extr

Well made website IMO. Gets to the point with plenty of easy-to-understand evidence and calls to action.

Seems pretty reasonable.

I think DeepSWE is flawed in a different way: the tasks look like someone took a bunch of big highly technical PRs they found really well done, and inverted it into specs for agents to autistically execute. This is not really how people use agents in practice IMO. And it's why DeepSWE is so generous to OAI models, rigid task execution is the thing they're best at. I think FrontierCode matches the vibes a lot better.

[Future voice]

I'm skeptical any of that matters at all if at some point AI is perceived by the government to be a true existential risk to public welfare.

I don't know that I want to stop such a thing. It's good that nerve gas is banned. I don't want random people having access to easy-to-follow instructions to make COVID-29.

They are not going to let open weights models with zero restrictions exist dude. They will be regulated like guns, or probably closer to nerve gas or enriched uranium.

I think they were totally correct in spirit. But RE: details about them giving access to an SK corp with possible Chinese ties. Of course that raises eyebrows in the USG, justifiably. Sloppy work from Anthropic.

To me they are genuinely trying to walk a tough line - they legitimately believe that they need to warn the public and make a lot of noise so society can try to adapt to this technology. OTOH no adaption (good or bad) can take place if the models themselves are so restricted as to be inaccessible, or if the powers that be don't understand it well enough to put the right policy/laws in place.

I mean obviously they're correct but also the complaints of the administration aren't totally without basis.

- 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.

Really funny to describe OpenAI/Anthropic as a "SaaS"

This isn't even about cyber attacks. This is just LLM development which is increasingly just called software development. And at least for cyber it says "Sorry I can't help with that"!

I'm a big fan of Anthropic. Just check my post history. I've been accused of working there. But this is complete bullshit and they need to get real. Silent sandbagging is not acceptable, especially given they've shown with this release their safety filters have HUGE amounts of false positives.

Interesting it's in python!

The points in this article don't really land for me. They are mostly critiques of particular MCP implementations rather than the modality itself. My impression right now:

- 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).

Damn already there in 154. Thank you man.

IMO they have all been clean and noticeable upgrades over their predecessors. Opus 4.7 in particular was a solid jump in capabilities.

Are you thinking of the /effort level in Claude Code? I would just go with xhigh as a reasonable default. Most important thing in prompting is specifying what "done" and "success" looks like to you. Ask Claude to help you come up with a well formed request and spend most of your time on that, then paste that into a brand new session.

I don't even bother looking at the code until I've run a code review pass on it. Why waste my time with trivial bug fixes? I find the best way to spend time right now is like:

- 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.

The advantage is that /code-review supplies a structured idea of how to review and what that process should look like and then launches independent subagents to approach the issue from multiple angles.

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.

Hey Boris, some feedback. I like the new /code-review skill but was disappointed you guys removed /simplify because I quite liked the focus on finding code reuse/efficiency opportunities.

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?

For a lot (most) of what we do with programming, the process actually doesn't matter. I understand you are a real ass dude who is in this shit for the love of the game. I respect that. You are a true artisan and exist in a kind of rarified space. There will always be a place for people like you and in some senses you are correct - you are not replaceable by any AI as they currently function today.

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.

ok thank you for this anecdote

Did you RTFA?

What is the OP talking about. $/unit intelligence is going down rapidly. You can achieve what would have been considered miracles in 2022 with < $10.

Pangram is highly reliable.

If you do not live in one of the handful of areas in the US with public transportation infrastructure and also do not own a car you are an extreme outlier. Likewise, if you do not use AI tools to code, outside of some highly niche and specialized areas where perhaps they are still not effective, you are also an extreme outlier and are going to making significant tradeoffs to continue that practice.

If they want to continue that practice that's fine but they will quickly find that it severely limits their options for participating in the software industry.

You are picking at the analogy rather than engaging with the point. In the US, excluding areas with substantial public transportation infrastructure (realistically just a few major cities), car ownership is nearly universal. You can choose to not own a car as a lifestyle choice but you will be making concessions in other areas (the types of jobs and homes you have access to), similar to how refusal to use AI in software engineering at this point will substantially limit your options and ability to participate in the sector.

You're welcome to feel that way but it's a luxury belief. In reality, outside of a few (one?) major city in the US with public transportation infrastructure, you need a car. 92% of people own a car, higher if you exclude the dense urban areas I'm talking about.