Seems so TBH, I think wrappers may eventually have a significant advantage over single-model products, but so far it hasn't happened. We need better routers.
LLMS
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Think before you prompt: second-order prompting advice
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Most prompting advice focuses on structure. Templates. Formats. Roles. Those matter. But they're surface mechanics. The real skill is what you think about BEFORE you write the prompt. Second-Order Thinking is one framework. There are dozens more from investing, military
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AI models are literal first-order thinkers
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AI models are first-order thinkers by default. You ask a question, they answer it. Directly. Literally. Without considering what happens next. That's not a flaw. That's how they work. The model responds to exactly what you give it. So if your prompt doesn't include the second
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Ask the follow-up: improve generic AI outputs
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"Why does my AI output sound so generic?" Because you're prompting at the surface. You ask for a strategy, AI gives you one, you move on. You never ask "and then what happens?" That one missing question separates average AI work from exceptional. And it has a name.
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OpenClaw: the layer giving superpowers to Codex/Claude
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Your mental map is off. openclaw is the layer that gives codex/claude superpowers. It can *use* codex/claude and others.
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Opus 4.8 tops BullshitBench after 4.7 dip, needs harder questions
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Top notch result from Opus 4.8 on BullshitBench, after a slight dip with 4.7. Need to start thinking of some new harder questions soon!
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Why not integrate? Anthropic banned you, use codex
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What’s stopping you from integrating it right now? (I see that Anthropic banned you, codex is the better performing harness/model tho so just use that)
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Opus uses twice the tokens and is more expensive
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Not a fair comparison. Opus burns at least twice the token for similar tasks AND is more expensive, far easier to spend a million there.
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Token allocation, not model, drives multi-agent AI performance, study finds.
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80% of multi-agent performance variance is just token allocation, not the model. That's from a UC Berkeley paper that decomposes any AI agent into 6 components: model, memory, context, skills, orchestration, verification. The model is one. The other 5 are where the gains