In April I wrote about OpenClaw — my self-hosted AI agent on a Hostinger VPS, wired to Telegram, email, calendar, and GitHub. That post ended with a short "What's Next" list. Three months later, most of it shipped — and the setup feels less like a chatbot and more like a small ops team.
What's new since April:
- Blog posts — voice note in, researched draft out, published on my site
- Code from GitHub issues — agent plans, I approve, PR lands
- Invoices — bilingual PDFs for US and German clients, email draft ready to send
- SEO & compliance work — live data pulls and reports without me clicking through five tools
- One Telegram chat, many projects — separate topics so context doesn't mix
- Conference proposals — talk drafts from memory, not from a blank page
Below: what changed, what broke, and what I'd do again.
Quick recap
OpenClaw is a persistent assistant with memory. I talk to it on Telegram — often by voice. It can run tasks, read my repos, draft email, and check GitHub. It only sends mail after I say yes.
Everything below builds on that.
1. Blog posts — from idea to publish
The April post promised blog drafting. That happened — repeatedly:
| Post | What the agent did |
|---|---|
| Hetzner EU production series | Researched the stack, drafted EN+DE, coordinated images |
| Lovable → Hetzner DACH | Same pattern, tied to a real client story |
| SISTRIX MCP SEO audit | Pulled live SEO data, read the client's site code, exported a strategy PDF |
Typical flow: voice note → agent gathers context from past notes and repos → markdown draft → I edit for tone and accuracy → publish.
Lesson: Great at first drafts with real data. My job is "would I post this under my name?"
2. GitHub issues → plans → PRs
The biggest upgrade: OpenClaw can hand off coding work to Cursor in the actual repo.
Today:
- New issue on a client's product repo
- I get a Telegram ping
- Agent writes an implementation plan from the codebase
- Plan lands as a GitHub comment — visible to the team, not buried in chat
- I approve → code gets written → PR opens
I stopped burning the expensive model on every small check. Quick triage stays cheap; planning and coding use the heavy models only when it matters.
Lesson: Keep planning in the repo and in GitHub. Telegram is for pings and decisions, not for hiding the plan.
3. Invoices — the boring work I avoided
Invoice day used to mean copy-paste HTML, hunt for rates, second-guess tax wording, export PDF, upload, draft the cover email. Now I open the right Telegram topic and say prepare the next invoice.
The agent already knows each client's rules — US vs German format, currency, VAT, layout. It pulls the last invoice as a template, bumps the number, fills in the period and line items, and asks me only what's missing. Output: PDF in Dropbox, email draft waiting — send only after I approve.
Lesson: Highest ROI automation I didn't mention in April. Repetitive, rule-heavy, easy to get wrong. Perfect for an agent with memory.
4. SEO, WordPress, compliance — one prompt, one artifact
April: basic memory and a few tools. July: the agent can talk to external services I use every week — SEO data, a client's WordPress blog, security scan reports.
The pattern is always the same: one Telegram message, agent combines live data + code context, I get a dated deliverable (markdown, PDF, or issue comment). I wrote up the SEO workflow here.
Lesson: One integration per job you do manually more than once a month. Don't wire everything on day one.
5. Telegram topics = project switcher
One group chat, many topics — blog, US client, German client, side projects.
Each topic loads the right context: which repo, which tone, which rules. I don't mix invoice talk with blog drafting in one thread.
Lesson: If your bot goes quiet in a group, check you're on a recent OpenClaw build. Forum topics had a reply bug that's fixed now.
6. Conference talk drafts
Between client work I still submit to AWS Community Days, AgentCon, CodeMotion, re:Invent. The agent keeps proposal drafts in a folder and knows the submission format.
Typical flow: draft a talk on security scanning for Poland Community Day → agent pulls from past project work and prior talks → title, abstract, outline ready to paste into the submission form → calendar reminder for the deadline.
Lesson: CFP writing is research + structure + your voice. The agent does the first two; I keep the third.
Numbers (rough, three months in)
- GitHub: dozens of issues triaged; several fixes shipped via agent → Cursor → PR
- Blog: 4+ substantial posts with agent help (EN+DE pairs)
- Invoices: 5+ US sprint invoices + German client invoices — PDF + email draft each time
- CFP: 10+ proposal drafts across Poland, DACH, Milan, re:Invent
- Cost: picking the right model for the task matters more than VPS hosting
- Failures: one email sent too early → hard "ask before send" rule; morning briefing broke once after an update — fixed with a version bump and a cheaper scheduled job
What's next
- Auto-implement after explicit GitHub approval
- Infra changes from chat (Hetzner work in progress)
- Quarterly VAT prep from the invoice folder — agent-assisted, accountant still reviews
Conclusion
Three months ago OpenClaw was email, calendar, and GitHub pings. Now it's an orchestrator: topics for context, Cursor for code, live integrations for data, the blog for content, invoices and talk drafts for the business side.
Still on €10/mo Hostinger Docker.
If you already run OpenClaw, what helped me most: hand off coding to Cursor, one Telegram topic per client/project, and one external integration per repetitive manual job.
Links:
Questions or want help wiring your agent? office@martinmueller.dev or calendly.com/martinmueller_dev.
