Meta delays "Behemoth" AI model release, per report - axios.com
<a href="https://news.google.com/rss/articles/CBMidkFVX3lxTE8tWEpLRVJSQkV0LTlBUnl0OHpuUS1RNVFNSHlzbzRuNDhQUzNBajA1SDZ6eTBzTzFnb0NkbG9aMzZYTXUybm14QWJrekhHemlDN3pCM09zajZ3bF9RQXhCb21VNkdfNlUtWUF1ZnNjSktUZTJBZ0E?oc=5" target="_blank">Meta delays "Behemoth" AI model release, per report</a> <font color="#6f6f6f">axios.com</font>
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Part 1 of 2 - The psychology, the positioning, and the architecture What if the most powerful thing an AI agent could do was not give you an answer but sit with the contradiction? Image generated by the author using Google Gemini For years, we have trained machines to converge upon the answer, reduce uncertainty, and optimise. However, what cognitive science tells us is something we do not have an easy time believing; namely, that the discomfort arising from simultaneously holding two contradictory beliefs (like the example Leon Festinger referred to as cognitive dissonance back in 1957) serves as one of the most powerful engines of human reasoning. What if we began to build that tension into the architecture of an AI agent , not as multi-agents debating back and forth between one another
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The Cognitive Dissonance Agent: Why the Best AI Reasoning Starts With Self-Doubt
Part 1 of 2 - The psychology, the positioning, and the architecture What if the most powerful thing an AI agent could do was not give you an answer but sit with the contradiction? Image generated by the author using Google Gemini For years, we have trained machines to converge upon the answer, reduce uncertainty, and optimise. However, what cognitive science tells us is something we do not have an easy time believing; namely, that the discomfort arising from simultaneously holding two contradictory beliefs (like the example Leon Festinger referred to as cognitive dissonance back in 1957) serves as one of the most powerful engines of human reasoning. What if we began to build that tension into the architecture of an AI agent , not as multi-agents debating back and forth between one another
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AgentGate is a runtime accountability layer for AI agents: before an agent can execute a high-impact action, it must lock a bond as collateral. Good outcomes release the bond. Bad outcomes slash it. The mechanism makes bad behavior economically irrational. In March 2026, a coordinated swarm of nine AI agents ran 97 attacks against AgentGate. One team — Beta — spent 48 clean bond cycles building reputation and earned nothing for it. Bond capacity was mathematically enforced but not reputation-gated: a brand-new identity could lock the same bond-locking capacity as one with a spotless track record. The original swarm campaign classified this as a governance limitation, not a vulnerability. AgentGate’s core defenses held. Gamma maintained a 100% catch rate across all 38 of its attacks. The ca
Quoting Soohoon Choi
<blockquote cite="https://www.greptile.com/blog/ai-slopware-future"><p>I want to argue that AI models will write good code because of economic incentives. Good code is cheaper to generate and maintain. Competition is high between the AI models right now, and the ones that win will help developers ship reliable features fastest, which requires simple, maintainable code. Good code will prevail, not only because we want it to (though we do!), but because economic forces demand it. Markets will not reward slop in coding, in the long-term.</p></blockquote> <p class="cite">— <a href="https://www.greptile.com/blog/ai-slopware-future">Soohoon Choi</a>, Slop Is Not Necessarily The Future</p> <p>Tags: <a href="https://simonwillison.net/tags/slop">slop</a>, <a href="https://simonwillison.net/ta
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