I recently set up an old Dell PC running Ubuntu server as my homelab. It threw up the usual networking and permissions issues, and I thought an AI coding agent would massively speed up the debugging. I’ll walk through one task as an example (setting up a Samba share for Time Machine backups), which ended up highlighting the difference in utility between the agents pretty starkly.
Claude Code is £18/month. For hobby use, that felt expensive. I assumed Gemini would work just as well for less money. I had access via a Google AI trial, so why not try it?
Spoiler: I was wrong on multiple levels.
Attempt 1: Gemini CLI works fine… for simple tasks#
To be fair, raw Gemini CLI actually works well for straightforward conversions. When I needed to convert my LaTeX CV to Typst, it handled it without issues.
I thought: “If it can handle that, homelab debugging should be fine too.”
The real test came when I tried to set up a Samba share for Time Machine backups. This felt like a fairly easy, well-scoped task; find a Docker image that meets two requirements:
- Modern Samba version (more efficient streaming for virtual APFS)
- Configurable Docker image (to build a runtipi app later)
and then sort out any networking or permissions issues. Gemini found two Docker images:
- dperson/samba (old version)
- crazy-max/docker-samba (new, but hard to configure)
I explicitly told it: “I need modern Samba.” This clearly rules out dperson.
It kept suggesting dperson anyway. Over and over. Completely ignored my constraint.
It also kept getting stuck in infinite loops, where I had to intervene.
Attempt 2: Adding a harness didn’t help#
I thought that this was because the Gemini CLI harness wasn’t very good. So I tried using OpenCode (an agentic harness) and configured it to use Gemini under the hood.
What initially drew me to it was that it’s a genuinely sophisticated product. Unlike raw CLI tools, it has a web GUI so you can code from the browser without SSH, a dedicated thinking mode, and you can even configure different models for different roles; a cheaper model for small edits, a more powerful one for planning for example. On paper, it seemed like a step up in every way.
New harness, same results#
Unfortunately, despite the fancy UI, the experience was very similar to Gemini CLI. It kept falling back to dperson/samba, which didn’t meet my initial requirements. At least it didn’t loop forever — it gave up and explained why it was stuck.
Here’s the key insight: adding a more sophisticated harness made no difference. That doesn’t mean the harness is irrelevant; it might well be making things worse in ways that are hard to observe. But it’s clearly not enough to compensate for a weak underlying model.
The API cost shock#
I was using the cheaper Gemini Flash as the implementation agent, but I thought it might not be good enough for this task. So I switched over to using the more expensive Gemini Pro via API.
I spent roughly £5 in one evening across various homelab debugging tasks.
That’s 25% of a monthly Claude Code subscription. In one evening. And my Samba server still didn’t work.
Attempt 3: Claude Code actually solved it#
At this point, I realised I should probably try what I know works in my day job: Claude Code.
Claude’s approach to the same Samba problem:
- Planned before jumping in; actually understood what I was trying to achieve
- Recognised that the two images I’d found weren’t a good fit, and went looking for alternatives
- Found mbentley/docker-timemachine which is a purpose-built image I hadn’t come across
- SSH’d into the homelab and tested from both sides
- Actually solved it
The rule of thumb is that for low usage, PAYG is cheaper. So I initially tried Claude via API, thinking it would be cheaper than the subscription. While I did solve my problem, I spent another ~£5 in the process.
After this result, I bit the bullet on Claude Pro (£18/month). It’s a proper product rather than just API access; some features are paywalled even with an API key, such as remote control, which lets you kick off tasks from your phone while away from your desk.
The three lessons#
Lesson 1: A better harness can’t fix a weaker model#
Simple agents like Gemini CLI are fine for simple conversions like LaTeX to Typst. However, they struggle severely when solving more complex tasks.
There’s clearly a fundamental model limitation, and not just a tooling issue. Gemini kept taking shortcuts and ignoring my constraints, even with a better agentic harness.
Lesson 2: Claude still has an advantage right now#
Right now, you can’t get the same experience for less money. The quality gap is real: it’s not just about features, it’s about problem understanding, reasoning, and finding the right solutions.
That said, this space is moving fast. OpenCode with a stronger model choice might close the gap; I only tested it with Gemini. And the model landscape in 6 months will look very different. It’s worth keeping an eye on.
Lesson 3: The subscription threshold is lower than you think#
This goes against the conventional rule of thumb that API pricing is cheaper for light usage.
I spent £5 in one evening of hobbyist debugging. At that rate, the £18/month subscription pays for itself after just 4 evenings of work — and that’s not counting the time lost fighting with Gemini.
Developer time is expensive (even if it is just for your hobbies!). The subscription makes sense much earlier than I’d expected.