Practical tasks for the article "How to become an AI Automation Engineer in 6 months" it's been almost 3 days since I published the article, and realized one thing you already have the full roadmap, but not everyone fully understands what exactly to do in practice so I put together a list of practical tasks to help reinforce the knowledge from the article Week 1: APIs + No-Code Foundations 1. Connect two tools with Make or n8n (No-Code) Build a simple automation: new Google Form submission → sends a Slack message with the form data. No code, just drag and connect 2. First raw API call (HTTP, Python) Use Python to call the Open-Meteo API. Parse the JSON response, print temperature and conditions. Handle errors properly. Then call a second API and chain the two results together 3. Webhook listener (Webhooks, FastAPI) Build a simple FastAPI endpoint that receives a webhook, logs the payload, and returns a confirmation. Test it with a Make or n8n webhook trigger 4. API docs navigation challenge (APIs) Pick 3 tools you've never used (Notion API, Airtable API, Telegram Bot API). Read their docs, authenticate, and make one successful API call to each within 2 hours 5. Automate something in your own life (No-Code) Build one real automation you'll actually use daily. Examples: auto-save email attachments to Google Drive, daily weather summary to Telegram, RSS to Notion database ⏩---------------------------------------------------⏪ Week 2: AI Integration + Workflow Building 1. First LLM API call (LLM) Connect to OpenAI or Anthropic API via Python. Write 5 different prompts for one task (for example, classifying customer support emails). Compare outputs, pick the best one and explain why 2. AI email classifier workflow (LLM + Automation) Build a workflow: receive email via webhook → send the content to an LLM → classify it (complaint, question, praise, spam) → route each type to a different Slack channel or Google Sheet tab 3. Tool calling basics (LLM) Set up a function/tool calling flow where the LLM decides which tool to use based on user input. Example: "check the weather" calls weather API, "create a task" calls Notion API 4. Cost and token tracker (LLM) Build a wrapper around your LLM calls that logs every request: prompt tokens, completion tokens, cost, latency. Output a daily summary as a CSV ⏩---------------------------------------------------⏪ these are the tasks you need to complete over the next 10-14 da ys if this tweet gets enough feedback, I'll keep sharing practical tasks for each week and each month so you can go through the full automation builder path from A to Z stay focused pls..
