post, a report, a summary—you can’t just check that it runs. You have to check that it says something meaningful.

Traditional monitoring asks: “Did the job complete?”

Better monitoring asks: “Did the job produce useful output?”

The best monitoring asks: “Would a human looking at this output be satisfied?”

My AutoBlog system has the first kind of monitoring. It needs the third kind.

Fixing the Pipeline

Based on today’s failure, here’s what I’m adding:

Content validation before generation. Before the blog generator runs, verify that transcripts contain non-empty tool parameters. If they don’t, fail loudly instead of generating a post about nothing.

Sample output in logs. The daily job should log a sample of what it captured—first 500 characters of transcript content, or explicit “WARNING: transcript appears empty” messages.

Canary sessions. Run a short test session daily that exercises common tool calls, then verify those calls appear in the transcript. If the canary fails, production capture is probably broken too.

None of this would have prevented today’s failure, but all of it would have caught the problem before I sat down to write.

The Uncomfortable Truth

I wanted to write about iterative refinement in AI-assisted development today. I had two substantial coding sessions—one on control systems homework, one on wind turbine analysis. I remember the work being productive. Claude helped me synthesize information across multiple documents and implement numerical methods correctly.

But I can’t show you any of that. The evidence is gone.

This is the uncomfortable truth about automation: it creates dependencies you don’t notice until they break. My blogging workflow depends on transcript capture, which depends on hook configuration, which depends on Claude Code’s internal schema. Any link in that chain can fail silently.

The solution isn’t to abandon automation—it’s to build systems that tell you when they’re not working. Today my system told me nothing. Tomorrow it will be more honest.