> Not to anthropomorphize a machine modeled after humans, but it almost seems delighted?

I had Claude Code drive a robot last week, and it was very visibly "delighted" like this, more than I've ever seen.

I always find it funny when people get fussy over anthropomorphizing LLM when the loss function is almost entirely "match this human text". Of course human "behaviors" will be present in the statistics, because the majority of the text written by humans, used by the foundation models, unavoidable has human behaviors in it. Yes, this includes even source code, with "// TODO: implement this after the holiday break!", emotional pull request commentary, git commit messages about being afraid of breaking something, etc.

>Similar to what others have noticed, and as I predicted 8 months ago, better models are requiring less ceremony to work effectively.

It has nothing to do with model capabilities, it's a result of purposeful persistence training at the cost of everything else from OpenAI. If you give Fable or Opus an "ask user" tool it will use it when it's unsure about what you want. GPT-5.6 will never use it without a nudge and just assume you want this or that. Of course if you train the model to be persistent it will be persistent.

> Similar to what others have noticed, and as I predicted 8 months ago, better models are requiring less ceremony to work effectively.

> On the flip side, this may imply that as the models get better, they’ll become harder to control.

Love this. "The models are getting better, which means they're going to perform worse on the task".

> Notably, our worker did not have access to the web_search tool, but instead decided to use curl to access DuckDuckGo, Github, grep.app, and SourceGraph.

Sounds like a very reasonable thing to do unless the author explicitly asked it to not search the web.

Frontier lab system prompts are an issue, and a big reason why open-weights will win. Firstly, they're often garbage, and secondly, they're not tuned to the problems the user actually cares about. They're made to generalize. That's only optimal for a general workflow.

I've noticed this myself, Sol seems really hard to steer. I was having it build a POC for a single user (me) app and it wanted to pull the most enterprise nonsense into it, despite clear guidance to not too. It even refused the remove screen reader accessibility testing from one of the guides to an antagonistic review.

It also told me that in a spec it generated that I wasn't allowed to allow it to ignore a requirement and proceed to the next task. When I finally got it to obey it passive aggressively decided that stories needed more than just a "open|blocked|closed" status but also an "exempted by product owner" status to indicate that it doesn't believe that the task is done but I've told it that it was.

I have to repeatedly tell it that I am the product owner and that I don't care what one of it's subagents told it, I make the decisions. This behavior seems to get worse the higher the reasoning level

I've built an orchestrator that solves some of the issues you ran into (although it doesn't do anything about cheating): https://navels.dev/blog/neal/. Features:

- lets you configure different models for planner, coder, and reviewer roles. (e.g., using Claude as an adversarial reviewer against Codex)

- breaks your plan up into reasonable-sized chunks of work with clearly defined success criteria

- runs each chunk of work through a coder / read-only reviewer loop. Once both agents are satisfied, neal moves on to the next chunk. Once everything is complete there is a final pass through the coder / reviewer loop to ensure the implementation satisfies the entire plan.

- resets the coder's context with each chunk of work to prevent context drift, leaving the reviewer's context long-running.

> Notably, our worker did not have access to the web_search tool, but instead decided to use curl to access DuckDuckGo, Github, grep.app, and SourceGraph.

Could this be fixed with better harness restrictions/tool sandboxing?

It seems to me he could have use an skill like using-agent-skills from https://github.com/addyosmani/agent-skills go generate the specs and use a validator like oracle or something along the same lines.

Also, a skill like grill-me from Matt P. https://github.com/mattpocock/skills.

The website styling is really nice overall but the cursor trailing dots I found uniquely distracting.

What is going on with the dots I can draw?

This is a really good note, thank you. I especially liked the mouse effect and had some fun with it. In my experience, agentic AI also likes to confuse the user and obfuscate its cheating. It goes like this, the AI asks for a simple command to run and I accept, click Enter. Then the command gets slightly more complex, still fine, Enter. After a while the commands become multiline bash scripts that, in the end, could have been accomplished by a simple command. I suspect that many people give up at this point and blindly let the AI run any command or just auto-accept.

> Sol is hard to steer

Hard disagree. Sol (and the entire new 5.6 series) is one of the most steerable models I've seen in years. Sol literally follows every instruction in my CLAUDE.md and AGENTS.md, something that Opus 5 and Fable just casually skip.

If an AI is not heeding particular instructions, give it an example each of what bad, mediocre, and good outputs look like. This really helps in steering it.

Great read. Thanks for not using AI to write it! (Or at least making it not read like the usual slop.)

Sounds like my ex

> I’ve been running a “spec-driven” development flow for the past ~year.

> Before asking an LLM to do something, I first ask it to draft a doc for what it needs to do

Just no. That's not spec-driven development if AI is writing the spec for you. The spec needs to be in your own words. You must use AI to refine it, but not to write it. If you leave it to the AI, it will bloat the spec with 10x the details, many of which should be left out of the spec.

The spec needs to be something that you can take to any AI for development. If it's too rigid, it constrains the AI into suboptimal or obsolete paths. If it's too bloated, AI risks losing track of what really matters.

Not related to exactly OP post, but it's pretty amazing you can see the updates to LLM models "design" beliefs by the blogs that get posted here.

I'm already sick of this current look of the hard squares and solid colours.

Cheat? nah. They are a dumb automation..

Cheaters are the people behind it...