- They're obviously being targeted politically because they refuse to kiss the ring, vibes, whatever you want to call it.

- They're also justifiably being scrutinized because they just spent like 3 months telling everyone that Mythos is a nuclear bomb and telling the government to fuck off as they drip fed access to a bunch of random corporations.

- MCPs are great for stateless, mostly read-only interactions with document store type things. Notion/Slack/Linear are perfect use cases. I have those MCPs connected to claude code and they work great. These tools never had CLIs or super well used public APIs to begin with. MCP handles the auth for me. Cool.

- MCPs are great but not fully necessary for "function shaped" things where you're trying to run some Function and that Function has a lot of parameters with some subtlety to them and perhaps needs some examples to really help the LLM understand. Though you can get away with a skill + curl, or a hand rolled script even.

- MCPs are not so great for interacting with more complex stateful systems with large surface area. You don't want/need an AWS MCP, for example. And of course Cloudflare is the canonical example here where they do have an MCP but it has a special "Code Mode" because they have a huge product surface and a lot of state.

Most companies are somewhere in the vast space between being a document store type thing and AWS, so aren't really sure what their MCP should look like, or how customers will use it, but feel like they're missing the boat if they don't ship something. So they ship an MCP and perhaps the people who need the document type stuff load it up and get some use out of it, but others are not so satisfied. Or maybe from the other direction, people are trying to use your product but aren't super technical or don't know how to best use it with AI, but "loading up an MCP" seems like a reasonable way to start, so they ask everyone "Where's your MCP"?

I run into this at work all the time. We get a lot of requests for an MCP. But our product is not so simple to just stuff into a bunch of stateless API calls. And we question whether the people requesting the MCP really know what they want it for, exactly, other than to hook up to claude code so they can say "claude go do everything" (which is a valid sentiment, but implies a lot of work on our end to figure out how to make that work well).

- Defining the issue/ticket, what "success" looks like (if I have a good idea of this), high level approach guidance 50%

- Dispatch agent to work on it 5%

- Occasionally return and nudge agent + send /simplify or /code-review 5%

- Look at the code/session summary, divergences from the plan, ask followup questions 40%

Occasionally yes there is some solution the AI chose that is suboptimal and I would prefer fixed in a different way. Mostly though it's straightforward.

It's analogous to how in the early days you could see benefits by telling the models to "think step by step". /code-review is something like "review angle by angle". "Consider removed behavior" and also "Look at language gotchas" and also "Look at test changes"...etc. Yes these are all somewhat implicitly already part of what "code review" means, but the models perform best with explicitness.

If you want my 2c as a power user: just don't think about it and use /code-review xhigh --fix. This will cover like 98% of what you want out of code review. It's a good skill.

I see now in 2.1.152 you added those focus areas back to /code-review, but still bundled with the correctness finding. It would be great to have more fine grained control over the /code-review angles beyond just effort level. Or maybe you would recommend that I just specify that as freeform input after effort level?

However, 99.9999% of coding is not like that. Non-coders don't care about the code at all. They just care about outcomes. People don't care if it's "slop" if it works. Similar to bug prevalence, the optimal level of slop is not zero and will be decided by the market, not by coders.