No, Mythos is not *that* good; its PR is that good.
CULTURE
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Most Jobs Aren’t About Coding or Math Anymore
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most jobs aren’t about coding or math. see my essay in Fortune last week.
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Builders Focus on Creation Over Hype Cycles
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Spot on. The people who actually use the models daily are too busy building to post "IT'S SO OVER" every two weeks haha.
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Knowledge Power Paradox in AI Era
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Knowledge is not power, but it's not exactly not-power either.
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AI impresses non-coders too, selection bias on X
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There are plenty of people who are awed by AI who are not coders, I think the argument that AI impresses programmers most is, in part, selection bias on X, which is heavy on coders and people making fun of non-coders for not getting AI.
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OpenClaw: Non-Technical Users’ First Contact with Agent Models
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Someone recently suggested to me that the reason OpenClaw moment was so big is because it's the first time a large group of non-technical people (who otherwise only knew AI as synonymous with ChatGPT as a website) experienced the latest agentic models. [Translated from EN to English]
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X Algorithm Transparency and Platform Bias Concerns
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The 𝕏 algorithm is open source and updated frequently, but you do not point to any alleged bias or suggest corrections, choosing instead to leave for platforms that everyone knows have a strong bias for political correctness, which just another way of saying “lies”. You used
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The AI Capability Gap: Technical Users vs General Public Understanding
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After building with bleeding edge AI I get this separation that @karpathy lays out deeply. Family and friends have no idea how good the bleeding edge is. Completely uneducated about AI. Andrej Karpathy (@karpathy) Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are. — https://nitter.net/karpathy/status/2042334451611693415#m
→ View original post on X — @scobleizer, 2026-04-09 20:17 UTC
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AI Changing College Classes: Students Sound the Same
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Everyone now kind of sounds the same’: How #AI is changing college classes by Asuka Koda @CNN Learn more: bit.ly/3NWzh9H #ArtificialIntelligence #MachineLearning #ML
→ View original post on X — @ronald_vanloon, 2026-04-09 19:49 UTC
