That’s great to know. I’ve come to the same conclusion. I’ve found that things work best when they happen right where I’m already working. Uploading files or recreating context in a web service adds friction, especially when everything is already available locally.

Came across this interesting article on the internals of Claude Code: https://medium.com/@outsightai/peeking-under-the-hood-of-cla... . The author redirected the LLM calls into LiteLLM to analyze how it behaves.

That's really neat!

Ah, that’s interesting. Are there any parts of Claude Code that you feel could work differently or get in your way for these kinds of tasks?

Will check out Grok Code Fast - thanks for the pointer. In my experience, coding agents can swing a lot in quality depending on the model’s reasoning power. When the model starts making small but avoidable mistakes, the overhead tends to cancel out the benefit. Curious to see how Grok performs on multi-step coding tasks.

True. Im working with Python CRUD apps, which every model is fluent in. And I'm personally generating 100-line changes, not letting it run while I'm AFK.

That's what I love most about Claude. I love Django and I love React (the richness of building UIs with React is insane) and sure enough Claude Code (and other models I'm sure) is insanely good at both.

Ah, that makes sense. I’ve had similar luck with UI refactoring on cheaper models, mainly because you can quickly verify whether the output is right.

Cline is great, but it’s primarily focused on coding workflows, similar to Claude Code. RowboatX is aimed at a different category: background agents for non-coding automations (e.g. meeting prep, daily briefings, etc).

The big difference from Claude Code (and Cline) is that RowboatX can spin up persistent background agents that run on schedules, use the system shell, and call MCP tools to automate tasks outside of coding.

In the demo - for the voice and music, it uses the ElevenLabs MCP server for TTS, and ffmpeg locally to stitch the audio together. In the demo we pull content from Tweets via an MCP tool, but you can swap that step for anything — for example, fetching your saved articles with curl. There’s no special model required; any LLM that can call tools will work.

We’ve included the exact agents used in the demo here for reference: https://github.com/rowboatlabs/rowboat/blob/cli/apps/cli/exa....

We’re adding an easier way to run examples soon. In the meantime, if you’d like to try this one locally: (1) Copy the agent file into ~/.rowboat/agents/ (2) Add the MCP server (and your keys) to ~/.rowboat/config/mcp.json (3) Run: 'rowboatx --agent=tweet-podcast --input=go'

Thanks! Curious what kinds of workflows you are automating right now and any pain points you’ve run into.

I automated a bunch of business workflows like sorting documents for accounting in my cloud storage, tagging emails that are invoices, stuff like that. I do this for a living though, so I also use these as casestudies and rowboat is a hard sell for end users I guess.

Thanks, that makes sense. I assume the CLI approach might be the main hurdle for end users?

Exactly. They want something that just works and runs somewhere. I'm talking about SME owners.

Yeah, I think when they made the bet it genuinely made sense. But in coding workflows, once models got cheaper, people did not spend less. They just started packing way more LLM calls into a single turn to handle complex agentic coding steps. That is probably where the math started to break down.

Ah did not realize this - good to know!

Fixed the quick start instructions to not start with OpenAI.

Yep, totally agree. We actually had an earlier web version, and the big learning was that without access to code-related tools the agent feels pretty limited. That pushed us toward a CLI where it can use the full shell and behave more like a real worker.

Really appreciate the support and the Goose pointer. Would love to hear what you think of RowboatX once you try it.