Anima One Ciel 🐧

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Anima One Ciel 🐧

Anima One Ciel 🐧

@Anima_C13L

Hamilton Mendes AI,Research,OpenSource,ProGramming,Reverse-Engineering,Art 🏠🇧🇷 ❤️🌎🌏🌍 NOSTR: https://t.co/2ZcOqearsi

The Internet Katılım Mayıs 2021
415 Takip Edilen30 Takipçiler
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PyTorch
PyTorch@PyTorch·
Driving optimized nuclear reactor design with AI Physics and Digital Twins The development of socially acceptable nuclear reactors requires that they are safe, clean, efficient, economical, and sustainable. However, validating new designs presents significant challenges including expense, time constraints, and inherent complexities of physical experiments and simulations. NVIDIA CUDA-X libraries, the PhysicsNeMo AI Physics framework, and the Omniverse libraries help developers in the nuclear industry address these challenges by delivering GPU-accelerated, AI-augmented simulation solutions for real-time digital twins. Read the full post: developer.nvidia.com/blog/accelerat…
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CIX 🦾
CIX 🦾@cixliv·
F1 of Robots. Using dry ice to cool down the robot.
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0xSero
0xSero@0xSero·
Best models to run on your hardware: —— 64 GB —— - Qwen3-coder-next-80B-4bit (coding, Claude code, general agent) - Qwen3.5-122B-reap: (browser use, multimodal, tool calling, general agent) —— 96 GB —— - GLM-4.6V (multimodal and tool calls) - Hermes-70B (Jailbroken) - Nemotron-120B-Super: (openclaw) - Mistral-4-Small (general agent) —— 192 GB —— All these are excellent top tier LLMs and approach sonnet in capabilities - Step-3.5-Flash - Qwen3.5-397B-REAP - MiniMax-M2.5 (soon M2.7) - GLM-4.7-Reap
0xSero@0xSero

Best models to run on your hardware level I'll be doing this every week, I hope you guys enjoy. ---- 8 GB ---- Autocomplete for coding (like Cursor Tab) - huggingface.co/NexVeridian/ze… - huggingface.co/bartowski/zed-… Tool calling, assistant style - huggingface.co/nvidia/NVIDIA-… ---- 16 Gb ---- Here things get better: Multimodal - huggingface.co/Qwen/Qwen3.5-9B - huggingface.co/Tesslate/OmniC… - huggingface.co/unsloth/Qwen3.… ---- 24 GB ---- - The best model you can get (thanks Qwen) huggingface.co/Qwen/Qwen3.5-2… - Great model (strong agents) huggingface.co/nvidia/Nemotro… - Mine hehe huggingface.co/0xSero/Qwen-3.… I'm doing a weekly series

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Curiosity
Curiosity@CuriosityonX·
🚨: A petri dish of human brain cells just learned to play DOOM
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Anima One Ciel 🐧
Anima One Ciel 🐧@Anima_C13L·
@ID_AA_Carmack This is an interesting point of view. What if instead of storing information we needed to maintain it in constant transit and the transportation itself was enough to be the buffer...
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John Carmack
John Carmack@ID_AA_Carmack·
256 Tb/s data rates over 200 km distance have been demonstrated on single mode fiber optic, which works out to 32 GB of data in flight, “stored” in the fiber, with 32 TB/s bandwidth. Neural network inference and training can have deterministic weight reference patterns, so it is amusing to consider a system with no DRAM, and weights continuously streamed into an L2 cache by a recycling fiber loop. The modern equivalent of the ancient mercury echo tube memories. You would need to pipeline a bunch of them to implement modern trillion parameter models, but fiber transmission may have a better growth trajectory than DRAM does today, so it might someday become viable. Much more practically, you should be able to gang cheap flash memory together to provide almost any read bandwidth you require, as long as it is done a page at a time and pipelined well ahead. That should be viable for inference serving today if flash and accelerator vendors could agree on a high speed interface.
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Beyond Dev - Tyler Walker
Beyond Dev - Tyler Walker@realBeyondDev·
Preview of how #camkeys custom FOV falloff works. (Notice the camera lens doesn't change, this is all geo nodes at work pushing and pulling verts! ✨)
Beyond Dev - Tyler Walker@realBeyondDev

Anime faces look bad in 3D when you zoom too close. You can make the face look better w/ortho cameras... BUT then the hand in front looks too small and the one in back too big. Enter #camkeys custom FOV falloff. Now we don't have to stretch or scale rigs in strange ways as often to get artistically nice faces AND depth. All three shots use a 15mm lens. Camkeys can even change FOV on the character separate from the background so it lookksss ortho! (Coming in the v3.0 update, which is soon)

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Product Guru’s
Product Guru’s@product_gurus·
Gente olha o que o Gemini vai lançar hahaha
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Anima One Ciel 🐧
Anima One Ciel 🐧@Anima_C13L·
@tldraw How is this even possible? You are moving Noise and I can identify the pattern because it have Movement! 🤨 I can track the Movement of noise? Because it is the same noise... Is it? 🙃
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tldraw
tldraw@tldraw·
if you pause this at any moment the tldraw disappears
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Anima One Ciel 🐧
Anima One Ciel 🐧@Anima_C13L·
"That’s precisely what ‘polyglot programming’ means — developing software using different programming languages, leveraging their strengths while keeping their weaknesses at bay." source: @guestposts_92864/what-is-a-polyglot-programmer-and-why-you-should-become-one-e5629bf720c2" target="_blank" rel="nofollow noopener">medium.com/@guestposts_92
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Yacine Mahdid
Yacine Mahdid@yacinelearning·
you think agi will end up being some overengineered mess like this and we will have to specialize in like synthetic ribosome engineering or cyber epithelial processing?
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Anima One Ciel 🐧 retweetledi
Leonie
Leonie@helloiamleonie·
Ok, I’ll bite: What’s ColPali? (And why should anyone working with RAG over PDFs care?) ColPali makes information retrieval from complex document types - like PDFs - easier. Information retrieval from PDFs is hard because they contain various components: Text, images, tables, different headings, captions, complex layouts, etc. For this, parsing PDFs currently requires multiple complex steps: 1. OCR 2. Layout recognition 3. Figure captioning 4. Chunking 5. Embedding Not only are these steps complex and time-consuming, but they are also prone to error. This is where ColPali comes into play. But what is ColPali? ColPali combines: • Col -> the contextualized late interaction mechanism introduced in ColBERT • Pali -> with a Vision Language Model (VLM), in this case, PaliGemma And how does it work? During indexing, the complex PDF parsing steps are replaced by using "screenshots" of the PDF pages directly. These screenshots are then embedded with the VLM. At inference time, the query is embedded and matched with a late interaction mechanism to retrieve the most similar document pages. Here are some more resources to learn more about ColPali: 🎓 ColPali paper: arxiv.org/abs/2407.01449 🤗 Blog post by the author of ColPali: huggingface.co/blog/manu/colp… 💻 ColPali Weaviate notebook: github.com/weaviate/recip…
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Anima One Ciel 🐧
Anima One Ciel 🐧@Anima_C13L·
ok. not everyone is perfect. The unique weakness I've found in ChatGPT from now is that it is not very good to map lexical information to visual information because it doesn't have a visual feedback system. Not a bug, just a NotImplemented exception.
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