u/Parking-Guava-3398

My kids are barefoot literally all the time, and I’ve realized this is what’s driving my robot vacuum search more than anything else. Not “smart home” stuff. Not fancy features. Just… can I walk around my house and feel like the floor is actually clean.

I keep going back to How do I choose between a $600 and $1,600 robot vacuum in 2026? because I genuinely can’t tell where “better cleaning” ends and “more expensive for no reason” starts.

I don’t mind paying more if it really means cleaner floors, less hair, less dust, less second-guessing. But if the $1,600 one still leaves me doing a quick cleanup before bedtime, I’d rather know that now.

For moms who’ve bought either one — did the more expensive robot vacuum actually make a real difference in everyday cleaning?

I kept running into the same problem with AI agents: they could finish a task, but they never really held the job.
The work would come back the next day and I was still the one rebuilding context, checking what was pending, figuring out what output mattered, and deciding what the next step should be.
So I built Holaboss. It’s an open source desktop + runtime for AI workers.
The core idea is that the worker should not just help with work. It should take the work off your hands.
When the work is recurring, stateful, and tied to an outcome, I don’t think a chat thread is enough.
The worker needs its own workspace. In Holaboss, each worker gets a real operating environment with:

AGENTS.md for human rules
workspace.yaml for the runtime plan
local skills
apps and integrations
outputs
durable memory
runtime state
automations
a handoff trail you can inspect later

The reason I built it this way is pretty simple. I care much less about “can the agent do one cool run?” and much more about “can this worker actually keep the work moving without me?”
That means stuff like:

recurring follow-ups
content/research loops
queue-based work
things with backlog, state, and unfinished next steps
work that should be judged by whether it keeps progressing, not whether one answer looked good

The part I’m most proud of is the structure. I didn’t want memory to just mean “more chat history.” I wanted the worker to have a real work boundary, so rules, memory, outputs, skills, and runtime truth don’t all get mixed together.
That makes it much easier to resume, inspect, hand off, and reuse. Tech wise it’s an Electron desktop with a TypeScript runtime, Fastify API server, and SQLite-backed state store. MIT licensed. macOS works today, Windows/Linux are still in progress.
Repo: https://github.com/holaboss-ai/holaboss-ai
If you like the direction, I’d really appreciate a ⭐️.
Happy to answer questions about the architecture or how I’m thinking about AI workers that own ongoing work instead of just doing one-off execution.

How do I choose between a $600 and $1,600 robot vacuum in 2026? my kids are always barefoot and I’m tired of guessing if the floor is actually clean

I built an open source AI worker desktop because one-off agents kept handing the work back to me