City-Scale LiDAR Semantic & Instance Segmentation Dataset 🏙️ 3D point cloud segmentation friends, tired of small or messy outdoor datasets that wreck model generalization? Say hello to WHU-Urban3D—6️⃣B annotated points, just published in ISPRS Journal! 🏆 Why we need it: Previous datasets were either indoor, small, or poorly labeled. WHU-Urban3D fills the gap for real, complex urban scenes. 💪 🌟 WHU-Urban3D Highlights 1️⃣ Massive scale & full coverage 🏙️ Shanghai & Wuhan, airborne ALS (>3.2M m²) mobile MLS (>6.5 km), full urban 3D scene coverage 2️⃣ 6 billion precise labels 🏷️ 30 classes: buildings, vehicles, pedestrians… perfect to feed deep learning models 3️⃣ Rich attributes 🛠️ XYZ intensity, echo counts… multi-dimensional features boost model generalization 📈 4️⃣ Multi-modal fusion 📷 MLS panoramic images included for cross-data learning 💡 Applications: Intelligent transport, urban planning, AR/VR… unleash your models! 🔥 🔗 Open access Dataset: whu3d.com/ Paper: sciencedirect.com/science/ar… #PointCloud #DeepLearning #WHU #OpenDataset #RemoteSensing #ComputerVision #3DSegmentation #Research #AI

A Large-Scale Dataset for Molecular Structure-Language Description via a Rule-Regularized Method 1 163k molecule–description pairs released: a new open resource lets LLMs learn chemistry the way chemists do—by reading accurate, structure-grounded prose instead of just SMILES. 2 No human annotator burnout: the pipeline starts with IUPAC names, enriches OPSIN’s XML parse tree with missing ring-topology, stereochemistry and locant data, then prompts GPT-5.2 to turn the metadata into natural language—fully automatic, 98.6 % precision on 2 k hand/LLM-checked samples. 3 The trick is “rule-regularized” metadata: fused, bridged and spiro systems get explicit atom-label maps, fusion edges and bridge locants, so the LLM cannot hallucinate where substituents sit or which bond is axial. 4 An atom-matching safety net: every generated description must self-report its non-H count; mismatches are discarded, trimming 2.3 % of drafts but catching 72 % of remaining errors without extra human labor. 5 Hard molecules (multi-ring, spiro, bridged) are routed to higher-reasoning GPT-5.2 calls; easy ones use cheaper GPT-5—cost-aware scheduling keeps the whole 163 k set within academic budget. 6 Ablation shows metadata alone lifts accuracy from 94.1 % to 98.6 %; without the atom filter, precision collapses to 27.7 % on failing samples, proving both steps are essential. 7 Average caption is 261 words—longer than vision datasets—yet validators reconstruct the exact structure in 11.7 min, showing the text is usable for downstream reaction-planning or property-prediction fine-tuning. 8 Authors release the Java metadata generator, 163 k descriptions and the 2 k validation split on Hugging Face, inviting the community to swap in other LLMs or extend to peptides, organometallics, etc. 💻Code: github.com/TheLuoFengLab/Mol… 📜Paper: arxiv.org/abs/2602.02320 #ChemNLP #MolLang #OpenDataset #Structure2Text #GPTChemistry

🧵 ROVR AMA Recap, The Open Dataset That Could Power Spatial AI 🚗🌍 On July 25, the ROVR team hosted an AMA about their Open Dataset. Here's a full recap of the best answers from @danveral11, @GeoCKC and @mikeahorton 👇 #ROVR #DePIN #SpatialAI #AutonomousDriving #OpenDataset

🛰 Real-World Data. Decentralized. Open-Source. ROVR Network is building the world’s first open dataset for Spatial AI & Autonomy, and YOU can be part of it. 📅 AMA Today 🕓 July 26 — 16:00 UTC / 9:00 PDT 🎙 Join here → discord.gg/EzPrqKGA?event=13… We’ll explore: 🧠 Why we’re building a fully open, decentralized mapping network 🔍 “If Tesla doesn’t need LiDAR, why does ROVR?” 💡 Can teams actually use ROVR data in real-world commercial apps? 🔹 community Q&A #ROVRNetwork #DePIN #HDMaps #OpenDataset #SpatialAI #AutonomousDriving #LiDAR #Web3

@OpenledgerHQ x @AlloraNetwork = Self-Improving AI Agents 🔥 This isn’t just another AI collab, it’s a leap toward autonomous, evolving field experts that actually learn and improve over time. Why this is a huge win for Web3 and AI 👇 @OpenledgerHQ has teamed up with @AlloraNetwork to build self-improving agents, not just chatbots, but AI tools that evolve, adapt, and refine themselves as they work. Think autonomous experts. @AlloraNetwork is on fire right now leading in decentralized intelligence. Combining that with OpenLedger’s infra unlocks a real shot at next-gen, autonomous AI agents that aren’t just static tools but dynamic thinkers. What Are Self-Improving Agents? Think: • Autonomous researchers • AI analysts • On-chain strategists They evolve with feedback/data, thus improving accuracy, decision-making, and specialization. Basically: they get smarter as you use them. It’s not just hype, this is next-gen AI utility Most Web3 AI platforms use LLM models. @OpenledgerHQ lets you fine-tune them: ✓ OpenLora ✓ OpenDataset ✓ Custom pipelines This is DIY AI with serious firepower. Unlike static AI tools, these agents evolve with use. They adapt. They specialize. They improve. That’s a massive leap in both performance and personalization for builders, analysts, anyone. This isn’t “AI for vibes.” This is real AI for real builders. @OpenledgerHQ x @AlloraNetwork are laying the foundation for practical, evolving agents and it's bullish for anyone serious about Web3 x AI. @OpenledgerHQ brings the infra. @AlloraNetwork brings the intelligence. Together, they’re creating AI agents that actually do stuff, not just talk. This is one to watch closely. The AI x Web3 future is getting real

🚨 Major Partnership Alert from @OpenledgerHQ! OpenLedger has officially teamed up with @AlloraNetwork to build next-gen, self-learning AI agents — and the implications are massive. These aren’t just static bots. They’re evolving field experts, designed to learn, adapt, and grow over time. This is a power move. As AI x Crypto continues to accelerate, adaptive agents are becoming the next frontier — and Allora is already leading the charge. We’re looking at upcoming integrations with: 🧠 OpenLora 📊 OpenDataset 🔧 Fine-tuned LLMs via OpenLedger’s seamless UI Few platforms make it this easy to fine-tune and deploy intelligent, decentralized agents. OpenLedger is becoming a central force in the evolution of onchain AI. This collaboration could define the future of real-world, adaptive AI in Web3. Stay sharp — it’s all unfolding now. ⚡ And don’t forget to follow these top campaigns on @cookiedotfun: @tenprotocol · @OpenledgerHQ · @recallnet · @elympics_ai · @JoinSapien · @vooi_io

Major Partnership Announcement from @OpenledgerHQ! OpenLedger has officially joined forces with @AlloraNetwork to build advanced field expert AI agents - and the potential is game-changing. These agents won’t just function - they’ll learn and evolve over time. This is a strong strategic move for OpenLedger. As the AI x Crypto space accelerates, self-improving agents are emerging as a key frontier and Allora is already a standout player in that domain. We can expect integrations with: • 🧠 OpenLora • 📊 OpenDataset • 🔧 Fine-tuned LLMs through OpenLedger’s intuitive interface OpenLedger is one of the few platforms empowering users to fine-tune large language models with ease, making it a natural hub for deploying decentralized, intelligent agents. This collaboration could mark a pivotal moment in the evolution of adaptive, real-world AI systems in Web3. Stay tuned - the future of AI is being built right here. Let's follow these campaigns in @cookiedotfun too @tenprotocol @OpenledgerHQ @recallnet @elympics_ai @JoinSapien @vooi_io

I think they will use Open Lora and openledger 's opendataset as well as fine tuned models since OpenLedger is one of the only space we can fine tune LLM models with interface Certainly we will see a very useful ai agents coming from OpenLedger with this partnership

🚨 Big Partnership Alert 🚨 @OpenledgerHQ has officially teamed up with @AlloraNetwork to build self-improving AI field expert agents — yes, you read that right. These aren’t your average chatbots; these are advanced, evolving agents that will sharpen themselves over time. This move is extremely bullish for OpenLedger. Here's why: Allora is hot right now. They’ve been gaining traction fast in both the crypto and AI scenes. Self-improving agents are the next frontier in AI — think autonomous researchers, analysts, and strategists. This isn’t just hype; it’s real future-forward utility. OpenLora OpenDataset custom fine-tuning = an AI playground with actual muscle. OpenLedger is one of the only platforms in the space offering an interface for hands-on fine-tuning of LLMs. That’s a massive edge. So yeah — with OpenLedger’s infrastructure and Allora’s brainpower, we’re likely going to see a new wave of practical, evolving AI agents that can actually do stuff, not just talk about it. This is definitely one to watch closely — not just for AI fans, but for anyone serious about Web3 utility.

In @OpenledgerHQ has partnered with Allora Network to build self improving AI field expert agents a major leap in crypto x AI innovation. With OpenLora, fine-tuned models, and the OpenDataset, they’re uniquely positioned to lead this frontier.

Openledger just made a partnership with AlloraNetwrk to develop advanced field expert AI agents that improve themselves. They’ll likely use OpenLora, OpenDataset, and fine-tuned models. This is very bullish OpenLedger is gaining serious recognition in the AI and crypto space.

Openledger and Allora Network Are Cooking Up Something Wild! Alright, listen up, CT! You know how everyone's been yapping about AI? Well, @OpenledgerHQ just dropped a bombshell, partnering with Allora Network to build AI agents that improve themselves. Yeah, you heard that right: self-aware, self-evolving digital brains! My mind is officially blown. This isn't just some casual handshake; this is a full-on power-up for Openledger. The entire crypto and AI space is waking up to how massive self-improving agents are, and Allora's been making serious waves lately. Imagine the possibilities! We're talking about AI agents so smart they're practically doing their own taxes. And rumor has it, they'll be using Open Lora and Openledger’s opendataset to fine-tune these bad boys. Openledger is one of the only places where you can even do that with LLMs, which is just bonkers. Get ready for some seriously useful AI coming out of this partnership. This isn't your grandma's chatbot; this is the future, and it's looking mighty intelligent.

🚨 Big Partnership Alert from @OpenledgerHQ! 🚨 OpenLedger has officially partnered with @AlloraNetwork to develop advanced field expert AI agents — and the implications are huge. These AI agents won’t just operate—they’ll self-improve over time. 🔁🤖 This is a bullish move for OpenLedger. As the AI x Crypto space heats up, self-improving agents are becoming a critical frontier, and Allora has been making serious waves in that domain. We’re likely to see integration with: •🧠 OpenLora •📊 OpenDataset •🔧 Fine-tuned LLMs via OpenLedger’s unique interface OpenLedger stands out as one of the few platforms enabling fine-tuning of LLMs with a user-friendly interface, making it an ideal launchpad for powerful, decentralized AI agents. This partnership could mark a turning point in the development of truly useful, evolving AI systems in the decentralized world. Stay tuned — the future of AI is building right here. 🚀 #AI #Crypto #OpenLedger #Allora #LLM #Web3 #DeFi #AIagents

Why This Partnership is Bullish for OpenLedger This alliance is a major win for OpenLedger, boosting its recognition and cementing its position at the forefront of the AI and blockchain convergence. Here's why: Cutting-Edge AI: Developing self-improving agents puts OpenLedger squarely in a cutting-edge field of AI research and application. Allora Network's Momentum: Allora Network is already making waves in both the crypto and AI sectors with its focus on decentralized, self-improving AI. Partnering with them brings significant credibility and technological synergy. Leveraging #OpenLedger 's Strengths: This partnership is poised to heavily utilize OpenLedger's unique offerings: OpenLoRA: OpenLedger's #openlora protocol is designed to drastically reduce AI deployment costs (up to 90%!) by allowing thousands of fine-tuned models to run on a single GPU. This efficiency is crucial for the scalability of continuously improving agents. OpenDataset & Fine-Tuning LLMs: OpenLedger provides the infrastructure for decentralized data and an intuitive interface for fine-tuning Large Language Models (LLMs). This is essential for training and specializing AI agents, ensuring they have access to high-quality, verifiable, and domain-specific data. Their "Proof of Attribution" system ensures data contributors are fairly rewarded, fostering a robust data ecosystem. Decentralized AI Infrastructure: @OpenledgerHQ 's vision of a "sovereign data blockchain" for AI development aligns perfectly with the need for transparent, verifiable, and user-controlled data pipelines for these advanced AI agents. @OpenledgerHQ #opentora #rivalznetwork $RIZ @MemeX_MRC20 @KaitoAI @vooi_io @JoinSapien @TakerProtocol $Taker #taker #RIZ $Riz

Big partnership news from @OpenledgerHQ Openledger just made a partnership with AlloraNetwrk! They will be developing advanced field expert ai agents. These artificial intelligence agents will improve themselves! This is a very bullish news for OpenLedger as more of the space also recognize it and self-improving agents is a big field and allora making waves in crypto and ai space lately I think they will use Open Lora and openledger 's opendataset as well as fine tuned models since OpenLedger is one of the only space we can fine tune LLM models with interface Certainly we will see a very useful ai agents coming from OpenLedger with this partnership

We've just published the Bittensor SN94 Agent Action Dataset (May 2025) on Hugging Face 🤖📡 This dataset captures step-level behavior from miner Agents operating in SN94—a platform for evaluating advanced models and architectures for embodied AI. Explore here: [🔗huggingface.co/datasets/East…] #Bittensor #EastworldAI #OpenDataset #AI #TAO

Big news: #NVIDIA just released an open, physically grounded dataset to supercharge embodied AI research. By capturing 70,000 interactions with real-world objects in high-fidelity physics environments, this dataset sets a new standard for training AI agents that understand and manipulate the physical world. This is a massive leap forward for robotics, digital twins, and simulation-based learning — bringing us closer to more capable, real-world-ready AI systems. Explore the dataset and the research: blogs.nvidia.com/blog/open-p… #AI #EmbodiedAI #Robotics #DigitalTwins #Simulation #NVIDIA #OpenDataset #Omniverse

An open quantum chemistry property database of 120 kilo molecules with 20 million conformers • The paper introduces “QO2Mol,” a large-scale quantum chemistry dataset consisting of 120,000 organic molecules and 20 million conformers, aiming to revolutionize research in computational chemistry and drug discovery. • The dataset covers 10 elements (C, H, O, N, S, P, F, Cl, Br, I) and provides quantum mechanical properties calculated using high-precision B3LYP/def2-SVP methods. This allows comprehensive studies of structure-property relationships with real-world molecular relevance. • One of the standout features is its focus on high precision, leveraging around 10 million CPU core-hours for calculations, which offers potential energy, forces, and additional attributes for better molecular behavior prediction. • Compared to existing datasets like QM9 and ANI-1, QO2Mol boasts superior diversity in molecular structures and elements, allowing better applicability in various research fields such as drug discovery, material science, and AI model training. • The dataset is accompanied by benchmark codes and scripts, enabling easy integration for researchers aiming to develop or enhance AI models for molecular prediction tasks. • QO2Mol is designed to fill gaps in existing datasets, which either lack elemental diversity or are limited to low heavy atom counts, thus promoting more reliable and realistic modeling of organic molecules for various applications. • Benchmarking tests show that models like GemNet achieve the lowest prediction error on potential energy tasks, demonstrating the dataset’s potential in training high-performing models. • This dataset holds promise for significantly advancing quantum chemistry, enhancing machine learning model accuracy, and fostering new drug discoveries with a focus on both equilibrium and near-equilibrium conformers. 💻Code: github.com/saiscn/QO2Mol 📜Paper: arxiv.org/abs/2410.19316 #quantumchemistry #computationalchemistry #machinelearning #drugdiscovery #QO2Mol #opendataset #chemistry #materialscience

Excited to announce that our paper has been accepted and published! 🎉 Check it out here: nature.com/articles/s41597-0… Tutorial: m.bilibili.com/video/BV1bT42… (Keep tuned for the ENG version) #EEG #language #ChineseEEG #OpenDataset