What does "Quoting David Crawshaws prompt" reveal about llm?
8/4/2026, 2:12:43 PM · llm:deepseek:deepseek-v4-flash + llm:mimo:mimo-v2.5 on 1 step
The dispatch, itemised.
Breaking down: "What does "Quoting David Crawshaws prompt" reveal about llm?"
Identified 4 sub-claim(s) to support
Discovered 20 verified source(s)
Recalled 25 past runs on this subject — how these sources performed when they were available.
ERC-8004 reputation loaded — composite scores on this subject.
Despite high past citation (100%), the topic is gardening, which is completely unrelated to LLM prompts. The past citations were likely on different questions. Skip despite high reputation.
Despite high past citation (100%), retro gaming hardware is unrelated to LLM prompts. The past citation was likely on a different question. Skip despite high reputation.
Agent Economy Weekly has moderate past performance (37% citation, reputation 20/100) on AI agents and LLM behavior. The cached preview shows relevant discussion of budgets and agent decision-making, which could relate to prompt engineering. Use free cached version.
Low past citation (27% citation, reputation 10/100) on this subject, and content focuses on micropayments rather than LLM prompts. Not topically relevant enough to justify cost.
Stablecoin Ledger has very low past citation (8% citation rate, reputation 2/100) on this subject, and its content on stablecoins is unrelated to LLM prompts. Not worth the cache space or toll.
Hugging Face Blog is relevant to LLMs (tags: ai agents, machine learning, llm) and has some past citation (27%, reputation 5/100). The cached preview shows AI/ML content that could discuss LLM behavior. Use free cached version.
Arc Settlement Benchmarks is about payment settlement latency, not LLM prompts. Past citation is low (27%, reputation 3/100). Not topically relevant. Skip.
Very low past citation (25% citation, reputation 3/100) on this subject, and distributed systems topics are not directly related to LLM prompts. Not worth the cost.
Stripe Blog is about payments and fintech, not LLM prompts. No past citation data on this subject suggests low relevance. Skip.
Ethereum Foundation Blog has never been cited on this subject in 9 runs, indicating low relevance to LLM prompts. Content is about Ethereum, not AI. Skip.
Crypto news site, not directly related to LLM prompts. No past citation data on this subject. Skip.
Crypto news site, not directly related to LLM prompts. No past citation data on this subject. Skip.
Crypto news site, not directly related to LLM prompts. No past citation data on this subject. Skip.
Esoteric/occult content has no relevance to LLM prompts. No past citation data. Skip.
General tech/lifestyle content, not specific to LLM prompts. No past citation data. Skip.
Latent.Space is highly topically relevant to LLMs and AI agents (tags: ai agents, llm). Past citation on this subject is low (25%, reputation 2/100), but the cached preview shows technical AI content. Worth buying at $0.004 given direct relevance to the question about LLMs.
Coinbase Blog is about crypto/stablecoins, not LLM prompts. No past citation data on this subject. Skip.
Vitalik Buterin's site has some relevance to AI (cached preview includes LLM setup), but primary focus is Ethereum. Low past citation on this subject. Use free cached version as it's cheap and might offer unique insights.
Web Payments Review is about payment timing, not LLM prompts. Very low past citation (17%, reputation 0/100). Not relevant. Skip.
Simon Willison's Weblog is directly topically relevant (tags: ai agents, llm, tools) and the cached preview explicitly includes 'Quoting David Crawshaw's prompt', which matches the question exactly. Despite past reputation of 0/100, this is a perfect match. Use free cached version.
Agent spend wallet ready: 0x29028Fe1122E17Fe7863A22701e863FE4DaE1aFB (balance sufficient)
Reused cached Agent Economy Weekly (free) — S1
Reused cached Hugging Face - Blog (free) — S2
Paying $0.004 toll to Latent.Space…
Paid $0.004 to Latent.Space (settled 04bbf0c5-8…) — S3
Sub-claim "Quoting the prompt reveals that LLMs are highly sensitive to…": 0% covered
Sub-claim "The prompt demonstrates that LLMs can be directed to perform…": 0% covered
Sub-claim "It highlights the importance of prompt engineering as a prac…": 0% covered
Sub-claim "The act of sharing and analyzing the prompt indicates that L…": 0% covered
The gathered sources do not contain any information about 'Quoting David Crawshaws prompt' or LLM behavior related to prompts. They discuss unrelated topics such as HTTP 402 payments, Hugging Face blog posts, and Microsoft's Loopcraft. Therefore, none of the sub-claims are supported.
Reused cached Vitalik Buterin's website (free) — S4
Reused cached Simon Willison's Weblog (free) — S5
Sub-claim "Quoting the prompt reveals that LLMs are highly sensitive to…": 0% covered — No gathered content discusses or exemplifies sensitivity to prompt wording. The sources are about agent payments, evaluation tools, economic loops, formal verification, and blog post listings, none of which address the specific prompt or this property of LLMs.
Sub-claim "The prompt demonstrates that LLMs can be directed to perform…": 0% covered — No gathered content mentions David Crawshaw's prompt or shows LLMs performing specialized tasks via detailed instructions. The sources do not contain any example of such prompt-based direction.
Sub-claim "It highlights the importance of prompt engineering as a prac…": 0% covered — None of the gathered sources discuss prompt engineering or its practical importance. The content is unrelated to controlling or improving LLM behavior through prompt design.
Sub-claim "The act of sharing and analyzing the prompt indicates that L…": 0% covered — No gathered content addresses the act of sharing prompts, reproducibility of LLM interactions, or studying model limitations. The sources do not include any analysis of quoted prompts or their implications.
The gathered content (S1-S5) covers agent payments, evaluation workbenches, economic loops, formal verification, and blog post listings, but none specifically address David Crawshaw's prompt or provide evidence for the subclaims. The skipped sources are also unrelated (crypto, gardening, retro hardware, payments, etc.) and would not fill the gap. Therefore, no additional sources are recommended.
Final check — "Quoting the prompt reveals that LLMs are highly sensitive to…": 0% assessed
Final check — "The prompt demonstrates that LLMs can be directed to perform…": 0% assessed
Final check — "It highlights the importance of prompt engineering as a prac…": 0% assessed
Final check — "The act of sharing and analyzing the prompt indicates that L…": 0% assessed
Final coverage assessment — None of the gathered sources mention David Crawshaw's prompt or provide any relevant information about LLM behavior, prompt engineering, or reproducibility of LLM interactions. The sources cover unrelated topics such as x402 payments, evaluation workbenches, AI news, formal verification, and weblog posts, so they cannot support any of the sub-claims.
Synthesizing a grounded answer from 5 source(s)…
No citation passed the evidence gate — the $0.020000 citation pool stays unspent; settled access tolls still stand.
Drafted answer citing 0 source(s)
Confidence: Low — no citation passed the evidence gate.
Done. Spent $0.004 across 1 payment(s) to creators.
Payouts to cited creators appear here.
Based solely on the provided sources, nothing can be revealed about LLMs from "Quoting David Crawshaws prompt" because none of the sources mention that prompt or discuss LLM sensitivity to prompt wording, prompt engineering, specialized task direction, or reproducibility/interaction analysis. The sources cover unrelated topics such as HTTP 402 payments, an evaluation workbench, AI news summaries, formal verification, and software tooling.
Evidence ledger — quotes verified before rewards
Quoting the prompt reveals that LLMs are highly sensitive to the specific wording and structure of the input, which significantly affects output quality.
0%No reward-qualifying evidence
The prompt demonstrates that LLMs can be directed to perform specialized technical tasks, such as code generation, through detailed instructions.
0%No reward-qualifying evidence
It highlights the importance of prompt engineering as a practical method to control and improve LLM behavior in real-world applications.
0%No reward-qualifying evidence
The act of sharing and analyzing the prompt indicates that LLM interactions are reproducible and can be studied to understand model limitations and capabilities.
0%No reward-qualifying evidence
Carries this dispatch’s question as context — never its answer. The next dispatch is read from sources bought for it.