Agreed, this is exciting, and has me thinking about completely different orchestrator patterns. You could begin to approach the solution space much more like a traditional optimization strategy such as CMA-ES. Rather than expect the first answer to be correct, you diverge wildly before converging.
If that's true, it would be surprising; the current Sonnet 4.6 is not in the same league as either Opus 4.5 or 4.6, either anecdotally or on benchmarks.
I think it's simpler than that. AI, like the internet, just makes it easier to communicate boring thoughts.
Boring thoughts always existed, but they generally stayed in your home or community. Then Facebook came along, and we were able to share them worldwide. And now AI makes it possible to quickly make and share your boring tools.
Real creativity is out there, and plenty of people are doing incredibly creative things with AI. But AI is not making people boring—that was a preexisting condition.
I've been pleasantly surprised by the Claude integration with Xcode. Overall, it's a huge downgrade from Claude Code's UX (no way to manually enter plan mode, odd limitations, poor Xcode-specific tool use adherence, frustrating permission model), but in one key way it is absolutely clutch for SwiftUI development: it can render and view SwiftUI previews. Because SwiftUI is component based, it can home in on rendering errors, view them in isolation, and fix them, creating new test cases (#Preview) as needed.
This closes the feedback loop on the visual side. There's still a lot of work to be done on the behavioral side (e.g. it can't easily diagnose gesture conflicts on its own).
Do you think there would be value in a workflow that translates all non-English input to English first, then evaluates it, and translates back as needed?
Personally, I don't bother prompting LLMs in Japanese, AT ALL, since I'm functional enough in English(a low bar apparent from my comment history) and because they behave a lot stupider otherwise. The Japanese language is always the extreme example for everything, but yes, it would be believable to me if merely normalizing input by first translating just worked.
What would be interesting then would be to find out what the composite function of translator + executor LLMs would look like. These behaviors makes me wonder, maybe modern transformer LLMs are actually ELMs, English Language Models. Because otherwise there'll be, like, dozens of functional 100% pure French trained LLMs, and there aren't.
Difficulty is the only true moat. [Astronaut: always has been]
Current examples: esoteric calculations that are not public knowledge; historical data that you collected and someone else didn't; valuable proprietary data; having good taste; having insider knowledge of a niche industry; making physical things; attracting an audience.
Some things that were recently difficult are now easy, but general perception has not caught up. That means there's arbitrage—you can charge the old prices for creating a web app, but execute it in a day. But this arbitrage will not last forever; we will see downward price pressure on anything that is newly easy. So my advice is: take advantage now.
This is what excited me about Sonnet 4.6. I've been running Opus 4.6, and switched over to Sonnet 4.6 today to see if I could notice a difference. So far, I can't detect much if any difference, but it doesn't hit my usage quota as hard.
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