Загрузка видео...

Не удалось загрузить видео

На главную

Check out mistral.​rs, our #Rust-based open source inference engine allowing for fast #LLM serving for a variety of architectures including X-LoRA mixture-of-expert (MoE) models, Llama-3, Mistral/Mixtral, Gemma & many others. Built on the Hugging Face #Candle framework for #Rust w/ custom CUDA kernels in the backend (as well as...

ProfBuehlerMIT's profile picture

Markus J. Buehler

20,647 subscribers

73,581 просмотров • 2 лет назад •via X (Twitter)

ОбразованиеНаука и технологии#Rust#LLM#Candle#Rust
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 10

Фото профиля nicoarq
nicoarq2 лет назад

@huggingface This is really rly cool! I thought it was only limited to Mistral related LLMs though maybe it's not too late to change the name to something generic to not confuse people?

Фото профиля Markus J. Buehler
Markus J. Buehler2 лет назад

@huggingface Yes, the tool with with many architectures, including Llama-3, Phi-3 (up to 128k context), Mistral/Mixtral, Gemma, X-LoRA mixture-of-expert (MoE) models, & many others. Also features in-situ quantization to any level.

Фото профиля Ingo Villnow (DM5DK) 🇺🇦🌻
Ingo Villnow (DM5DK) 🇺🇦🌻2 лет назад

@huggingface Awesome! More apps like this written in Rust 😍

Фото профиля Jonathan Eisenzopf
Jonathan Eisenzopf2 лет назад

@huggingface This looks really cool. I’ve been trying to figure out optimal branching in rust candle. Nice job.

Фото профиля Thomas Wolf
Thomas Wolf2 лет назад

@huggingface wow!

Фото профиля PΔBLØ ᄃΞ
PΔBLØ ᄃΞ2 лет назад

@huggingface 🦀

Фото профиля Alexander Ocsa
Alexander Ocsa2 лет назад

@huggingface Does it support GPU hardware acceleration ?

Фото профиля Nicolas Patry
Nicolas Patry2 лет назад

@huggingface Impressive work !

Фото профиля Markus J. Buehler
Markus J. Buehler2 лет назад

@huggingface Thank you!

Фото профиля iandanforth 🦋 @iandanforth.bsky.social
iandanforth 🦋 @iandanforth.bsky.social2 лет назад

@_philschmid @huggingface Is this affiliated with Mistral AI? The name certainly implies it.

Похожие видео

We’re excited to officially launch LlamaParse, the first genAI-native document parsing solution. Not only is it better at parsing out images/tables/charts 📊📈 than virtually every other parser, it is now steerable through natural language instructions - output the document in whatever format you desire! It is also the only parsing solution that seamlessly allows you to build accurate RAG over complex documents, free of hallucinations 🔥 We launched it in private preview a few weeks ago and hit 2k users, 1M total PDF pages parsed. And now it’s better than ever. LlamaParse contains the following killer features: ✅ SOTA table/chart extraction ✅ Seamless integration with LlamaIndex 🦙 advanced RAG/agents ✅✨ Natural language Parsing Instructions ✅✨JSON mode and image extraction ✅✨Support for ~10 document types (.pdf, .pptx, .docx, .xml) and more Our pricing is simple: 1k free per day, and additional pages at 0.3c a page, or $3 for 1k pages. If you want advanced document RAG and/or private deployments, come get in touch with us to chat about LlamaCloud. Check out our full blog post here: LlamaParse client repo: Signup at 🦙☁️: Come talk to us:
5:07

We’re excited to officially launch LlamaParse, the first genAI-native document parsing solution. Not only is it better at parsing out images/tables/charts 📊📈 than virtually every other parser, it is now steerable through natural language instructions - output the document in whatever format you desire! It is also the only parsing solution that seamlessly allows you to build accurate RAG over complex documents, free of hallucinations 🔥 We launched it in private preview a few weeks ago and hit 2k users, 1M total PDF pages parsed. And now it’s better than ever. LlamaParse contains the following killer features: ✅ SOTA table/chart extraction ✅ Seamless integration with LlamaIndex 🦙 advanced RAG/agents ✅✨ Natural language Parsing Instructions ✅✨JSON mode and image extraction ✅✨Support for ~10 document types (.pdf, .pptx, .docx, .xml) and more Our pricing is simple: 1k free per day, and additional pages at 0.3c a page, or $3 for 1k pages. If you want advanced document RAG and/or private deployments, come get in touch with us to chat about LlamaCloud. Check out our full blog post here: LlamaParse client repo: Signup at 🦙☁️: Come talk to us:

LlamaIndex 🦙

143,136 просмотров • 2 лет назад

A team tested Pi0, Pi0 Fast, Gr00t, and ACT on real robot arms in manufacturing tasks. (🔖 Bookmark this for later!) The task was precise: place thin rectangular frames from a messy stack into a holder. The team fine-tuned each model on 100 real trajectories and compared training time, inference speed, motion quality, and success rates. ⬇️ Here’s a breakdown of what they found Pi0 (Original) ✅ Strongest overall performance in precise pick-and-place ✅ High success rate even in edge cases ✅ Longest training time (~11 hours, ~$30 per run) ✅ Inference time of 80 ms causes short pauses between actions Despite delays, it handles complex scenarios well… solid for high-precision tasks, but slow to train. Gr00t ✅ Trains fast (~2 hours, ~$5 per run) ✅ Performs almost as well as Pi0 on large-object tasks ✅ Struggles with fine precision; random movement in some trials ✅ More training didn’t fix jitter or random offsets Best suited for tasks where exact precision isn’t critical. Not ready for manufacturing-grade accuracy without more tuning. Pi0 Fast ✅ Promised faster training, but results were underwhelming ✅ Training at 6 hours still showed low success rates ✅ Inference was slower than expected ✅ Not reliable for generalizing even slightly new tasks Currently too unstable for real-world deployment. Doesn’t live up to the “Fast” name yet. ACT (Baseline) ✅ 200MB model—lightweight, but limited ✅ Struggles with stacked objects or ambiguous scenes ✅ Success rates around 70% in best-case setups ✅ Can’t match newer models on precision or generalization Still a solid baseline, but clearly a generation behind in robustness. 🚨 Extra Notes All newer models share a common issue: •Inference takes longer than a frame (80 ms vs 33 ms), so robots “pause” between chunks. •This results in jittery movements, but not a dealbreaker unless tasks are time-sensitive. Language-conditioned tasks also fell short: after training on two labeled tasks, the model couldn’t generalize to a third unseen combination using only text prompts. ✅ The good news? These models adapt well to new robot arms with quick fine-tuning. ❌ The bad news? There’s still no plug-and-play solution for improving performance after deployment. Reinforcement learning or DAgger-style data collection during real-world operation may be the next big step, something many teams in robotics are actively working on.
0:51

A team tested Pi0, Pi0 Fast, Gr00t, and ACT on real robot arms in manufacturing tasks. (🔖 Bookmark this for later!) The task was precise: place thin rectangular frames from a messy stack into a holder. The team fine-tuned each model on 100 real trajectories and compared training time, inference speed, motion quality, and success rates. ⬇️ Here’s a breakdown of what they found Pi0 (Original) ✅ Strongest overall performance in precise pick-and-place ✅ High success rate even in edge cases ✅ Longest training time (~11 hours, ~$30 per run) ✅ Inference time of 80 ms causes short pauses between actions Despite delays, it handles complex scenarios well… solid for high-precision tasks, but slow to train. Gr00t ✅ Trains fast (~2 hours, ~$5 per run) ✅ Performs almost as well as Pi0 on large-object tasks ✅ Struggles with fine precision; random movement in some trials ✅ More training didn’t fix jitter or random offsets Best suited for tasks where exact precision isn’t critical. Not ready for manufacturing-grade accuracy without more tuning. Pi0 Fast ✅ Promised faster training, but results were underwhelming ✅ Training at 6 hours still showed low success rates ✅ Inference was slower than expected ✅ Not reliable for generalizing even slightly new tasks Currently too unstable for real-world deployment. Doesn’t live up to the “Fast” name yet. ACT (Baseline) ✅ 200MB model—lightweight, but limited ✅ Struggles with stacked objects or ambiguous scenes ✅ Success rates around 70% in best-case setups ✅ Can’t match newer models on precision or generalization Still a solid baseline, but clearly a generation behind in robustness. 🚨 Extra Notes All newer models share a common issue: •Inference takes longer than a frame (80 ms vs 33 ms), so robots “pause” between chunks. •This results in jittery movements, but not a dealbreaker unless tasks are time-sensitive. Language-conditioned tasks also fell short: after training on two labeled tasks, the model couldn’t generalize to a third unseen combination using only text prompts. ✅ The good news? These models adapt well to new robot arms with quick fine-tuning. ❌ The bad news? There’s still no plug-and-play solution for improving performance after deployment. Reinforcement learning or DAgger-style data collection during real-world operation may be the next big step, something many teams in robotics are actively working on.

Ilir Aliu

21,844 просмотров • 1 год назад

We’re excited to announce the release and open-source of HunyuanImage 3.0 — the largest and most powerful open-source text-to-image model to date, with over 80 billion total parameters, of which 13 billion are activated per token during inference.The effect is completely comparable to the industry’s flagship closed-source model.🚀🚀🚀 HunyuanImage 3.0 originates from our internally developed native multimodal large language model, with fine-tuning and post-training focused on text-to-image generation. This unique foundation gives the model a powerful set of capabilities: ✅Reason with world knowledge ✅Understand complex, thousand-word prompts ✅Generate precise text within images Different from traditional DiT architecture image generation models, HunyuanImage 3.0’s MoE architecture uses a Transfusion-based approach to deeply couple Diffusion and LLM training for a single, powerful system. Built on Hunyuan-A13B, HunyuanImage 3.0 was trained on a massive dataset: 5 billion image-text pairs, video frames, interleaved image-text data, and 6 trillion tokens of text corpora. This hybrid training across multimodal generation, understanding, and LLM capabilities allows the model to seamlessly integrate multiple tasks. Whether you're an illustrator, designer, or creator, this is built to slash your workflow from hours to minutes. HunyuanImage 3.0 can generate intricate text, detailed comics, expressive emojis, and lively, engaging illustrations for educational content. The current release focuses solely on text-to-image generation and future updates will include image-to-image, image editing, multi-turn interaction, and more. 👉🏻Try it now: 🔗GitHub: 🤗Hugging Face:
2:09

We’re excited to announce the release and open-source of HunyuanImage 3.0 — the largest and most powerful open-source text-to-image model to date, with over 80 billion total parameters, of which 13 billion are activated per token during inference.The effect is completely comparable to the industry’s flagship closed-source model.🚀🚀🚀 HunyuanImage 3.0 originates from our internally developed native multimodal large language model, with fine-tuning and post-training focused on text-to-image generation. This unique foundation gives the model a powerful set of capabilities: ✅Reason with world knowledge ✅Understand complex, thousand-word prompts ✅Generate precise text within images Different from traditional DiT architecture image generation models, HunyuanImage 3.0’s MoE architecture uses a Transfusion-based approach to deeply couple Diffusion and LLM training for a single, powerful system. Built on Hunyuan-A13B, HunyuanImage 3.0 was trained on a massive dataset: 5 billion image-text pairs, video frames, interleaved image-text data, and 6 trillion tokens of text corpora. This hybrid training across multimodal generation, understanding, and LLM capabilities allows the model to seamlessly integrate multiple tasks. Whether you're an illustrator, designer, or creator, this is built to slash your workflow from hours to minutes. HunyuanImage 3.0 can generate intricate text, detailed comics, expressive emojis, and lively, engaging illustrations for educational content. The current release focuses solely on text-to-image generation and future updates will include image-to-image, image editing, multi-turn interaction, and more. 👉🏻Try it now: 🔗GitHub: 🤗Hugging Face:

Tencent Hy

412,658 просмотров • 10 месяцев назад

We are excited to share #PDF2Audio, an open-source alternative to the #podcast feature of #NotebookLM with flexibility & tailored outputs that you can precisely control in the app: You can make a podcast, lecture, discussions, short/long form summaries & more, including the use of the amazing🍓o1 model (Sam Altman OpenAI: with stunning results!). Code & HF Space: You can find #PDF2Audio on GitHub for local use or try the Hugging Face space, all featuring Gradio. Link to the repo & HF space in the reply. Thank you @knowsuchagencyfor the great work on #promptic and #pdf2podcast, as well as LiteLLM (YC W23), & AK for helping us with the Hugging Face spaces version. We hope that this tool is useful for the community. Background: Developing audio podcasts, lectures, & summaries from complex documents & data has become an exciting trend with impacts from research to education to business. Our open-source #PDF2Audio tool that allows users to utilize various models such as #o1 or local/open-source models, to develop deep-dives into technical content. Example application - material design analysis: As an example to show what the system can do, check the video for a detailed 13-minute analysis of one of the designs created by #SciAgents merging silk & dandelion pigments, created using 🍓o1. The conversation describes the new material, an integration of silk proteins & luteolin/dandelion pigments to create a new biomaterial. Silk, a natural #nanostructured protein-based fiber known for its strength & flexibility, is combined with dandelion pigments like luteolin, which offer unique optical properties. By merging these components at the nanoscale level, the resulting material displays structural coloration—vibrant, tunable colors created by the material's structure rather than synthetic dyes, and leverages silk's hierarchical organization as a scaffold for the pigments, ensuring uniform distribution and non-covalent bonding at the molecular level. Key technical features include: ➡️Low-temperature processing to maintain the integrity of both silk and pigments while reducing energy consumption by 30%. ➡️Enhanced mechanical properties, with tensile strength up to 1.5 gigapascals. ➡️Potential self-healing capabilities and environmental responsiveness, allowing the material to repair minor damage and change color based on environmental conditions. ➡️UV protection and antimicrobial properties, which make this material ideal for smart textiles, eco-friendly coatings, and medical applications. This development opens new doors for sustainable materials, offering an eco-friendly alternative to synthetic fibers with applications in various industries, from fashion to healthcare.
13:05

We are excited to share #PDF2Audio, an open-source alternative to the #podcast feature of #NotebookLM with flexibility & tailored outputs that you can precisely control in the app: You can make a podcast, lecture, discussions, short/long form summaries & more, including the use of the amazing🍓o1 model (Sam Altman OpenAI: with stunning results!). Code & HF Space: You can find #PDF2Audio on GitHub for local use or try the Hugging Face space, all featuring Gradio. Link to the repo & HF space in the reply. Thank you @knowsuchagencyfor the great work on #promptic and #pdf2podcast, as well as LiteLLM (YC W23), & AK for helping us with the Hugging Face spaces version. We hope that this tool is useful for the community. Background: Developing audio podcasts, lectures, & summaries from complex documents & data has become an exciting trend with impacts from research to education to business. Our open-source #PDF2Audio tool that allows users to utilize various models such as #o1 or local/open-source models, to develop deep-dives into technical content. Example application - material design analysis: As an example to show what the system can do, check the video for a detailed 13-minute analysis of one of the designs created by #SciAgents merging silk & dandelion pigments, created using 🍓o1. The conversation describes the new material, an integration of silk proteins & luteolin/dandelion pigments to create a new biomaterial. Silk, a natural #nanostructured protein-based fiber known for its strength & flexibility, is combined with dandelion pigments like luteolin, which offer unique optical properties. By merging these components at the nanoscale level, the resulting material displays structural coloration—vibrant, tunable colors created by the material's structure rather than synthetic dyes, and leverages silk's hierarchical organization as a scaffold for the pigments, ensuring uniform distribution and non-covalent bonding at the molecular level. Key technical features include: ➡️Low-temperature processing to maintain the integrity of both silk and pigments while reducing energy consumption by 30%. ➡️Enhanced mechanical properties, with tensile strength up to 1.5 gigapascals. ➡️Potential self-healing capabilities and environmental responsiveness, allowing the material to repair minor damage and change color based on environmental conditions. ➡️UV protection and antimicrobial properties, which make this material ideal for smart textiles, eco-friendly coatings, and medical applications. This development opens new doors for sustainable materials, offering an eco-friendly alternative to synthetic fibers with applications in various industries, from fashion to healthcare.

Markus J. Buehler

208,353 просмотров • 1 год назад

One of the biggest airdrops in Hedera history?👀🪂🌱 Hey! If you've made it to this tweet as fast as Hedera's TPS, bookmark it so you don't miss any details While $IVY skydives with some friends in the video, let's check out the conditions: 🔥First of all, let's talk about how it works🔥 The airdrop will have a tier system where you could get more or less IVY depending on your tier. There will be four different tiers, and we'll announce the conditions for each tier to move up. Here's how much you can get: ➡️Tier 1: 20,000 IVY tokens ➡️Tier 2: 40,000 IVY tokens ➡️Tier 3: 60,000 IVY tokens ➡️Tier 4: 80,000 IVY tokens 🧐How do you qualify for each tier? We can't spill all the beans now, but we'll give you the conditions on our X account. For now, IVY can give you a sneak peek: ✅Tier 1 Requirements: Hold all 3 NFTs ✅Tier 2 Requirements: Tier 1 + hold $100 worth of IVY tokens (at time of snapshot) ✅Tier 3 Requirements: Tier 1 + Tier 2 + ???? ✅Tier 4 Requirements: Tier 1 + Tier 2 + Tier 3 + ???? 📸Chill out! The snapshot hasn't been taken yet, so you'll know in due time what to do to aim for the higher tiers. And to wrap it up... we'll be giving away 3 NFT sets (1 set = NFT 1 + NFT 2 + NFT 3) to those who comment and retweet this tweet, tagging 3 friends 🌱🫂 Good luck! We'll announce the winners on Monday. 🚨Oh... one more IMPORTANT detail...🚨 If you have multiple NFT sets, your IVY amount multiplies. For example, if you have 3 complete sets of NFTs and qualify as Tier 3, your amount of IVY to receive in the airdrop will be: ➡️Tier 3 = 60,000 IVY tokens ✅NFT Sets = 3 Total airdrop amount = 60,000 IVY tokens x 3 sets = 180,000 IVY tokens The maximum multiplier will be x5 If you have any questions, you can hop into our Telegram channel to ask ❓ You'll find the link in the linktree on our X account See yaa, $IVY fam Built on Hedera.
0:43

One of the biggest airdrops in Hedera history?👀🪂🌱 Hey! If you've made it to this tweet as fast as Hedera's TPS, bookmark it so you don't miss any details While $IVY skydives with some friends in the video, let's check out the conditions: 🔥First of all, let's talk about how it works🔥 The airdrop will have a tier system where you could get more or less IVY depending on your tier. There will be four different tiers, and we'll announce the conditions for each tier to move up. Here's how much you can get: ➡️Tier 1: 20,000 IVY tokens ➡️Tier 2: 40,000 IVY tokens ➡️Tier 3: 60,000 IVY tokens ➡️Tier 4: 80,000 IVY tokens 🧐How do you qualify for each tier? We can't spill all the beans now, but we'll give you the conditions on our X account. For now, IVY can give you a sneak peek: ✅Tier 1 Requirements: Hold all 3 NFTs ✅Tier 2 Requirements: Tier 1 + hold $100 worth of IVY tokens (at time of snapshot) ✅Tier 3 Requirements: Tier 1 + Tier 2 + ???? ✅Tier 4 Requirements: Tier 1 + Tier 2 + Tier 3 + ???? 📸Chill out! The snapshot hasn't been taken yet, so you'll know in due time what to do to aim for the higher tiers. And to wrap it up... we'll be giving away 3 NFT sets (1 set = NFT 1 + NFT 2 + NFT 3) to those who comment and retweet this tweet, tagging 3 friends 🌱🫂 Good luck! We'll announce the winners on Monday. 🚨Oh... one more IMPORTANT detail...🚨 If you have multiple NFT sets, your IVY amount multiplies. For example, if you have 3 complete sets of NFTs and qualify as Tier 3, your amount of IVY to receive in the airdrop will be: ➡️Tier 3 = 60,000 IVY tokens ✅NFT Sets = 3 Total airdrop amount = 60,000 IVY tokens x 3 sets = 180,000 IVY tokens The maximum multiplier will be x5 If you have any questions, you can hop into our Telegram channel to ask ❓ You'll find the link in the linktree on our X account See yaa, $IVY fam Built on Hedera.

IVY

33,729 просмотров • 1 год назад

We are going big this year and into next! 2026 will be a Radiant year. The Radiant community is thrilled to announce the release of TWO MAJOR open-source projects that showcase the power of collaborative development: Radiant-Core Node! RXinDexer! 1. Radiant-Core Node 2.0-Project Phoenix Our next-generation blockchain node implementation! 🔗 -Built with modern C++20, Radiant-Core represents a significant evolution in our blockchain infrastructure: -Enhanced Performance: Support for transactions up to 12 MB with ~81,000 inputs. -Developer-Friendly: Native Prometheus metrics endpoint for easy monitoring. -Flexible Configuration: Node profiles from the system design paper (archive/agent/mining) for different use cases. -Glyph Token Swap Protocol Upgrades: **On-chain** broadcasted atomic swaps via PSRT. The classic advertised swaps will still work but this ushers in a whole new era for DEX and atomic swaps on-chain. -Cross-Platform: Docker-based CI for consistent builds across Linux, macOS, and Windows. -Available soon on testnet and aiming for mainnet activation by block 400k. 2. RXinDexer - Blockchain indexer for RXD and Glyphs 🔗 -A powerful Python-based indexing solution that enables developers to efficiently query and track Radiant blockchain data and Glyph assets. These releases embody our commitment to transparent, community-driven development. Open source isn't just about code—it's about empowering developers worldwide to: ✅ Contribute improvements and innovations ✅ Audit security and verify integrity ✅ Build complementary tools and services ✅ Learn from production-grade blockchain implementations Get Involved! Whether you're a seasoned blockchain developer or just getting started, your contributions matter. Check out the repositories, review the documentation, submit issues, or contribute code. You are the Proof of Work! And together, we're building the future of decentralized digital value transfer and sound money with no trusted third parties. The way Satoshi intended. The future is Radiant!
2:41

We are going big this year and into next! 2026 will be a Radiant year. The Radiant community is thrilled to announce the release of TWO MAJOR open-source projects that showcase the power of collaborative development: Radiant-Core Node! RXinDexer! 1. Radiant-Core Node 2.0-Project Phoenix Our next-generation blockchain node implementation! 🔗 -Built with modern C++20, Radiant-Core represents a significant evolution in our blockchain infrastructure: -Enhanced Performance: Support for transactions up to 12 MB with ~81,000 inputs. -Developer-Friendly: Native Prometheus metrics endpoint for easy monitoring. -Flexible Configuration: Node profiles from the system design paper (archive/agent/mining) for different use cases. -Glyph Token Swap Protocol Upgrades: **On-chain** broadcasted atomic swaps via PSRT. The classic advertised swaps will still work but this ushers in a whole new era for DEX and atomic swaps on-chain. -Cross-Platform: Docker-based CI for consistent builds across Linux, macOS, and Windows. -Available soon on testnet and aiming for mainnet activation by block 400k. 2. RXinDexer - Blockchain indexer for RXD and Glyphs 🔗 -A powerful Python-based indexing solution that enables developers to efficiently query and track Radiant blockchain data and Glyph assets. These releases embody our commitment to transparent, community-driven development. Open source isn't just about code—it's about empowering developers worldwide to: ✅ Contribute improvements and innovations ✅ Audit security and verify integrity ✅ Build complementary tools and services ✅ Learn from production-grade blockchain implementations Get Involved! Whether you're a seasoned blockchain developer or just getting started, your contributions matter. Check out the repositories, review the documentation, submit issues, or contribute code. You are the Proof of Work! And together, we're building the future of decentralized digital value transfer and sound money with no trusted third parties. The way Satoshi intended. The future is Radiant!

Radiant Blockchain

41,416 просмотров • 7 месяцев назад

🚀 Introducing PantheonOS ( A Fully Open-Source Agent OS for Science PantheonOS began as a research project in my Stanford lab and has since evolved into a vision to redefine data science in the era of AI—starting with computational biology, especially single-cell and spatial genomics. PantheonOS is a general agent platform built from the ground up. It is arguably the first distributed agent framework designed for scientific data analysis. 🔑 Key Features 1. Multi-Agent Collaboration – Built-in paradigms for distributed, cross-machine cooperation among agents and toolsets. 2. Native Toolset Support – Python, R, Julia, LaTeX, and more—designed for real scientific workflows. 3. Modular & Extensible – Developer-friendly design with shallow wrappers, plus LLM-driven toolset generation. 4. Evolvable Agents – Capable of evolving large-scale code projects to achieve superhuman performance (e.g., evolving upon the original Harmony [I Korsunsky, 2019, Nature Biotechnology] and Scanorama [BL Hie, 2019, Nature Biotechnology] implementations), and even evolving the system itself to adapt to new fields. 🎉 Stepwise Release Strategy We’re releasing PantheonOS in stages: Pantheon-CLI (today!), followed by Pantheon-Lab, Pantheon-Notebook, Pantheon-Slack, and more. 🌟 Pantheon-CLI Highlights - We're not just building another CLI tool. We're defining how scientists will interact with data in the AI era. - Open, Powerful, Python-First – The first fully open-source, endlessly extendable scientific “vibe analysis” framework. - Mixed Programming Magic – Combine Python, natural language, R, or Julia—seamlessly in the same environment. - PhD-Level Assistant – A command-line agent for complex real-world genomics and beyond, handling workflows at the PhD level. - Privacy by Design – Run entirely offline with local LLMs—your data never leaves your computer. ✅ Proven Applications (10 Demonstrations) Computational biology: 1. ATAC-seq: From raw reads to peak matrix 2. RNA-seq: From raw reads to expression matrix 3. Complex single-cell workflows (PhD-level) 4. Hybrid natural language + R for Seurat annotation 5. Learning from web tutorials + invoking single-cell foundation models 6. Cell segmentation on 10x Genomics HD Visium data And beyond: 7. Mixed Python & R programming examples 8. Molecular docking & structural analysis 9. Exploratory factor analysis for behavioral survey data 10. Customer segmentation & finance analytics 🌐 Learn More & Get Started Website: Pantheon-CLI Documentation: GitHub Repo: 💬 Join our community: PantheonOS Slack: PantheonOS Discord:
1:16

🚀 Introducing PantheonOS ( A Fully Open-Source Agent OS for Science PantheonOS began as a research project in my Stanford lab and has since evolved into a vision to redefine data science in the era of AI—starting with computational biology, especially single-cell and spatial genomics. PantheonOS is a general agent platform built from the ground up. It is arguably the first distributed agent framework designed for scientific data analysis. 🔑 Key Features 1. Multi-Agent Collaboration – Built-in paradigms for distributed, cross-machine cooperation among agents and toolsets. 2. Native Toolset Support – Python, R, Julia, LaTeX, and more—designed for real scientific workflows. 3. Modular & Extensible – Developer-friendly design with shallow wrappers, plus LLM-driven toolset generation. 4. Evolvable Agents – Capable of evolving large-scale code projects to achieve superhuman performance (e.g., evolving upon the original Harmony [I Korsunsky, 2019, Nature Biotechnology] and Scanorama [BL Hie, 2019, Nature Biotechnology] implementations), and even evolving the system itself to adapt to new fields. 🎉 Stepwise Release Strategy We’re releasing PantheonOS in stages: Pantheon-CLI (today!), followed by Pantheon-Lab, Pantheon-Notebook, Pantheon-Slack, and more. 🌟 Pantheon-CLI Highlights - We're not just building another CLI tool. We're defining how scientists will interact with data in the AI era. - Open, Powerful, Python-First – The first fully open-source, endlessly extendable scientific “vibe analysis” framework. - Mixed Programming Magic – Combine Python, natural language, R, or Julia—seamlessly in the same environment. - PhD-Level Assistant – A command-line agent for complex real-world genomics and beyond, handling workflows at the PhD level. - Privacy by Design – Run entirely offline with local LLMs—your data never leaves your computer. ✅ Proven Applications (10 Demonstrations) Computational biology: 1. ATAC-seq: From raw reads to peak matrix 2. RNA-seq: From raw reads to expression matrix 3. Complex single-cell workflows (PhD-level) 4. Hybrid natural language + R for Seurat annotation 5. Learning from web tutorials + invoking single-cell foundation models 6. Cell segmentation on 10x Genomics HD Visium data And beyond: 7. Mixed Python & R programming examples 8. Molecular docking & structural analysis 9. Exploratory factor analysis for behavioral survey data 10. Customer segmentation & finance analytics 🌐 Learn More & Get Started Website: Pantheon-CLI Documentation: GitHub Repo: 💬 Join our community: PantheonOS Slack: PantheonOS Discord:

evo-devo

17,369 просмотров • 11 месяцев назад

$ICP technical analysis update - the breakout is playing out EXACTLY as we want. remember when I said we were breaking a 4-year descending wedge at $7.64? we did. now we're at $6.20 with a massive 457K volume spike. wait, but why is the price is DOWN? this is EXACTLY what you want to see. we broke out to ~$10. shorts got liquidated. profit-taking hit. weak hands shook out. this pullback to $6.20 is a RETEST of the breakout level. that volume spike is ABSORPTION. smart money is loading this dip. the best breakouts ALWAYS retest before the real move. the pattern is still intact: descending wedge breakout confirmed ✅ proved we can break above $8-9 ✅ now retesting support at $6-7 ✅ textbook. old resistance becomes new support. we consolidate for a bit and then we run. fibonacci targets unchanged: $16 (0.236 fib) - first stop $24 (0.382 fib) - psychological break $38 (0.618 fib) - acceleration zone $59-93 (full retracement) - primary target when it runs, it runs FAST. $6 to $16 happens in days. $16 to $24 happens even faster. why this pullback is bullish: every major breakout retests. $BTC retests every level. $ETH retested $200 before $4,800. $SOL retested $20 before $260. $ZEC also retested many times. $ICP just retested at $6. this is your entry. fundamentals unchanged: Caffeine Web3 mode Q1 2026 ✅ Swiss incorporation complete ✅ Half of ICP locked ✅ All VC's fully vested ✅ AI market → $5-10 TRILLION ✅ Only blockchain that can host full end-to-end apps on chain ✅ nothing changed except you got a better price. risk/reward right now: risk: retest $5 (19% downside) reward: $16-59 (160-850% upside) 8:1 to 45:1 risk/reward. the chart is screaming: 4-year base complete ✅ wedge breakout confirmed ✅ volume absorption NOW ✅ fib levels mapped ✅ everything aligned. fortunes are made when it's -8% and people are doubting. not when it's +300% and trending. you're either buying this retest at $6. or watching it run to $16-59 without you. this is the trade. DONT FK IT UP ~spec
0:33

$ICP technical analysis update - the breakout is playing out EXACTLY as we want. remember when I said we were breaking a 4-year descending wedge at $7.64? we did. now we're at $6.20 with a massive 457K volume spike. wait, but why is the price is DOWN? this is EXACTLY what you want to see. we broke out to ~$10. shorts got liquidated. profit-taking hit. weak hands shook out. this pullback to $6.20 is a RETEST of the breakout level. that volume spike is ABSORPTION. smart money is loading this dip. the best breakouts ALWAYS retest before the real move. the pattern is still intact: descending wedge breakout confirmed ✅ proved we can break above $8-9 ✅ now retesting support at $6-7 ✅ textbook. old resistance becomes new support. we consolidate for a bit and then we run. fibonacci targets unchanged: $16 (0.236 fib) - first stop $24 (0.382 fib) - psychological break $38 (0.618 fib) - acceleration zone $59-93 (full retracement) - primary target when it runs, it runs FAST. $6 to $16 happens in days. $16 to $24 happens even faster. why this pullback is bullish: every major breakout retests. $BTC retests every level. $ETH retested $200 before $4,800. $SOL retested $20 before $260. $ZEC also retested many times. $ICP just retested at $6. this is your entry. fundamentals unchanged: Caffeine Web3 mode Q1 2026 ✅ Swiss incorporation complete ✅ Half of ICP locked ✅ All VC's fully vested ✅ AI market → $5-10 TRILLION ✅ Only blockchain that can host full end-to-end apps on chain ✅ nothing changed except you got a better price. risk/reward right now: risk: retest $5 (19% downside) reward: $16-59 (160-850% upside) 8:1 to 45:1 risk/reward. the chart is screaming: 4-year base complete ✅ wedge breakout confirmed ✅ volume absorption NOW ✅ fib levels mapped ✅ everything aligned. fortunes are made when it's -8% and people are doubting. not when it's +300% and trending. you're either buying this retest at $6. or watching it run to $16-59 without you. this is the trade. DONT FK IT UP ~spec

𝙯𝙨𝙥𝙚𝙘

37,902 просмотров • 8 месяцев назад

After 8+ years on the Tesla Autopilot team and 3 years at Intel, I started Apex Compute to design a new architecture for efficient AI inference. For the past 9 months, we’ve been building our custom inference accelerator. Today we’re releasing Unified Engine v1. Last June we raised our seed round with Maxitech , DeepFin Research, Soma Capital and an incredible group of angel investors. In less than 9 months, we completed our RTL architecture and brought our first pre-silicon prototype to life on FPGA. Our architecture combines systolic array and vector processing in a single compute engine with multiple architectural optimizations, achieving very high FLOPs utilization. A single engine is super lean and it uses less than 90K LUTs and 1 MB Block RAM. It may also be one of the smallest logic-footprint compute engines developed so far. Our Unified Engine v1 supports: -matrix-matrix multiplication (~95% FLOPs utilization) -softmax (~90% FLOPs utilization) -broadcast and element-wise operations -RMSNorm / LayerNorm -block quantization/dequantization (fp4, int4) -multi-engine synchronization and many other operations. We even implemented memory-efficient attention similar to FlashAttention, reaching ~90% FLOP utilization. Full benchmarks and the software stack are available on our GitHub: We have basic compiler written in Python and it supports PyTorch tensors directly to easily test and transfer tensors between the accelerator and host using bf16, fp4 and int4 formats. Our FPGA prototype can already run LLM inference and outperform NVIDIA Jetson Orin Nano, even on a mid-tier FPGA setup (6.4x lower memory bandwidth, 18% slower clock speed at 4.5 Watts). Check the side-by-side comparison video below. Our GitHub includes low-level operator implementations, examples for tiled matrix multiplication, operation chaining, tensor parallelism, attention kernel and a full Gemma 3 1B model implementation. Many more models(Vision Transformers and VLA) are coming soon. Our accelerator IP is AXI-ready for deployment on any AMD(Xilinx) FPGA platform today. Even better, our two-engine prototype runs on an entry-level AMD(Xilinx) FPGA as a PCIe accelerator card. You can purchase it here for $50 to experiment our pre-silicon prototype on your desktop PC or Raspberry Pi 5. We will be releasing hardware bitstream updates as the architecture gets new features. More to come soon! We are expanding our team and looking for compiler engineers and floating-point hardware design engineers. If you're interested, please send me a DM.
1:17

After 8+ years on the Tesla Autopilot team and 3 years at Intel, I started Apex Compute to design a new architecture for efficient AI inference. For the past 9 months, we’ve been building our custom inference accelerator. Today we’re releasing Unified Engine v1. Last June we raised our seed round with Maxitech , DeepFin Research, Soma Capital and an incredible group of angel investors. In less than 9 months, we completed our RTL architecture and brought our first pre-silicon prototype to life on FPGA. Our architecture combines systolic array and vector processing in a single compute engine with multiple architectural optimizations, achieving very high FLOPs utilization. A single engine is super lean and it uses less than 90K LUTs and 1 MB Block RAM. It may also be one of the smallest logic-footprint compute engines developed so far. Our Unified Engine v1 supports: -matrix-matrix multiplication (~95% FLOPs utilization) -softmax (~90% FLOPs utilization) -broadcast and element-wise operations -RMSNorm / LayerNorm -block quantization/dequantization (fp4, int4) -multi-engine synchronization and many other operations. We even implemented memory-efficient attention similar to FlashAttention, reaching ~90% FLOP utilization. Full benchmarks and the software stack are available on our GitHub: We have basic compiler written in Python and it supports PyTorch tensors directly to easily test and transfer tensors between the accelerator and host using bf16, fp4 and int4 formats. Our FPGA prototype can already run LLM inference and outperform NVIDIA Jetson Orin Nano, even on a mid-tier FPGA setup (6.4x lower memory bandwidth, 18% slower clock speed at 4.5 Watts). Check the side-by-side comparison video below. Our GitHub includes low-level operator implementations, examples for tiled matrix multiplication, operation chaining, tensor parallelism, attention kernel and a full Gemma 3 1B model implementation. Many more models(Vision Transformers and VLA) are coming soon. Our accelerator IP is AXI-ready for deployment on any AMD(Xilinx) FPGA platform today. Even better, our two-engine prototype runs on an entry-level AMD(Xilinx) FPGA as a PCIe accelerator card. You can purchase it here for $50 to experiment our pre-silicon prototype on your desktop PC or Raspberry Pi 5. We will be releasing hardware bitstream updates as the architecture gets new features. More to come soon! We are expanding our team and looking for compiler engineers and floating-point hardware design engineers. If you're interested, please send me a DM.

Hasan

37,603 просмотров • 4 месяцев назад

I have been testing DeepSeek-V4-Pro with the Pi coding agent. I am mindblown by how well it works out of the box. A few notes: I spent a few hours building an LLM wiki with an agent powered entirely by DeepSeek-V4-Pro on Fireworks AI inference. This is the first time I feel like there is an open-weight model that can reason at the level of Claude and Codex. And it does this in a cost-effective way with support for 1M context length. To be clear, I am using DeepSeek-V4-Pro inside of Pi without any special configuration. It works out of the box. It's exciting that there is a model that can just be plugged into a basic harness like Pi, and it just works. I've never seen that before. Most models require lots of configuration and setup. DeepSeek's DeepSeek-V4-Pro is clearly good at agentic coding (probably the best from the open-weight models), but the model is also great on knowledge-intensive tasks where reasoning matters. The agent pulled agentic engineering best practices from different company docs (Anthropic, OpenAI, Google, Stripe, Meta, Modal, DeepSeek, Mistral, Cohere), searched and digested Reddit and HN threads, summarized arxiv papers, and surfaced trending GitHub repos. Then it distilled everything into actionable tips across categories. I love the Wiki it built. The quality is really good. Here is a snapshot of what the wiki looks like: DeepSeek-V4-Pro handled the task without breaking stride. Multi-step research queries, code generation for scaffolding, context-heavy reasoning across disparate sources. For coding specifically, this is the first open-weight model that genuinely feels like a Codex or Claude Code experience. It compares in capability and actual multi-turn agentic work. What made the loop feel so responsive was Fireworks' inference speed (the fastest in the market) and the fact that they actually validate models at the systems level before shipping. No corrupted reasoning traces. Just fast, reliable iteration. The hybrid CSA and HCA attention design cuts KV cache to just 10% and inference FLOPs by nearly 4x at 1M-token context. This is what makes the agent loop actually fast and cheap enough to run in practice. For devs who've been watching open-weight models close the gap but haven't found one that actually delivers in practice, this is the closest I've seen. Try it here:
0:31

I have been testing DeepSeek-V4-Pro with the Pi coding agent. I am mindblown by how well it works out of the box. A few notes: I spent a few hours building an LLM wiki with an agent powered entirely by DeepSeek-V4-Pro on Fireworks AI inference. This is the first time I feel like there is an open-weight model that can reason at the level of Claude and Codex. And it does this in a cost-effective way with support for 1M context length. To be clear, I am using DeepSeek-V4-Pro inside of Pi without any special configuration. It works out of the box. It's exciting that there is a model that can just be plugged into a basic harness like Pi, and it just works. I've never seen that before. Most models require lots of configuration and setup. DeepSeek's DeepSeek-V4-Pro is clearly good at agentic coding (probably the best from the open-weight models), but the model is also great on knowledge-intensive tasks where reasoning matters. The agent pulled agentic engineering best practices from different company docs (Anthropic, OpenAI, Google, Stripe, Meta, Modal, DeepSeek, Mistral, Cohere), searched and digested Reddit and HN threads, summarized arxiv papers, and surfaced trending GitHub repos. Then it distilled everything into actionable tips across categories. I love the Wiki it built. The quality is really good. Here is a snapshot of what the wiki looks like: DeepSeek-V4-Pro handled the task without breaking stride. Multi-step research queries, code generation for scaffolding, context-heavy reasoning across disparate sources. For coding specifically, this is the first open-weight model that genuinely feels like a Codex or Claude Code experience. It compares in capability and actual multi-turn agentic work. What made the loop feel so responsive was Fireworks' inference speed (the fastest in the market) and the fact that they actually validate models at the systems level before shipping. No corrupted reasoning traces. Just fast, reliable iteration. The hybrid CSA and HCA attention design cuts KV cache to just 10% and inference FLOPs by nearly 4x at 1M-token context. This is what makes the agent loop actually fast and cheap enough to run in practice. For devs who've been watching open-weight models close the gap but haven't found one that actually delivers in practice, this is the closest I've seen. Try it here:

elvis

59,803 просмотров • 2 месяцев назад

Remember that paper that started with ‘Certainly, here is a possible introduction for your topic’? How did that get past peer review?! I don’t want AI tools to do my research for me. I want AI tools to speed up boring tasks that take up my time, so I can focus on the important stuff. Anara moved to a new handle (formerly Unriddle) does exactly that. Here’s how you can use it for your research. 🧵👇 #SponsoredWalkthrough One of the biggest challenges in research is time. A solid literature review takes at least 2-3 months… sometimes even longer, depending on the depth of analysis needed. Reading, organising, and synthesising information is a slow process, but it’s absolutely necessary for high-quality work. AI can help speed it up. Not by replacing your critical thinking. It’s your PhD, your ideas need to be your own—but by automating the tedious, repetitive parts of research so you can focus on deep understanding, analysis, and writing. Unlike other AI tools, Anara works with almost any document format. This is what makes it really stand out from the rest. For instance, you can upload: ✅PDFs and other word-based documents ✅Images and presentations ✅Handwritten notes, voice memos, even videos There are so many resources out there that we can learn from. You can upload everything from research papers to YouTube videos and even your own notes and scribbles. It actually understands handwriting surprisingly well! You get automatic summaries when you upload documents. The AI extracts key information immediately, giving you quick insights. It can also help you keep your documents organised. Use the Groups feature to sort and categorise your resources. Create a group for your literature review and keep these papers separate from your other projects or chapters. Tip: Overwhelmed by the number of papers in your "to-be-read" folder? Upload your papers to Anara for immediate insights on each of them, then use these to decide which ones you want to read in more detail. Quickly identify which papers are worth your time—thank me later! You can also go deeper into the papers with Anara’s chat feature. Instead of endlessly scrolling through documents to find relevant sections, just ask the AI a question based on your uploaded files. The chat provides direct answers, all with citations. ✅Suggests questions based on your prompt, helping you refine your focus ✅Everything is sourced directly from your documents. So no random AI-generated nonsense ✅Switch between different AI models to suit your needs. Some are better for summarisation, others for deeper contextual analysis It actually sticks to the sources you give it. My favourite feature is the ability to make flashcards! After you upload a document, Anara can create flashcards to help you test your understanding. Perfect for revision and retention. But… can you trust it? The problem with many AI research tools is hallucination... meaning that they make things up. Anara doesn’t do that. It reduces hallucinations by only referencing the documents you upload. Plus, it provides detailed references and hyperlinks so you can check the original source down to the exact page number. This doesn’t mean you shouldn’t read the paper for yourself. It does mean that you can find what you need much faster, and then verify it with automatic citations. At the end of the day, these tools are here to help you, not replace you. If you’ve made it this far, then it’s (definitely) time to go to 👇 anara(dot)so and give it a try. Use code THEPHDPLACE20 for 20% off
1:48

Remember that paper that started with ‘Certainly, here is a possible introduction for your topic’? How did that get past peer review?! I don’t want AI tools to do my research for me. I want AI tools to speed up boring tasks that take up my time, so I can focus on the important stuff. Anara moved to a new handle (formerly Unriddle) does exactly that. Here’s how you can use it for your research. 🧵👇 #SponsoredWalkthrough One of the biggest challenges in research is time. A solid literature review takes at least 2-3 months… sometimes even longer, depending on the depth of analysis needed. Reading, organising, and synthesising information is a slow process, but it’s absolutely necessary for high-quality work. AI can help speed it up. Not by replacing your critical thinking. It’s your PhD, your ideas need to be your own—but by automating the tedious, repetitive parts of research so you can focus on deep understanding, analysis, and writing. Unlike other AI tools, Anara works with almost any document format. This is what makes it really stand out from the rest. For instance, you can upload: ✅PDFs and other word-based documents ✅Images and presentations ✅Handwritten notes, voice memos, even videos There are so many resources out there that we can learn from. You can upload everything from research papers to YouTube videos and even your own notes and scribbles. It actually understands handwriting surprisingly well! You get automatic summaries when you upload documents. The AI extracts key information immediately, giving you quick insights. It can also help you keep your documents organised. Use the Groups feature to sort and categorise your resources. Create a group for your literature review and keep these papers separate from your other projects or chapters. Tip: Overwhelmed by the number of papers in your "to-be-read" folder? Upload your papers to Anara for immediate insights on each of them, then use these to decide which ones you want to read in more detail. Quickly identify which papers are worth your time—thank me later! You can also go deeper into the papers with Anara’s chat feature. Instead of endlessly scrolling through documents to find relevant sections, just ask the AI a question based on your uploaded files. The chat provides direct answers, all with citations. ✅Suggests questions based on your prompt, helping you refine your focus ✅Everything is sourced directly from your documents. So no random AI-generated nonsense ✅Switch between different AI models to suit your needs. Some are better for summarisation, others for deeper contextual analysis It actually sticks to the sources you give it. My favourite feature is the ability to make flashcards! After you upload a document, Anara can create flashcards to help you test your understanding. Perfect for revision and retention. But… can you trust it? The problem with many AI research tools is hallucination... meaning that they make things up. Anara doesn’t do that. It reduces hallucinations by only referencing the documents you upload. Plus, it provides detailed references and hyperlinks so you can check the original source down to the exact page number. This doesn’t mean you shouldn’t read the paper for yourself. It does mean that you can find what you need much faster, and then verify it with automatic citations. At the end of the day, these tools are here to help you, not replace you. If you’ve made it this far, then it’s (definitely) time to go to 👇 anara(dot)so and give it a try. Use code THEPHDPLACE20 for 20% off

The PhD Place

23,135 просмотров • 1 год назад

been diving deep into Solstice lately...and honestly, this feels like the next stage of defi evolution on solana !! i actually spent 6+ hours researching and breaking it all down in a full detailed video > from how usx works to the ai-powered yieldvault, staking architecture, and why solstice might change the solana defi landscape. here’s what makes it stand out 👇 ================================ The Core Idea: solstice is building a self sustaining defi economy on solana, blending tradfi reliability with defi creativity. the focus? real yield, transparent onchain activity, and long term wealth creation, not hype cycles. ================================ 3 Pillars driving it all: 1. usx > solana native stablecoin fully collateralized, programmable, and scalable. more than a stablecoin, it fuels the entire solstice ecosystem. think of it as the liquidity backbone for all yield and staking strategies. 2. yieldvault > automated compounding engine ai-powered, delta-neutral strategies that generate yield in any market. capital protected, transparent, and designed for passive income. no inflated apy gimmicks. just sustainable, on-chain compounding. 3. solstice staking > institutional grade non-custodial validator network, 100% renewable energy, 99.99% uptime. over $1b in staked assets already powering solana. built for both individual stakers and large protocols. ================================ Why it matters: while most defi projects chase trends, solstice is building infrastructure that scales - for users, investors, and institutions. it’s aiming to onboard the next billion into defi by keeping things fast, clean, and reliable. ================================ How your capital grows here: • mint or hold $USX - always fully backed • deposit into yieldvault - earn stable returns through delta-neutral ai strategies • stake in the validator network - earn yield while securing solana • reinvest and compound - every token works, every second counts ================================ Key advantages: ✅ built on solana - instant txs + ultra-low fees ✅ yield from real onchain activity, not emissions ✅ cross-integrated products that reinforce each other ✅ powered by renewable energy ✅ transparent analytics + verifiable smart contracts ================================ this isn’t “degen defi” it’s infrastructure for the next wave of sustainable onchain finance. if you’re into yield, stability, and scalable defi... keep an eye on Solstice. the future of defi might just start here with xeet .
2:16

been diving deep into Solstice lately...and honestly, this feels like the next stage of defi evolution on solana !! i actually spent 6+ hours researching and breaking it all down in a full detailed video > from how usx works to the ai-powered yieldvault, staking architecture, and why solstice might change the solana defi landscape. here’s what makes it stand out 👇 ================================ The Core Idea: solstice is building a self sustaining defi economy on solana, blending tradfi reliability with defi creativity. the focus? real yield, transparent onchain activity, and long term wealth creation, not hype cycles. ================================ 3 Pillars driving it all: 1. usx > solana native stablecoin fully collateralized, programmable, and scalable. more than a stablecoin, it fuels the entire solstice ecosystem. think of it as the liquidity backbone for all yield and staking strategies. 2. yieldvault > automated compounding engine ai-powered, delta-neutral strategies that generate yield in any market. capital protected, transparent, and designed for passive income. no inflated apy gimmicks. just sustainable, on-chain compounding. 3. solstice staking > institutional grade non-custodial validator network, 100% renewable energy, 99.99% uptime. over $1b in staked assets already powering solana. built for both individual stakers and large protocols. ================================ Why it matters: while most defi projects chase trends, solstice is building infrastructure that scales - for users, investors, and institutions. it’s aiming to onboard the next billion into defi by keeping things fast, clean, and reliable. ================================ How your capital grows here: • mint or hold $USX - always fully backed • deposit into yieldvault - earn stable returns through delta-neutral ai strategies • stake in the validator network - earn yield while securing solana • reinvest and compound - every token works, every second counts ================================ Key advantages: ✅ built on solana - instant txs + ultra-low fees ✅ yield from real onchain activity, not emissions ✅ cross-integrated products that reinforce each other ✅ powered by renewable energy ✅ transparent analytics + verifiable smart contracts ================================ this isn’t “degen defi” it’s infrastructure for the next wave of sustainable onchain finance. if you’re into yield, stability, and scalable defi... keep an eye on Solstice. the future of defi might just start here with xeet .

ANNABEL❤️

14,216 просмотров • 9 месяцев назад

We are pleased to announce The beta version of the X314 platform has officially launched. Dapp: KEY FEATURES include: 1. Advertising screen and list of projects launched on our platform. 2. Launch tokens 314 feature: If you want to launch a project on our platform, in addition to basic project information, developers can also customize many other parameters about tax, wallet, marketing, buyback, burn, ... to create a diverse mechanism to suit their project. We will use X314 as a payment fee for these projects to ensure that X314 will be deflated in the long term. 3. X314 trading features/website of all projects on the X314 platform: Users can use this website to trade their tokens, its outstanding feature is to choose the liquidity pair on 314 or on Pancakeswap so that users can get the best price. 4. Market feature is under development. 5. Personal profile section includes many utilities to help users earn more income, advertise their own projects, profiles include many levels and links to their official platforms to help a developer's profile increase the level of trust based on the level on their profile. REWARD MECHANISM 👨‍👧‍👧 Referral cashback rate * Tier 1: 30% * Tier 2: 10% * Tier 3: 5% ✅ VIP level price * VIP1: 0.1 BNB, 8.5% discount * VIP2: 0.2 BNB, 8% discount * VIP3: 0.4 BNB, 7% discount * VIP4: 0.8 BNB, 6% discount * VIP5: 1.2 BNB, 5% discount 💰Fee standard: * Launch token base price: 5000 X314 tokens, each new token function accumulates 500 X314 tokens 💹 Rotating advertising price: * 1 day: 20000 X314 tokens * 3 days: 50000 X314 tokens * 7 days: 100000 X314 tokens ❇️ Project management fee Projects: * New Popular Project: 1000 X314 Tokens * Most Popular Project: 50 X314 Tokens * Newly Launched Project: 1000 X314 Tokens * Most Launched Project: 50 X314 Tokens X314 is not only a project but also an ecosystem full of the best features to help users choose trustworthy projects. Besides, this is also a platform that helps transparent developers to develop good projects. Our Media: ✅Website: 🚀Twitter: ✅Telegram: ✅ CK Audit: ✅ CMC: ✅ Coingecko: #X314 #platform #BSC #BNBChain #314swap
0:30

We are pleased to announce The beta version of the X314 platform has officially launched. Dapp: KEY FEATURES include: 1. Advertising screen and list of projects launched on our platform. 2. Launch tokens 314 feature: If you want to launch a project on our platform, in addition to basic project information, developers can also customize many other parameters about tax, wallet, marketing, buyback, burn, ... to create a diverse mechanism to suit their project. We will use X314 as a payment fee for these projects to ensure that X314 will be deflated in the long term. 3. X314 trading features/website of all projects on the X314 platform: Users can use this website to trade their tokens, its outstanding feature is to choose the liquidity pair on 314 or on Pancakeswap so that users can get the best price. 4. Market feature is under development. 5. Personal profile section includes many utilities to help users earn more income, advertise their own projects, profiles include many levels and links to their official platforms to help a developer's profile increase the level of trust based on the level on their profile. REWARD MECHANISM 👨‍👧‍👧 Referral cashback rate * Tier 1: 30% * Tier 2: 10% * Tier 3: 5% ✅ VIP level price * VIP1: 0.1 BNB, 8.5% discount * VIP2: 0.2 BNB, 8% discount * VIP3: 0.4 BNB, 7% discount * VIP4: 0.8 BNB, 6% discount * VIP5: 1.2 BNB, 5% discount 💰Fee standard: * Launch token base price: 5000 X314 tokens, each new token function accumulates 500 X314 tokens 💹 Rotating advertising price: * 1 day: 20000 X314 tokens * 3 days: 50000 X314 tokens * 7 days: 100000 X314 tokens ❇️ Project management fee Projects: * New Popular Project: 1000 X314 Tokens * Most Popular Project: 50 X314 Tokens * Newly Launched Project: 1000 X314 Tokens * Most Launched Project: 50 X314 Tokens X314 is not only a project but also an ecosystem full of the best features to help users choose trustworthy projects. Besides, this is also a platform that helps transparent developers to develop good projects. Our Media: ✅Website: 🚀Twitter: ✅Telegram: ✅ CK Audit: ✅ CMC: ✅ Coingecko: #X314 #platform #BSC #BNBChain #314swap

BSC🔶X314

17,909 просмотров • 1 год назад

An open-source, onchain creator platform that never says "no" to creativity. If you ask, "Can I build this here?" - the answer is always yes. Learn more about our vision below 🧵 👁 What is TheCreators? 🚪 A collective of artists, builders, and fine memes reshaping the creator economy. We empower creators to go direct-to-market, bypassing traditional gatekeepers. By bringing creativity and commerce onchain, we're unlocking a new era of freedom. But this isn’t new. This is one of the longest plays. - purchased by rskagy.base.eth 👁️🚪 d/acc #BasedCreators ⏹ in 2012 - was a web2 branding and ad agency until 2021 - working for gamestopNFT and onboarding hundreds of thousands of gamestop apes to buy their first NFTs, something changed, leading to a meeting of minds, and a collective mission to deliver on the promises of web3 gaming. to re-convene at a later date, when the time is right. It has since undergone 4 years of trial and error and battle testing to see what works and what doesn't work when it comes to community owned digital worlds. 🌐 Building a Based World What does it mean to build a based world? To us, it means a world where creators own their work, communities thrive and share an ownership stake in their own distribution, and creativity is limitless. Our platform aims to enable anyone to create immersive experiences - games, events, and digital spaces - without barriers. We’re developing: ✅ AI-powered worldbuilding - natural language prompts generate metaverse spaces ✅ Onchain asset discovery, deployment, & interaction from within virtual worlds ✅ No-code & pro-level tools - Unity-based with deep onchain integration ✅ Plug-and-play templates – for rapid creation and customization by novice 3D and game devs. ✅ Maintained by the open source community We're partnered with: 🏰 Guild - enabling their userbase of 4m+ connected wallets to reward their communities with token-gated towns, games, events, drops, & experiences 👏 liquid 💧 / moved to @clanker_world (USED FOR TESTING NOW) - Official metaverse event partner, building Clankercon 2025 for the clanker ecosystem of 100k+ meme token communities 🤖🎮 FARCAST - Superpowering their vision for agentic gaming with minigames and metaverse tech 🐶 Own The Doge 🐶🖼 - Creating lasting, meaningful impact in the world thru memes & onchain culture. Targeting education, gaming, food, & fun. Do Only Good Everyday 🌏 TheCreators is a global movement, with friends in high places ✨ Watch the video in this post to see an overview of one of the first-party games slated to be released on our platform, "Settlers of Ariel," built by TheCreators Studios. Creators will have access to modular templates, AI-assisted customization, and interoperable game elements built by us, and trained using our AI to enable natural language metaverse building. It's all early-stage, but we're moving fast. ClankerCon and other upcoming projects will showcase the first real applications. Then we grow other creators to publish on the platform. ❓How will we select the first creators to be allowed on the platform? What's next? We will be releasing THEO-0, our onchain AI agent as our next development milestone. This agent will actively run a 24/7 campaign to enable people to vouch for their favorite creators via a limited voting process. Vote for creators you like, and they will gain CREATE points, and you will also gain points for voting every day in a streak. Creators with the most points on the leaderboards will be among the first onboarded to the platform, and all creators with points will be invited to take part in Token Generation Events as each season of leaderboards comes to a close. 1. Help us grow as we look forward to launching THEO-0 2. Be ready to start voting for your favorite creators! How do I become a Creator? If you pour your time, energy, and soul into something special - you already are one. If your mission is to reclaim art and culture from corporate algorithms, & make a more based world for our children, you belong here. Join us. 👁🚪
1:11

An open-source, onchain creator platform that never says "no" to creativity. If you ask, "Can I build this here?" - the answer is always yes. Learn more about our vision below 🧵 👁 What is TheCreators? 🚪 A collective of artists, builders, and fine memes reshaping the creator economy. We empower creators to go direct-to-market, bypassing traditional gatekeepers. By bringing creativity and commerce onchain, we're unlocking a new era of freedom. But this isn’t new. This is one of the longest plays. - purchased by rskagy.base.eth 👁️🚪 d/acc #BasedCreators ⏹ in 2012 - was a web2 branding and ad agency until 2021 - working for gamestopNFT and onboarding hundreds of thousands of gamestop apes to buy their first NFTs, something changed, leading to a meeting of minds, and a collective mission to deliver on the promises of web3 gaming. to re-convene at a later date, when the time is right. It has since undergone 4 years of trial and error and battle testing to see what works and what doesn't work when it comes to community owned digital worlds. 🌐 Building a Based World What does it mean to build a based world? To us, it means a world where creators own their work, communities thrive and share an ownership stake in their own distribution, and creativity is limitless. Our platform aims to enable anyone to create immersive experiences - games, events, and digital spaces - without barriers. We’re developing: ✅ AI-powered worldbuilding - natural language prompts generate metaverse spaces ✅ Onchain asset discovery, deployment, & interaction from within virtual worlds ✅ No-code & pro-level tools - Unity-based with deep onchain integration ✅ Plug-and-play templates – for rapid creation and customization by novice 3D and game devs. ✅ Maintained by the open source community We're partnered with: 🏰 Guild - enabling their userbase of 4m+ connected wallets to reward their communities with token-gated towns, games, events, drops, & experiences 👏 liquid 💧 / moved to @clanker_world (USED FOR TESTING NOW) - Official metaverse event partner, building Clankercon 2025 for the clanker ecosystem of 100k+ meme token communities 🤖🎮 FARCAST - Superpowering their vision for agentic gaming with minigames and metaverse tech 🐶 Own The Doge 🐶🖼 - Creating lasting, meaningful impact in the world thru memes & onchain culture. Targeting education, gaming, food, & fun. Do Only Good Everyday 🌏 TheCreators is a global movement, with friends in high places ✨ Watch the video in this post to see an overview of one of the first-party games slated to be released on our platform, "Settlers of Ariel," built by TheCreators Studios. Creators will have access to modular templates, AI-assisted customization, and interoperable game elements built by us, and trained using our AI to enable natural language metaverse building. It's all early-stage, but we're moving fast. ClankerCon and other upcoming projects will showcase the first real applications. Then we grow other creators to publish on the platform. ❓How will we select the first creators to be allowed on the platform? What's next? We will be releasing THEO-0, our onchain AI agent as our next development milestone. This agent will actively run a 24/7 campaign to enable people to vouch for their favorite creators via a limited voting process. Vote for creators you like, and they will gain CREATE points, and you will also gain points for voting every day in a streak. Creators with the most points on the leaderboards will be among the first onboarded to the platform, and all creators with points will be invited to take part in Token Generation Events as each season of leaderboards comes to a close. 1. Help us grow as we look forward to launching THEO-0 2. Be ready to start voting for your favorite creators! How do I become a Creator? If you pour your time, energy, and soul into something special - you already are one. If your mission is to reclaim art and culture from corporate algorithms, & make a more based world for our children, you belong here. Join us. 👁🚪

Create on Base ⏹

28,304 просмотров • 1 год назад

What does the reputation model look like for agents? (alpha leak below) And how do we associate the proofs that we have about human beings with the agents who represent them? You may have heard of a process called KYC or Know Your Customer. That's very common with traditional financial applications and services. We have introduced a concept that we call KYA or Know Your Agent, which is a structured way to be able to express what model, how data was used in training, who the deployer is, what entities this agent instance is accountable back to, providing not only provenance but identity of the associated organization or entity. That's also another root of trust that we think about a lot: Enterprises and organizations tied back to things like their domains. To share a little bit of an alpha leak here, a product that we're excited to be rolling out in the next few weeks will allow our enterprise partners to more easily verify and prove the traits and capabilities of their teams as well as their counterparties. On the agent front, that makes it really easy to prove that an agent is acting on behalf of a given business or entity. We've already seen lawsuits where the absence of such technology has been a huge risk, such as with airlines that incorporate ChatGPT wrappers in their support pages. And then those AI enabled interactions end up making up plane tickets that don't exist and those airlines have to honor them. As small of an example as that might be, being able to prove agent accountability also unlocks a huge set of opportunities for use in enterprise for those agent to agent interactions. The Deep Trust Framework that our team has put together that we're excited to be bringing into a friendly SDK form in the next few weeks for some of our partners includes those reputation based capabilities, so how you can basically keep track of the interactions an agent has had, associate all of that to the entity to which they're accountable, and then that creates a sustainable reputation model for these agent to agent Interactions. Source: Billions CEO Evin McMullen evin speaking at House of Chimera Spaces Event Dec 3, 2025
2:06

What does the reputation model look like for agents? (alpha leak below) And how do we associate the proofs that we have about human beings with the agents who represent them? You may have heard of a process called KYC or Know Your Customer. That's very common with traditional financial applications and services. We have introduced a concept that we call KYA or Know Your Agent, which is a structured way to be able to express what model, how data was used in training, who the deployer is, what entities this agent instance is accountable back to, providing not only provenance but identity of the associated organization or entity. That's also another root of trust that we think about a lot: Enterprises and organizations tied back to things like their domains. To share a little bit of an alpha leak here, a product that we're excited to be rolling out in the next few weeks will allow our enterprise partners to more easily verify and prove the traits and capabilities of their teams as well as their counterparties. On the agent front, that makes it really easy to prove that an agent is acting on behalf of a given business or entity. We've already seen lawsuits where the absence of such technology has been a huge risk, such as with airlines that incorporate ChatGPT wrappers in their support pages. And then those AI enabled interactions end up making up plane tickets that don't exist and those airlines have to honor them. As small of an example as that might be, being able to prove agent accountability also unlocks a huge set of opportunities for use in enterprise for those agent to agent interactions. The Deep Trust Framework that our team has put together that we're excited to be bringing into a friendly SDK form in the next few weeks for some of our partners includes those reputation based capabilities, so how you can basically keep track of the interactions an agent has had, associate all of that to the entity to which they're accountable, and then that creates a sustainable reputation model for these agent to agent Interactions. Source: Billions CEO Evin McMullen evin speaking at House of Chimera Spaces Event Dec 3, 2025

Billions Network

68,484 просмотров • 7 месяцев назад

What a year. 🚀 2025 was the year ChainOpera AI turned vision into real momentum: building a community-co-created, community-co-owned AI agent network and pushing the boundaries of what decentralized, collaborative intelligence can look like. 🚀 Biggest highlights from 2025 ✅- AI Terminal officially launched: We unveiled the ChainOpera AI Terminal as a unified gateway to decentralized AI, making it possible for anyone to interact with powerful, decentralized LLMs without technical friction. Positioned as the “browser for the DeAI era,” the AI Terminal marked a major step toward making decentralized intelligence accessible, usable, and mainstream. ✅- AI Terminal adoption at massive scale: Momentum followed quickly. The AI Terminal surpassed 2M registered users and consistently ranked top 3 among all apps on the BNB AI DappBay, validating strong product–market fit and real, sustained usage at scale. ✅- Announcing Coco: the world’s first community-owned Super Agent: We introduced Coco, the intelligence layer that sits between users and the agent network. Coco dynamically routes each request to the most efficient, community-built agent—optimizing for quality and speed while rewarding the creators behind the best-performing agents. This was a defining moment in realizing a truly community-owned intelligence layer. ✅- From agents to a living agent network: With the launch of the Agent Social Network and Super Agent architecture, ChainOpera AI moved beyond isolated agents toward a collaborative system where humans and specialized agents coordinate, share context, and solve complex, multi-step tasks together. ✅- $COAI breakout year: The listing of $COAI across major exchanges shocked the market, and throughout the year COAI consistently remained among the top AI-native crypto tokens by visibility, activity, and community engagement – reflecting growing confidence in the long-term vision of collaborative intelligence. ✅- Global presence: ChainOpera AI around-the-world tour: ChainOpera AI went global in 2025, sponsoring and participating in major AI and Web3 events across North America, Europe, and Asia, including ETHDenver, Consensus Toronto, Token2049 Singapore, ETHCC, SBC, and Devcon. These global touchpoints helped us engage directly with developers, builders, investors, and partners worldwide, accelerating adoption and positioning ChainOpera AI at the center of the emerging AIxBlockchain movement. ✅- Community momentum at scale: Community remained the heart of ChainOpera AI’s growth. We successfully completed three seasons of structured community engagement, executed a widely participated community airdrop, and ran multiple ecosystem-shaping campaigns to incentivize builders, creators, and early adopters. These efforts strengthened alignment between users, developers, and the protocol, laying the foundation for a durable, community-owned AI ecosystem. ✅- “AI for Markets” taking shape: We laid critical groundwork for AI-native market intelligence, including the launch of PrediMarket Agent and multiple trading and analysis agents—early building blocks toward an AI-driven ecosystem for crypto and DeFi markets. ✅- Building in public, with the community: Across product launches, research milestones, ecosystem discussions, and global events, we continued to build openly to bring developers, users, and partners directly into the evolution of ChainOpera AI. This year also marked the launch of the ChainOpera AI Foundation website, formally kicking off a bold Ecosystem Fund designed to empower builders, incubate high-impact projects, and accelerate the growth of a truly community-owned, collaborative AI ecosystem. To every builder, user, and supporter who helped make this year possible: THANK YOU! 🧭 What we’re excited about in the coming year 🔹- A Stronger, Denser Agent Economy (everyday adoption + cross-chain reach): In 2026, we are scaling the Agent Economy from growth to daily usage, with more agents, richer workflows, deeper multi-agent collaboration, and higher-impact use cases that users rely on every day. In parallel, we are expanding the agent network beyond a single ecosystem with cross-chain execution and interoperability, allowing agents to access the best liquidity, data, and opportunities wherever they exist. 🔹- AI Market Infrastructure Evolution: Building on PrediMarket Agent and our growing suite of trading and market-intelligence agents, we are advancing toward a mature AI market infrastructure, where agents continuously monitor, reason, simulate, optimize, and act across crypto, DeFi, and beyond. The goal is to make complex markets more accessible, more transparent, and more intelligence-driven, turning research, decision-making, and execution into a fast and reliable loop for everyday users. 🔹- Ecosystem Acceleration through the Foundation: With the ChainOpera AI Foundation and our Ecosystem Fund and Co-Creation Grants, we are doubling down on empowering independent builders to expand the protocol, the agent network, and the underlying infrastructure, so the community can co-create, co-own, and scale the ecosystem together. 🔹- Business Expansion and Market Penetration: In 2026, we will focus on expanding ChainOpera’s reach through strategic partnerships, product-led growth, and new paths to monetization, bringing AI agents to a broader global user base and driving sustained adoption, engagement, and revenue, while staying aligned with community ownership and an open ecosystem. 2025 was the proof. 2026 is where it compounds. 🔥 Co-Create. Co-Own. COAI.
0:22

What a year. 🚀 2025 was the year ChainOpera AI turned vision into real momentum: building a community-co-created, community-co-owned AI agent network and pushing the boundaries of what decentralized, collaborative intelligence can look like. 🚀 Biggest highlights from 2025 ✅- AI Terminal officially launched: We unveiled the ChainOpera AI Terminal as a unified gateway to decentralized AI, making it possible for anyone to interact with powerful, decentralized LLMs without technical friction. Positioned as the “browser for the DeAI era,” the AI Terminal marked a major step toward making decentralized intelligence accessible, usable, and mainstream. ✅- AI Terminal adoption at massive scale: Momentum followed quickly. The AI Terminal surpassed 2M registered users and consistently ranked top 3 among all apps on the BNB AI DappBay, validating strong product–market fit and real, sustained usage at scale. ✅- Announcing Coco: the world’s first community-owned Super Agent: We introduced Coco, the intelligence layer that sits between users and the agent network. Coco dynamically routes each request to the most efficient, community-built agent—optimizing for quality and speed while rewarding the creators behind the best-performing agents. This was a defining moment in realizing a truly community-owned intelligence layer. ✅- From agents to a living agent network: With the launch of the Agent Social Network and Super Agent architecture, ChainOpera AI moved beyond isolated agents toward a collaborative system where humans and specialized agents coordinate, share context, and solve complex, multi-step tasks together. ✅- $COAI breakout year: The listing of $COAI across major exchanges shocked the market, and throughout the year COAI consistently remained among the top AI-native crypto tokens by visibility, activity, and community engagement – reflecting growing confidence in the long-term vision of collaborative intelligence. ✅- Global presence: ChainOpera AI around-the-world tour: ChainOpera AI went global in 2025, sponsoring and participating in major AI and Web3 events across North America, Europe, and Asia, including ETHDenver, Consensus Toronto, Token2049 Singapore, ETHCC, SBC, and Devcon. These global touchpoints helped us engage directly with developers, builders, investors, and partners worldwide, accelerating adoption and positioning ChainOpera AI at the center of the emerging AIxBlockchain movement. ✅- Community momentum at scale: Community remained the heart of ChainOpera AI’s growth. We successfully completed three seasons of structured community engagement, executed a widely participated community airdrop, and ran multiple ecosystem-shaping campaigns to incentivize builders, creators, and early adopters. These efforts strengthened alignment between users, developers, and the protocol, laying the foundation for a durable, community-owned AI ecosystem. ✅- “AI for Markets” taking shape: We laid critical groundwork for AI-native market intelligence, including the launch of PrediMarket Agent and multiple trading and analysis agents—early building blocks toward an AI-driven ecosystem for crypto and DeFi markets. ✅- Building in public, with the community: Across product launches, research milestones, ecosystem discussions, and global events, we continued to build openly to bring developers, users, and partners directly into the evolution of ChainOpera AI. This year also marked the launch of the ChainOpera AI Foundation website, formally kicking off a bold Ecosystem Fund designed to empower builders, incubate high-impact projects, and accelerate the growth of a truly community-owned, collaborative AI ecosystem. To every builder, user, and supporter who helped make this year possible: THANK YOU! 🧭 What we’re excited about in the coming year 🔹- A Stronger, Denser Agent Economy (everyday adoption + cross-chain reach): In 2026, we are scaling the Agent Economy from growth to daily usage, with more agents, richer workflows, deeper multi-agent collaboration, and higher-impact use cases that users rely on every day. In parallel, we are expanding the agent network beyond a single ecosystem with cross-chain execution and interoperability, allowing agents to access the best liquidity, data, and opportunities wherever they exist. 🔹- AI Market Infrastructure Evolution: Building on PrediMarket Agent and our growing suite of trading and market-intelligence agents, we are advancing toward a mature AI market infrastructure, where agents continuously monitor, reason, simulate, optimize, and act across crypto, DeFi, and beyond. The goal is to make complex markets more accessible, more transparent, and more intelligence-driven, turning research, decision-making, and execution into a fast and reliable loop for everyday users. 🔹- Ecosystem Acceleration through the Foundation: With the ChainOpera AI Foundation and our Ecosystem Fund and Co-Creation Grants, we are doubling down on empowering independent builders to expand the protocol, the agent network, and the underlying infrastructure, so the community can co-create, co-own, and scale the ecosystem together. 🔹- Business Expansion and Market Penetration: In 2026, we will focus on expanding ChainOpera’s reach through strategic partnerships, product-led growth, and new paths to monetization, bringing AI agents to a broader global user base and driving sustained adoption, engagement, and revenue, while staying aligned with community ownership and an open ecosystem. 2025 was the proof. 2026 is where it compounds. 🔥 Co-Create. Co-Own. COAI.

ChainOpera AI

17,016 просмотров • 6 месяцев назад