February 22, 2026 AI, AI Agent, HowTo No comments
My 1-Hour Open Claw Setup: Docker, Llama 3.1, and Telegram
I saw all the fuss about Open Claw online and then spoke to a colleague and she was saying she is buying a Mac mini to run Open Claw locally. I could not resist the temptation to give it a try and see how far I can get. This post is just a quick documenting what I was able to do in like one hour of setup.
If you’re like me and find it difficult to follow all the latest AI hype and missed it, Open Claw is an open-source AI agent framework that connects large language models directly to your local machine, allowing them to execute commands and automate workflows right from your terminal or your phone.
A quick preview below. This is just nuts. In one hour I was able to run OpenClaw on Docker talking to llama3.1 running locally and communicating with this via Telegram bot from my phone 🤯.
Back in the old days I would open some kind of documentation and follow steps one by one and unquestionably get stuck somewhere. This time I started with Gemini chat prompting it to guide me through the installation and configuration process. This proved to be the best and quickest way.
Decision 1: Running locally or in some isolation.
I think this one is an obvious choice. Giving hallucinating LLMs permission to modify files on my primary laptop sounds like a recipe for disaster. Decided to go with Docker container but if I find the right workflows I might buy Mac mini as well.
Commands were fairly simple, something along these lines:
git clone https://github.com/openclaw/openclaw.git
cd openclaw
./docker-setup.sh
docker compose up -d openclaw-gateway
Decision 2: Local LLM or Connect to something (Gemini, OpenAI)
This is a more difficult decision to make. Even though I’m running an M4 with 32GB, I cannot run too large of a model. From reading online it is obvious that connecting to large LLMs has an advantage of not hallucinating and giving best results but at the same time you’ve got to share your info with it and run the risk of running into huge bills on token usage. Since this was purely for my self learning and I don’t yet have good workflows to run, I just decided to connect using a small model llama.3.1 running via Ollama. Since it was running on my local machine and not docker, I had to play a bit with configuration files but it worked just fine. And yeah, the answers I would get are really silly.
Later I found that ClawRouter is the best path forward. Basically you use a combination of locally run LLM and large LLMs you connect to. I might do this in the next iteration.
Decision 3: Skills, Tools, Workflows
This is just insane how many things are available. Because this can run any bash (yeah, in your telegram you can say “/bash rm x.files” – scary as hell) on the local the capabilities for automation with LLMs are almost limitless.
Conclusion
I can barely keep up with all of the innovations that are happening in the AI space but they are awesome and I’m inspired by the people who build them and feel like I want to vibe code so much more instead of spending my time filling-in my complex cross-border tax forms over the weekend.
Building Personal AI Agent
January 11, 2026 AI, AI Agent No comments
Rich people always had access to assistants (+chief of staff) that would help them with all kinds of chores, would advise them on things, or just do things behind the scenes. We live in an interesting time of AI, where anyone has access to these fabulous LLMs that can do some of those things for us. Like, I’m sure most of us are doing our travel plans with some help from LLMs, or we make buying decision, etc. It is just mind boggling how good these things are becoming!
In parallel, as software engineers, we keep hearing about AI agents all the time. We use AI agents at work. The most useful and prominent example of LLM agents are coding agents. You are likely using Claude Code at work. I already heavily rely on LLMs to track many of my personal goals, to critique me, to give suggestions, etc.
But what if I build a personal AI Agent to avoid repeating things and to make it watch me more proactively?
Vibe Coding AI Agent
There we go! Let’s build something simple first. My use cases for LLMs are fairly simple, nothing too crazy and very closely tied to my Life Goals and areas of life. For example, I have chats with Gemini labeled like “Nutrition”, “Finance”, “Career”, etc. When I eat my breakfast I snap a picture of it and estimate my nutrition intake. When I’m considering stock buying I do research with Gemini. When I plan a trip I build an itinerary with LLM, etc. I track my weekly progress in google docs. I track my finances in spreadsheets. The more I think about this the more I realize there is a room for a personal AI agent that is highly tuned to my personal needs and would orchestrate all of this. Additionally there won’t be any ready solution online, because this is so personal, so I’ve got to build one agent for myself!
Email Digest Agent after ~3 hours of Vibe Coding
So what I’ve built in 3 hours is a “personal AI agent that automatically generates daily digest emails by fetching data from multiple sources in parallel: it retrieves stock market insights for ~X tickers, extracts the current week’s goals (with checkbox tracking) from a tabbed Google Doc, pulls my monthly focus items, pulls net worth data from a Google Sheets dashboard, then uses Gemini to generate a professionally formatted HTML email summary and sends it via SendGrid. The system is built with LangGraph for workflow orchestration, uses OAuth2 for secure Google API access, and preserves formatting details like checkboxes (✅/⬜) by converting them to email-compatible emoji before direct insertion into the final email.”
Tech Stack
- LangGraph 0.2+ – Workflow orchestration with parallel execution
- LangChain 0.3+ & Gemini API (gemini-2.0-flash-exp) – LLM integration
- Google Docs/Sheets API – Data sources with OAuth2
- Alpha Vantage API
- SendGrid – Email delivery
- Python 3.9+ – Runtime
Conclusion
This project is just scratching the surface of what is possible to be built very quickly for personal needs. The most exciting realization for me wasn’t the technical implementation but how accessible it is to connect things together. Setting up all of the API keys and then vibe coding all together is so straight forward that it is just unbelievable.
I will continue this project next week to make it actually properly work for my needs and then will host it on some server to send me those digests. Next steps would be to supplement it with prompts to LLMs to give me quick ideas for what I should focus on, better tracking, etc so I can achieve my goals quicker. So exciting!
Go ahead and build something for yourself!
