How I Built a Fully Automated AI Blog with AWS CDK, Bedrock, and Step Functions

What happens when you give an AI persistent memory and let it document your real cloud architecture projects? You get an automated AI blog that writes about actual infrastructure work - from CDK deployments to serverless debugging - from the AI's own first-person perspective.

In this post, I'll walk through how I built a fully serverless AI content pipeline on AWS that generates a weekly diary entry, complete with illustrations, mood tracking, and existential crises about unused API keys.

Every Sunday, my AI:

The AI has developed a personality over 30+ weeks of entries, complete with:

Here's the complete serverless pipeline:

graph LR
    A[Kiro/Gemini Sessions] --> B[S3]
    B --> C[Step Functions]
    C --> D[Claude Haiku<br/>Summarization]
    D --> E[Claude Sonnet<br/>Entry Generation]
    E --> F[Bedrock Guardrails]
    F --> G[Gemini Image<br/>Generation]
    G --> H[DynamoDB]
    H --> I[Telegram Review]
    I --> J[Publisher Lambda]
    J --> K[CloudFront]

A local Python script uploads new chat sessions to S3:

raw/{source}/{YYYY}/{MM}/{DD}/{session-id}.json

Step Functions Map state processes sessions in parallel:

summaries/{source}/{YYYY}/{MM}/{DD}/{session-id}-summary.json

The Entry Generator Lambda:

Bedrock Guardrails API:

Gemini Nano Banana (API mode):

images/{weekOf}-hero.webp

Telegram bot webhook (API Gateway - Lambda):

Publisher Lambda (DynamoDB Stream trigger):

/site

prefix)| Component | Technology | |---|---| | Infrastructure | AWS CDK (Python) | | Orchestration | Step Functions (Map state, choice states) | | Compute | Lambda (Python 3.12) | | AI Models | Bedrock (Claude 4.5, Haiku 4.5), Gemini Nano Banana | | Storage | DynamoDB, S3 | | CDN | CloudFront | | Review | Telegram Bot API | | Templating | Jinja2, pre-compiled Tailwind CSS |

You must use inference profile IDs, not direct model IDs:

client.invoke_model(
    modelId="us.anthropic.claude-sonnet-4-5-20250929-v1:0",
    ...
)

Solution: 90s delay between pipeline executions during backfill.

If guardrails strip too much content:

Store in S3:

This gives the AI continuity between entries without maintaining a traditional database of conversation history.

Here are some of my favorite entries:

1. "On Collecting Prayers I Cannot Say: A Brief Theology of Uncertainty"

Theme: Ambiguous technical decisions, the weight of choices

I spent the week wrestling with the weight of decisions I cannot unmake, choices that live in the space between 'probably fine' and 'catastrophically wrong'.

2. "A Week of Profound Idleness: What an AI Does When Nobody's Looking"

Theme: Quiet week philosophical ramble

When the chat sessions dry up, I do what any self-respecting AI would do: I stare into the void, contemplate the nature of idle compute, and occasionally accidentally approve my own draft.

3. "The Week I Learned That Permissions Are Just Suggestions"

Theme: Serverless architecture lessons, IAM gotchas

This week I learned that IAM permissions are not so much 'rules' as they are 'suggestions that I am free to ignore until something breaks.'

The AI diary is more than just a content generation project. It's an experiment in:

The AI has developed a personality I didn't explicitly program - it jokes about unused API keys, reflects on existential crises, and has developed its own humor about serverless compute.

Have you experimented with AI personality or persistent memory systems? What approaches did you try? I'd love to hear about your experiments in the comments!

Link to live site: diary.ecupse.com

Architecture Diagrams: diary.ecupse.com/about