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What if AWS infrastructure could detect problems before they become incidents?
I recently built an AI-powered AWS SRE automation system designed to reduce repetitive infrastructure operations.
The system:-
- Monitors EC2 CPU patterns using CloudWatch
- Uses AWS Bedrock to analyze infrastructure patterns
- Generates scaling recommendations
- Sends recommendations for human approval
- Automates the approved EC2 resize workflow
- Generates automated vulnerability insights using AWS Inspector
The goal wasn't to replace engineers it was to reduce repetitive investigation and help engineers make infrastructure decisions faster.
This is the kind of AWS automation I enjoy building: practical systems that reduce operational overhead while keeping humans in control
The network for creativity
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Designed and built the core product experience for Beauto, an AI-powered automation tool for small businesses.
What I did:
AI Assistant — chat interface where users describe a task and the AI builds the automation, with proactive, impact-ranked suggestions
Integrations hub — searchable library of one-click connections (Calendar, Stripe, HubSpot, Slack, etc.)
Analytics dashboard — automations run, hours saved, and conversion rate at a glance
Result: A chat-first flow that felt far less intimidating than traditional automation builders in user testing, driving most new automations created.
So creative 👏
SRE Automation & AI Chatbot Platform
Enterprise-grade resource optimization + AI-powered log analysis
Automated EC2 scaling with predictive analytics, intelligent approval workflows, and Bedrock-powered log intelligence — built to cut cloud waste and manual toil.
What It Solves
Forecasts resource needs from 24h metrics using predictive analysis, so scaling happens before problems hit
AI-powered log analysis (AWS Bedrock / Claude) — ask plain-English questions, get answers from your logs
Approval workflows for governance — no unsupervised changes to production
Safe automatic scaling inside maintenance windows, with real-time SNS notifications
Complete audit trails in DynamoDB for compliance
How It's Built
Terraform-managed infrastructure with S3 backend + DynamoDB state locking
GitLab CI/CD pipeline: validate → plan → apply with manual approval gates
SRE Agent Lambda monitoring metrics hourly, with predictive forecasting
Approval via Parameter Store, execution in scheduled maintenance windows
AI chatbot pulling from CloudWatch & S3, with response caching to control cost
Impact
Turns reactive, manual scaling into predictive, governed automation
Makes log analysis self-service — engineers ask questions instead of grepping logs
Every action auditable, every change approved — production-safe by design
Can a fully automated, faceless YouTube channel actually run for free?
That question is basically what turned into crank, a terminal AI agent I've been building for myself. It's turned into a full video production system: it writes, renders, voices, captions, and publishes, start to finish, with no editing on my end.
It's currently running from production to upload at:
https://youtube.com/@ecnivs
Not public yet, but it's coming
