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๐Ÿš€ Meshy Auto Split is live. Generate or upload a draft model, get printable parts in 40 seconds. Each cut follows the model's natural structure, auto-capped watertight, auto-arranged on the build plate, ready to assemble. Multi-color prints, oversized models, figures. All covered.

167,555 views โ€ข 2 months ago โ€ขvia X (Twitter)

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auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - โ€œimportant discovery per joulesโ€ - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - โœจ introduction to the auto-research infrastructure. โœจ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - whoโ€™s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - โœจ auto-research agent results โœจ - 1:22:53 - โœจ how a big research DAG looks like? โœจ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - whatโ€™s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys ๐ŸŒน

Yacine Mahdid

12,091 views โ€ข 3 months ago

๐ŸšจUpdate! Our new demo is LIVE ๐Ÿšจ In this demo, we walk through the core features of Intelligence Cubed, a next-generation AI model platform built for research, experimentation, and ownership. ๐Ÿ”น 500+ Research Models Intelligence Cubed has grown from 200+ to 506 models, contributed by our expanding Research Fellow Cohort, including researchers, PhDs, and post-docs from Stanford, CMU, Harvard, MIT, and other top U.S. institutions. ๐Ÿ”น Model Cards & Research Transparency Each model is linked to its original research paper and includes a detailed model card outlining its purpose, use cases, category, pricing, market traction, reviews, and public ownership percentage. ๐Ÿ”น 1.2M Public-Owned Models Weโ€™ve introduced Public-Owned Models, with over 1.2 million models available โ€” all fully documented with research papers and comprehensive model cards. ๐Ÿ”น Auto Router Not sure which model to use? Our Auto Router analyzes your question and automatically routes it to the most suitable model. In this demo, it selects an LLM Detection Survey model to answer the query. ๐Ÿ”น Modelverse, Canvas & Workflows Users can explore models in Modelverse, try them instantly, add favorites to cart, and deploy purchased models in Canvas using drag-and-drop to build custom workflows. We also provide professionally curated workflows for immediate hands-on experience. ๐Ÿ‘‰Try Now: #AI #Web3 #AIModel #DeFi #blockchain #LLM #OpenSourceAI #AIxWeb3 #DeAI #IntelligenceCubed

iยณ (Intelligence Cubed)

116,576 views โ€ข 8 months ago