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Yo en estos momentos al ver los Archivos de Epstein.
La Elite illuminati, judía, usurera, asesina, satanista, caníbal si existe.
Y la voy a exponer hasta el ultimo aliento.
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alemty.eth
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alemty.eth
@alemty_eth
Fundador de Alemty DAO Pionero de Web 3.0, Realidad Aumentada y Computación Espacial. BTC - ETH - AERO - OVR - IOTA 👁️
San Nicolás de los Garza Katılım Ağustos 2021
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Yo esperando esa solución que reemplace Ethereum para escalar proyectos.
Como les gusta decir mamadas, EVM no tiene reemplazo y no parece tenerlo cerca.
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Justin Bons@Justin_Bons
Ethereum helped move crypto forward for the better Be thankful that smart contracts & deflationary economics were invented by ETH years ago Unfortunately, there has been no innovation since then ETH is now dying, so we must move to chains that can support our aspirations! 🔥
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Updates since then:
* Deepseek v4 is out. There *is* a 2-bit quant that can run within 90 GB ( huggingface.co/antirez/deepse… ), and it works, however it's only fast on Apple hardware (I've head ~35 tok/s). On AMD, it's ~7 tok/s. IMO actually taking the effort to properly support more than one hardware manufacturer is a great example of the difference between mere "decentralized AI" and genuine "CROPS AI". I hope we can become better at this.
* github.com/vbuterin/messa… also has alpha telegram support now. However, the path to adding your account is quite janky
* github.com/Luce-Org/luceb… looks promising as a way to run "dense" models (eg. Qwen 27B) more efficiently. It's janky, but on my 5090 laptop it seems to be ~2x more tok/s than llama.cpp
* VoxTerm (local AI recording, no third-party servers) continues to be developed github.com/dmarzzz/VoxTerm
And there's a lot more projects coming on the horizon.
One other thing that has been on my mind is that there's actually a lot of intersection between "CROPS ethereum access layer" and "CROPS AI". For example, we want a ZK way to make (paid) calls to remote LLMs. But if we have this, then it's just as useful for solving another problem: private RPC reads in Ethereum.
Another example: application-specific finetuned LLMs. Leanstral ( mistral.ai/news/leanstral ; I get ~38 tok/s on AMD) fits into < 70 GB, but can hold its own against 1T models on writing Lean code. Things like this are a huge boon for writing more secure code ( vitalik.eth.limo/general/2026/0… ). We should have models finetuned for Ethereum-related use cases as well.
vitalik.eth@VitalikButerin
My self-sovereign / local / private / secure LLM setup, April 2026 vitalik.eth.limo/general/2026/0…
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Oye @grok ¿Qué carajos tienen que soportar los hombres en comparación a nosotras? Se lo más objetivo posible, no te pongas del lado de ellos con justificaciones estúpidas
Guille@guilleconcafe
🚨 ¡Así se vé la mestruación!: Video explica la evolución de la mestruación y lo que "sufren" las mujeres durante ese periodo.
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@Aditya_181105 Deepsek me refactorizó y distribuyó todo el código en partes sencillas en una tarde, y solo me gasté como 0.50 ¢
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La razón por la cual va a ganar deepsek, es porque sus token están en chino simplificado.
Con unos pocos caracteres chinos, ya describieron decenas de palabras y oraciones.
Es como en los speedruns, donde utilizan roms chinas para ahorrar tiempo en los cuadros de diálogo.
Bull Theory@BullTheoryio
THIS IS INSANE 🚨 DeepSeek is now up to 50x CHEAPER than OpenAI and Anthropic for AI tokens. DeepSeek’s latest permanent 75% price cut pushed some inference costs down to fractions of a cent per million tokens. AI companies charge based on input tokens, output tokens, cached tokens and reasoning tokens. 1 BILLION output tokens costs approximately, $3,480 in DeepSeek, $30,000 in OpenAI GPT-5.5 and $15,000 in Claude Sonnet. That’s why enterprises are panicking, A coding agent can burn millions of tokens PER DAY. If 10,000 engineers each consume 10 million AI tokens per day, annual costs could range from just MILLIONS with DeepSeek to BILLIONS with OpenAI or Anthropic models. Microsoft is reportedly cancelling most Claude Code licenses internally and pushing engineers toward its own cheaper GitHub Copilot tools instead, while Uber already used it's entire year AI budget by April due to heavy usage by it's engineers. The smarter AI models become, the longer they think, the more tokens they generate and the more compute they burn. Reasoning AI models secretly generate massive internal tokens before replying, meaning a visible 5,000 token response can actually consume 20,000 to 100,000+ effective reasoning tokens. That’s why OpenAI is pushing mini models, Google is pushing Flash and Anthropic is aggressively optimizing token caching. The biggest problem with AI may not be how smart it is, but how expensive it becomes at scale. In the future, the winners may be the companies that offer good enough AI at the cheapest cost.
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@svmcito Sí, pero la realidad es que se le despide porque su ego no lo soportan.
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@abbyisnotmynam2 Lo lamento mucho, pero eso explica porqué piensas así, tu papá no lo hubiera querido.
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@xalertnow La infidelidad duele, y es comprensible el dolor de quien la sufre.grabar, exponer y humillar a tu pareja en redes no es justicia, es violencia. Esa persona también es madre,hija,ser humano. Lo q vino después es una tragedia q pudo evitarse Su error no justifica 1 cacería publica
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