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Kun Chen GitHub
axi — Design principles for agent ergonomics. Higher accuracy with lower token cost than both MCP and regular CLI.
The subject this post names, from the same vocabulary the directory files beliefs under, and the words it uses that this site has seen least often elsewhere. Posts are matched on those words alone — nothing here is a summary of this one.
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17 September
2 September
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26 July
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Their words
We don't need the the agents to be 100% accurate, 100% high quality in order for us to use it. It could, you know, literally be 80% and then we help it the rest of the way, or it could be 99% we help it the rest of the way. And so I I think controllability is probably the single biggest breakthrough that we need for agents at every single level.
13 July
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Their words
And sometimes confidence, high confidence doesn't necessarily mean high accuracy. And I think part of the reason we're fooled is usually, you know, the more you see something, the better you'll remember it, just like the Apple logo. But that's not always the case.
21 June
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korrents.com
Generative AI systems have no built-in drive toward truth and accuracy, unlike people or institutions.Their words
Generative AI has no internal, designed momentum towards truth and accuracy (beyond the absurdly diminishing returns of energy-hungry multi-layered LLMs), but a person or an institution can (and should).
7 April
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Their words
And so, you have what seem like three very different disconnected phenomena all being explained by this one set of ideas.
12 February
1 January
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korrents.com
When accuracy and belonging conflict, most people take the belief that keeps them accepted by their community.Their words
if you have to choose between, well, I could believe this thing and be ostracized or criticized or outcast from my community, or I could believe this other thing, which may not be that accurate, but it will get me praised and rewarded and accepted by my community. A lot of the time, the desire to belong overpowers the desire to understand.
23 November 2025
From one piece Product Evals in Three Simple Steps 2 beliefs, in the piece's order there
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Their words
The benchmark is human performance, not perfection. We sometimes get requirements for 90%+ accuracy.
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korrents.com
The main advantage of an LLM evaluator over human annotators is scalability, not higher accuracy.Their words
In my opinion, the true benefit isn't higher accuracy than human annotators-it's scalability.
11 September 2025
4 July 2025
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korrents.com
Modern CPUs can now predict the indirect dispatch jump in a bytecode interpreter loop with high accuracy.Their words
Modern CPUs mostly no longer struggle to predict the bytecode-dispatch indirect jump inside a "conventional" bytecode interpreter loop.
18 April 2025
25 May 2024
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Their words
And from what I understand, the research actually shows that they just produce what people want to hear, not necessarily the information that is being looked for.
1 January 2024
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