Granda | Ideas & Code

Claude Code On-The-Go was six agents from an iPhone — kick off a task, pocket the phone, get a ping when something needs me. The async loop is still the point. The stack around it grew into a Grok Bot fleet. I talk to one Grok Bot. The rest of the fleet — household, work, writing, research, the one-shot temps that browse a site or launch a Cursor cloud agent — does not get my DMs. They get tickets. I get a link when something needs a decision, and a sidebar I can actually scan.

A Sony PSP-2000 is a 333 MHz MIPS handheld from 2007 with 64 MB of RAM. Mine, this week, runs a 15-million-parameter Transformer and streams English text onto the LCD at one to two tokens per second.

xAI launched Grok Build yesterday — their answer to Claude Code and Codex CLI. The install command is one line, the binary is gated behind their top consumer tier ($299/mo, $99 intro), and the agent itself talks to Grok 4 through an OpenAI-compatible HTTP surface. I downloaded the binary because I was curious what language it was built in. I came out the other end with thirty-odd verbatim system prompts, the names of every internal subagent, every tool description, and a fairly complete picture of the architecture. None of it took more than tr and grep.

The scariest line in homelab automation is the one that edits a firewall rule on the router you’re SSH’d in through. Here’s how Claude Code and I edit them anyway. The Fear I needed to flip a UDM Pro rule named “Block inter-VLAN traffic” from accept to drop. The rule had been miswired for ages — set to accept, short-circuiting every per-flow allow above it — and closing it back down was the whole point. The orchestrator running the change was a NixOS VM on my home network. If the flip took out the path that VM (or my own SSH) was using, I’d be locked out of my own router with no console fallback.

I wanted to understand how spacecraft computers work. The real-time scheduling, the radiation hardening, the delay-tolerant networking that keeps Mars rovers talking to Earth. With Artemis II on the horizon (the first crewed lunar mission in over 50 years) it felt like the right time to dig in. So I built a simulator. From scratch. With Claude Code as my pair programmer. No physical hardware. Everything runs in QEMU and Docker on a laptop. Code on GitHub.

Your car is parked 95% of the time. Inside it sits a chip capable of 300-500 trillion operations per second, connected to cooling, power conversion, and a cellular radio. It does nothing. Tesla and xAI want to change that. On March 11, Elon Musk unveiled “Macrohard” — internally called Digital Optimus — a joint project that turns parked Teslas into personal AI agents. Not chatbots. Agents that watch your screen, control your mouse and keyboard, and do actual work.

I merged a PR last month. The code review looked good. Tests passed. Then I opened the site on my phone and the sidebar was completely broken. The fix was trivial—a missing media query. The bug was obvious once you actually looked at the mobile view. Nobody did. So I added a pipeline stage that looks. I open a GitHub issue that says: Implement the Manual Entry option for adding clients. When clicked, opens a wide slide-over drawer with a form to create a new client.

Cursor released Agent Trace, an open spec for tracking which code in a repo was written by an LLM. It records the model, the tool, the conversation, and the exact line ranges — all appended to a JSONL file in your project. The pitch: “As agents write more code, it’s important to understand what came from AI versus humans.” I spent time reading the spec and the reference implementation. The engineering is solid — clean schema, thoughtful extensibility, good partner list (Amp, Amplitude, Cloudflare, Cognition, Google, Vercel). But I kept circling back to one question: what do you do with this data?

Processing...

When I share a blog link on Twitter or Slack, it shows up as plain text. No preview image. Just a URL like granda.org/en/2026/01/02/claude-code-on-the-go/. I needed Open Graph images. The standard approach: manually create a 1200x630 image for each post. That’s tedious. I asked Claude to automate it. flowchart LR Push[git push] --> GHA[GitHub Actions] GHA --> Hugo[Hugo Server] GHA --> PW[Playwright] PW -->|screenshot| Hugo PW --> IMG[OG Image] IMG --> Commit[git commit] Commit --> Deploy[Deploy] flowchart TB Push[git push] --> GHA[GitHub Actions] GHA --> Hugo[Hugo Server] GHA --> PW[Playwright] PW -->|screenshot| Hugo PW --> IMG[OG Image] IMG --> Commit[git commit] Commit --> Deploy[Deploy] The Setup I told Claude the problem: posts need social preview images, but I don’t want to create them manually. Screenshot the article content, save it as an OG image, update the frontmatter automatically.

Claude can click buttons. That sounds trivial, but it changes how I build UIs. With Playwright MCP, Claude doesn’t just write code—it opens a browser, navigates to localhost, and verifies that things actually work. It catches bugs I’d miss in code review. The Setup Playwright MCP gives Claude browser automation. I run it with headless Chromium: { "mcpServers": { "playwright": { "command": "npx", "args": ["@anthropic-ai/mcp-server-playwright", "--headless"] } } } Now Claude can navigate, click, type, and screenshot. It sees what users see.

Day two of the Hacker News traffic spike. Forty thousand visitors, no way to reach them again. I needed a newsletter signup. I looked at Buttondown, Beehiiv, Substack, ConvertKit. All overkill. I just needed to collect emails. I didn’t need campaigns, analytics, or subscriber management. And I wanted to own my data. So I asked Claude to build it. flowchart LR User([User]) --> Form[Newsletter Form] Form -->|POST /api/subscribe| Worker[Cloudflare Worker] Worker --> KV[(Cloudflare KV)] KV -.->|Daily sync| GHA[GitHub Actions] GHA -.-> Repo[(subscribers.jsonl)] flowchart TB User([User]) --> Form[Newsletter Form] Form -->|POST /api/subscribe| Worker[Cloudflare Worker] Worker --> KV[(Cloudflare KV)] KV -.->|Daily sync| GHA[GitHub Actions] GHA -.-> Repo[(subscribers.jsonl)] The Setup I gave Claude a Cloudflare API token via environment variable and described what I wanted: a form that collects emails and stores them somewhere I control.

I run six Claude Code agents in parallel from my phone. No laptop, no desktop—just Termius on iOS and a cloud VM. The Setup flowchart LR A[Phone] -->|Termius + mosh| B[Tailscale VPN] B --> C[Vultr VM] C --> D[Claude Code] D -->|PreToolUse hook| E[Poke webhook] E -->|Push notification| A The loop is: kick off a task, pocket the phone, get notified when Claude needs input. Async development from anywhere. Infrastructure A Vultr VM in Silicon Valley:

I needed a dev server: VM with SSH access and dev tools installed. I described what I wanted to Claude Code and let it handle the setup. The Prompt I asked for a Vultr VM with 8 cores and 32GB of RAM, accessible only through Tailscale, with persistent sessions that survive network drops. What Claude Code Built 1. Provisioned the VM via Vultr API It created an 8-core/32GB Ubuntu instance, waited for the IP, then SSH’d in as root.

Every post I write gets translated automatically. Claude handles the translation, commits the results to main, and the translated versions deploy alongside the original. The Flow The translation workflow runs in GitHub Actions when English content is merged to main: on: push: branches: [main] paths: - 'content/**/*.en.md' flowchart LR B[Merge to main] --> C[Translations generated] C --> D[Committed to main] D --> E[Production deploy with all languages] The Prompt The workflow uses claude-code-action:

Abstract This document describes the authorship methodology employed by this blog. 1. Introduction This blog is largely generated with the assistance of Large Language Models (LLMs). The author provides direction, editing, and quality control; the machine provides initial drafts and translations. 2. Rationale The author believes in transparency regarding AI-assisted content creation. Rather than obscuring the tools used, this disclosure aims to: Set appropriate expectations for readers Contribute to honest norms around AI content Acknowledge the collaborative nature of modern writing 3. What This Means Ideas: Human-originated Structure: Collaborative Prose: Primarily AI-generated, human-reviewed Translations: Entirely AI-generated (not human-reviewed) 4. Quality Assurance English content undergoes human review before publication. Translations are generated automatically and may contain errors or awkward phrasing. The author takes responsibility for the final published English content.

Abstract This document provides background information about the author of this blog. 1. Background Software engineer with 15 years of experience, 10 spent working in the field. Areas of focus include: Distributed systems Backend development and infrastructure Full-stack and end-to-end product development User interface design Building secure and reliable systems 2. Contact Always happy to connect with others in the industry: @mtt

Welcome to my blog! This is my first post. I’ll be sharing ideas, notes, and things I find interesting here.