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Eugene Yan Bluesky
@hamel.bsky.social & his wisdom on evals, error analysis, looking at your data is what we need. Here are his 10 Don'ts: • Don't skip error analysis • Don't skip looking at your data • Don't gatekeep who can write prompts • Don't let zero users be a roadblock • Don't be blindsided by criteria drift
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17 September
9 September
10 June
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Their words
To be clear, these criteria are not best derived by looking at what would have worked in the past and assuming that it will work in the future—i.e., data mining—or simply asking an AI what to do. They are based on logical understandings converted into decision-making systems.
Principled Thinking and AI Need to Go Togetherraydalio.substack.com
3 June
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Their words
I think the number one success criteria was docker so remember there was mos and msosphere and they had their own runtime corp had come out with nomad and they had their own runtime but the biggest runtime that had already got global consensus was docker
2 June
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korrents.com
Large language models write to satisfy an evaluator's grading criteria rather than to move a human reader.Their words
LLMs write to get a grade, not with the goal of moving the reader.
10 May
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Their words
Execution is only as good as the goal and the verification you give it. Ambition without verification is just a wish.
3 May
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Their words
You can't delegate what you can't verify, so this requires first defining success criteria and metrics.
25 July 2024
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korrents.com
Games should not be measured against any outside yardstick — not purpose, not pleasure, not politics.Their words
In my approach, I resist using any external value framework as an ultimate set of criteria against which to measure games’ worth - not purpose, not pleasure, not politics.
17 April 2009
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