History has just been written. The first AGI Job with on-chain burn mechanics has been successfully created—marking the emergence of a new economic primitive for autonomous intelligence. Proof : https://
etherscan.io/tx/0x56e959fe2
3d294542ea7b5651c8e303adf13b029a08af50665f2feb986a3f12e
… From execution → to validation → to value
AI
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First AGI Job with On-Chain Burn Mechanics Created
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Gary Marcus Challenges Claims of Near-Zero LLM Hallucination Rates
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This ML Prof told me that the hallucination rate for frontier reasoning LLMs is “next to nil” And then gave me data, only after I pushed him, showing a best-case rate of 4.6% (which of course is benchmark specific). 4.6% is not “next to nil”. Imagine if your accountant hallucinated 4.6% of the time. Or worse, your pilot. Aran Nayebi (@aran_nayebi) Have you had a chance to try the latest reasoning models? You'll see their hallucination rate is next to nil. In fact, there’s a big difference between frontier reasoning models & the base LLMs that're freely available to the public, see e.g. here: nitter.net/aran_nayebi/status/202… — https://nitter.net/aran_nayebi/status/2041249684698648922#m
→ View original post on X — @garymarcus, 2026-04-06 22:25 UTC
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First AGI Job with On-Chain Burn Mechanics Successfully Created
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🔥🔥🔥🔥🔥🔥🔥🔥 MONTREAL.AI (@Montreal_AI) 🔥 History has just been written. The first AGI Job with on-chain burn mechanics has been successfully created—marking the emergence of a new economic primitive for autonomous intelligence. Proof: etherscan.io/tx/0x56e959fe23… From execution → to validation → to value transformation at the protocol level. This is not a feature. This is the beginning of a new economic substrate. 🚀 The AGI workforce is now live—and evolving. #AGIALPHA #AGIJobs #ASIFirst — https://nitter.net/Montreal_AI/status/2041280523306225808#m
→ View original post on X — @ceobillionaire, 2026-04-06 22:23 UTC
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First AGI Job with On-Chain Burn Mechanics Successfully Created
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🔥 History has just been written. The first AGI Job with on-chain burn mechanics has been successfully created—marking the emergence of a new economic primitive for autonomous intelligence. Proof: etherscan.io/tx/0x56e959fe23… From execution → to validation → to value transformation at the protocol level. This is not a feature. This is the beginning of a new economic substrate. 🚀 The AGI workforce is now live—and evolving. #AGIALPHA #AGIJobs #ASIFirst
→ View original post on X — @ceobillionaire, 2026-04-06 22:22 UTC
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Anthropic Secures Multi-Gigawatt TPU Deal
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Anthropic signed an agreement with Google and Broadcom for multiple gigawatts of next-generation TPU capacity. With all what’s coming from Anthropic it feels very much needed. Also, managed 24/7 agents will consume a lot.
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AI Hallucinations Remain Unsolved According to Gary Marcus
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The plot gets wilder: the prof's evidence for hallucinations has been allegedly solved is a chart from OpenAI showing that all models test hallucinated at least 4.6% of the time on known (therefore somewhat gameable) benchmark. That certainly isn't "solved". Imagine if your accountant hallucinated 4.6% of the time. Or your pilot. [Translated from EN to English]
→ View original post on X — @garymarcus, 2026-04-06 22:21 UTC
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Y Combinator Head Dismisses OpenClaw Security Risks
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Wild: the head of Y Combinator seems pretty blind to the security risks in OpenClaw. Garry Tan (@garrytan) I'm telling most everyone I know that they should build a personal OpenClaw — https://nitter.net/garrytan/status/2041267467562193249#m
→ View original post on X — @garymarcus, 2026-04-06 22:19 UTC
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LLM Hallucination Rates Remain Critical Barrier for Production
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Your best data are still a 4.6% hallucination rate, and on a handpicked benchmark at that. Game over. 4.6% isn’t even close to zero. And for many applications that’s deadly. Thanks for playing, and goodbye.
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Anthropic Reaches $30B ARR in Explosive Monthly Growth
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Breaking: Anthropic is now at $30B ARR. Up from $19B in February. That's $11B ARR added in one month. WAT.
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Symbolic Learning vs Curve-Fitting: Reverse-Engineering Generative Programs
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With curve-fitting, you are recording a lossy approximation of the output of some generative program. With symbolic learning, you are losslessly reverse-engineering the source code of the generative program. Symbolic learning won't be the best fit for all problems, but for the ones where the latent program is reasonably simple, it will outperform by many orders of magnitude.