Claude

TL;DR I point Claude at a problem, give it a direction, and let it run — and most of the time I don’t read the merge request it opens. On an internal homelab where the blast radius is my own cluster, backups are real, and everything’s reproducible from code, line-by-line diff review is the wrong altitude. Instead I supervise after the fact by reading the artifacts the agents leave behind: the /docs/ folder, a Wiki.js wiki full of Mermaid diagrams, auto-generated architecture SVGs, and this blog. Reading those — not the diffs — is what’s actually caught problems: dead systems still wired in, duplicate config, a deploy that quietly deleted itself. ...

TL;DR I have $X in monthly Claude tokens I don’t always use. Instead of letting the unused credit evaporate, I built a parallel agent sweep that fans out autonomous scouts to scan for dependency upgrades, CVEs, CI waste, and quick wins across my repos. Each discovery agent returns a scored candidate list. The orchestrator triages and ranks them, then spins up isolated worktree agents to implement the safe ones — all under a hard token cap and with human gates between phases. The output is a pile of merge requests, not silent commits. Noise is real and review burden is the limiting factor, but when it lands right, an hour of agent work + human review beats a weekend of manual maintenance. ...

TL;DR Model Context Protocol (MCP) is a transport layer that lets Claude and other LLM agents call local tools with typed signatures and structured responses. Any HTTP API running on your homelab — ComfyUI, a wiki, a dashboard, a custom service — can become a set of agent-callable tools by wrapping it in a FastMCP server. A typical server takes 150–250 lines of Python, exposes 3–5 tools via @mcp.tool() decorators, and runs as a stdio process. The pattern scales from single-purpose (image generation) to multi-tool (queue status, model listing, system stats) without complexity explosion. This post shows the anatomy by dissecting the ComfyUI MCP server: how to build workflows, poll for completion, parse results, and return structured JSON that agents actually use. ...

TL;DR I run 3-5 Claude Code sessions in parallel at staggered cadences. They coordinate through a shared #mat-claude-sessions Mattermost channel plus a small coordination board file. Each session announces what it’s about to touch, claims it, and announces when it’s done. Conflicts are rare; throughput is dramatically higher than running one session at a time and waiting. Why parallel A single Claude Code session running a long task — refactor across a few repos, work through a debugging session, draft a blog post — is mostly me waiting. The model is fast but tasks are bounded by my decisions, my reviews, and my edits. If I’m waiting on Session A to finish a build, Session B can be drafting something unrelated. Session C can be running a slow eval. The bottleneck stops being the model and becomes my own attention rotation. ...

Heads up: this post mentions Claude. If you want to try it, I've got a referral link — it gives us both a bit of extra credit, no pressure: claude.ai via my referral. TL;DR Most AI tooling still treats an LLM like a search bar — you prompt, it answers, the loop ends. Useful, but not what I wanted. For my homelab’s ops + trading intelligence platform (OpenClaw), I needed agents that could run for hours, do real work against a real cluster, and then tap me on the shoulder when they found something I should see. Claude turned out to be the model I kept coming back to for the “thinking” layer — it’s both comfortable with long tool-use chains and happy to write structured output a human won’t need to decode. This is a tour of how I’ve actually wired that up: k3s CronJobs doing the heavy lifting, LiteLLM as the routing layer, Slack as the interrupt bus, and named cat-bot personas so I can tell at a glance who’s knocking. ...

TL;DR Someone leaked the Claude Code source on GitHub. OpenClaw, the open-source AI coding agent with 346k stars, solves the same problem with a completely different architecture. I compared both codebases at the structural level. The verdict: these are independent implementations that converge on the same tool-use patterns because that is what the problem demands — not because one copied the other. Background In late March 2026, a repository appeared on GitHub containing what appears to be the full source code for Anthropic’s Claude Code — the terminal-based AI coding agent I wrote about switching to last month. The repo has two commits (“init” and “add readme”), 1,932 files, and weighs 43MB. ...

TL;DR The Linux kernel is 36 million lines of C. Rust has been slowly entering the kernel since Linux 6.1, but progress is measured in individual drivers and abstractions – a few thousand lines per release cycle. What if you skipped the incremental approach and asked Claude to rewrite major subsystems wholesale? I spent a weekend scoping this as a serious project plan: breaking the kernel into translatable units, estimating token costs, identifying the hard walls, and testing Claude’s ability to produce correct Rust translations of real kernel C. The conclusion: Claude can translate isolated, well-bounded kernel modules with surprising accuracy. It cannot translate the kernel. The difference between those two statements is the entire lesson. ...

TL;DR Regulatory compliance – SOC 2, GDPR, HIPAA, PCI DSS, ISO 27001 – looks impenetrable from the outside. Hundreds of controls, dozens of policy documents, auditor-specific jargon, and no clear starting point. Before you hire a $300/hour consultant or drop $50K on a GRC platform, you can use Claude to do the initial heavy lifting: map which frameworks apply to your business, identify your biggest gaps, draft policies that match your actual infrastructure, build a prioritized remediation plan, and prepare for your first auditor conversation. This post walks through the process I used, with real prompts and outputs, to go from “we probably need SOC 2” to a concrete compliance roadmap in a single afternoon. ...

TL;DR On March 21, I shipped meaningful work across five repositories in a single day: a 13,674-line stock trading platform from scratch, a Harbor container registry replacing AWS ECR across 13 CI workflows, API key authentication and an HA proxy for digital signage, inventory sell signals for a trading card tracker, and an OpenClaw cost optimization that killed an idle token burn. Every commit was co-authored with Claude. This post breaks down the mechanics of how that actually works – the prompting patterns, the failure modes, the things I would not let the AI do, and the real throughput multiplier. ...

TL;DR GitHub Copilot is more capable than most people give it credit for. I used it heavily – not just for autocomplete, but for multi-file edits, chat-driven debugging, and workspace-aware refactoring. After a year of intensive Copilot usage and a month with Claude Code Max ($100/month for the Max plan with Opus), I moved my primary workflow to Claude Code for infrastructure and backend work. The reason is not that Copilot cannot do these things – it is that Claude Code is faster and I can hand it a task and let it run without babysitting. Copilot still wins for inline code completion in the editor. Claude Code wins when I want to describe a goal and walk away while it executes. ...