ruso-0's comments

An interesting combination, indeed. Running local models through Claude Code opens up many possibilities for teams that can't push code to external APIs.

However, I've noticed something with local LLMs: they're much more prone to context window issues than hosted models. A 7KB model with an 8KB context fills up quickly when reading files, and once it overflows, it starts displaying hallucinations in the function signatures. Have you encountered this problem with Unsloth models? If so, how do you handle context management?

This is a really smart approach - reviewing in the background while the agent keeps working means zero interruption to the flow.

Curious about one tradeoff though: by the time Saguaro catches a bug in the next hook cycle, Claude has already moved on and built more code on top of the broken foundation. Does it handle cascading fixes well? Like if Claude wrote a broken function in file A, then imported and used it in files B and C before Saguaro flags it — does the fix propagate cleanly or does it sometimes cause a chain reaction of corrections?

I've been experimenting with the opposite approach — validating the AST before the write hits disk, so broken code never lands. Catches syntax issues instantly but obviously can't catch logic bugs the way a full review daemon can. Feels like both approaches together would be the ideal setup

I've been doing exactly this for the past few weeks, really. Claude Code + MCP plugins is the setup that really worked for me.

The key thing I learned was not to just tell Claude your repository and hope for the best. The raw approach consumes tokens incredibly fast, but Claude reads entire files when it only needs one feature, retries incorrect edits more than 5 times, and loses context halfway through.

What really works is giving Claude a CLAUDE.md file in the root of your repository with specific instructions for the workflow (which tools to prefer, when to compress or read raw, etc.). Claude Code reads it automatically upon login. Think of it as an .editorconfig file, but for AI behavior.

For the $25/PR review use case specifically, the bottleneck isn't Claude's intelligence, but the context window management. A repository of 500 files can exhaust the window before Claude finishes reviewing. You would need some kind of indexing layer that provides Claude only with the relevant snippets for each PR difference, not the entire codebase.

What kind of repositories do you have in mind? The approach varies greatly depending on the size, but I'd like to hear your thoughts.

Let's see, this is a great project, but the sandboxed virtual machine approach makes sense. I've had Claude Code destroy a configuration file on my local machine more than once, so running it remotely is actually smart, but I'm not sure.

However, when creating MCP tools, I've noticed that the processing isn't really what's expensive. It's when the agent gets stuck in a loop. For example, I note writes faulty code, executes it, detects the error, tries to "fix" it, makes it worse, rinses it, and repeats it 5 to 10 times. This quickly consumes the context window, regardless of where the code is running.

I'm curious: Does Oblien detect when an agent is simply wasting time? Or is that left to the agent framework? Because, I mean, sometimes I look for that solution and there isn't one :(