Jean Lee

In 5 days, we’re going to showcase how we're solving one of voice AI's biggest challenges - bridging the gap between what customers say and how they say it. Gnani.ai teams are going to be attending NVIDIA's GTC Conference in San Jose, California on March 17 to exhibit our speech-to-speech AI models.  As one of the first few companies building these models globally, our partnership with NVIDIA to use their innovative platform to solve critical voice AI challenges is crucial and groundbreaking.  Why we’ve been invited to speak: The most challenging piece about voice AI agents is that when you start using them, you realize the software pipeline only looks into the text behind the conversations. As we were implementing voice AI agents for multiple customers, we realized we were missing essential elements: the pitch, the tone, and the way customers react to certain words the bot was saying. So, in addition to solving the classic LLM problems of latency and hallucinations, which continue to be big challenges, we also needed to make the voice AI agents’ conversations more authentic. To do this, we decided to build speech-to-speech AI models for real-time performance, from voice recognition to response generation. Our software stack makes customer interactions fast, natural, and context-aware. This idea resonated with NVIDIA. That’s why we’re building these models on broader NVIDIA architecture. What we’ll be presenting:    At GTC, we’ll be sharing the specific innovations we've invented and what this really means for not just voice AI agents, but the voice AI industry as a whole. We’ll also reveal how our solution: - Is trained on over 14 million hours of multilingual conversational data - Has out-of-the-box support for 14 languages - Is equipped with accurate, context-rich responses that adapt to real-time interactions - Transforms raw data into strategic customer intelligence - Provides insights that elevate enterprise decision-making   - Incorporates industry-leading software like NVIDIA TensorRT-LLM, NVIDIA Triton and NVIDIA Riva The key features we're presenting are about how we’re able to merge multiple elements in the pipeline of a voice AI agent so that we can reduce the latency to 250ms--making interactions human-like. We’ll share the algorithms we used and the pipeline we used to make this possible, as well as the broader NVIDIA architecture that made it possible to design these voice agent flows for customers. It’s going to be great. See you there! Ganesh Gopalan Bharath Shankar Thoshith S Avinash Benki John Philip NVIDIA NVIDIA GTC

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Today, we announced the open sourcing of our AI tools for Online safety at Roblox. We invest significantly in this area in building the tools and running it on our platform spanning Core, Edge, and Cloud to enhance safety for our player community. The need for improving Digital safety in the world of AI is substantial for everyone, and this initiative marks a significant step in that direction that everyone can benefit from. #AISafety #Roblox #DigitalSafety https://lnkd.in/g_bftZCb

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🚀 Big news for AI agent builders: Agent Reinforcement Trainer (ART) now integrates seamlessly with 🦜🔗 LangGraph! If you’ve ever built multi-step, ReAct-style agents, you know the pain: endless prompt tweaking, fragile reasoning, and lots of manual trial-and-error. With this new integration, you can now train your LangGraph agents with reinforcement learning — making them smarter, more adaptive, and more efficient over time. ✨ What this means in practice: * Agents learn to reason through multi-step problems without hand-holding * They improve tool usage automatically (no more prompt spaghetti) * They make smarter decisions in complex workflows * Training is scalable and robust, powered by ART’s reinforcement engine And the best part? It’s a drop-in upgrade: * Swap in ART’s init_chat_model and wrap_rollout * All interactions, tool calls, and reasoning chains get logged automatically * Plug straight into ART’s RULER reward function for training — no custom rewards needed 💡 Imagine your LangGraph prototypes evolving into production-ready agents that actually get better with use. That’s the power of combining LangGraph’s agent framework with ART’s reinforcement learning. 👉 Full docs & examples here: https://lnkd.in/gwWj-xxx Can’t wait to see what the community builds with this. If you’re experimenting with multi-step AI agents, give your agents the ability to learn from experience!

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March 12, 2025 – VictoryXR a provider of immersive education solutions, has recently announced that Kendall Hunt Publishing Company a U.S. textbook publisher, will integrate personalized AI tutors onto textbook pages using newly released technology from VictoryXR. “AI is changing the way students learn and we are starting with personalized tutors embedded onto the pages of textbooks,” said Chad Chandlee, president of Kendall Hunt. VictoryXR stated that the conversational AI tutors will help students learn in three key ways: explaining a passage, summarizing a page, and reading a passage in the language of their choice. Kyle Henderson https://lnkd.in/gdQxVuBn

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Developer docs have evolved. What used to be static reference material is now dynamic, interactive, and increasingly AI-powered. How we're seeing the market shape up: 📚 Docs platforms – Fern, ReadMe, Redocly, Mintlify, GitBook 💻 SDK platforms – Fern, Speakeasy, Stainless 🤖 AI search – Fern, Inkeep, kapa.ai ✍️ AI technical writers – Promptless, Quantstruct (YC W25) Read more about "Why Documentation Is Important" by Ryan T.: https://lnkd.in/gFDWiug7

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$55 million in seed funding just hit the Radical AI books, led by RTX Ventures, with a crew stacked deeper than an ISS launch manifest: nVentures (NVIDIA's VC arm), Eni Next (of Eni S.p.A.), noa, Infinite Capital, and AlleyCorp. That's not just a round, that's a declaration. And the message is clear: #materialsscience isn't slow and academic anymore. It's autonomous, atomically precise, and scaling faster than most folks can spell "interatomic potential." Founded in 2024 and now running full throttle from Midtown Manhattan, Radical AI is rebuilding the entire scientific process from the molecular level up. Joseph F. Krause, PhD, ex-Army National Guard CBRN Specialist, Army Research Lab fellow, and AlleyCorp investor, partnered with serial builder and AlleyCorp Board Partner Jorge Colindres to construct something we don't see enough of: a deep tech company with actual technical depth. Add Gerbrand Ceder to the founding trio, Samsung Distinguished Chair at University of California, Berkeley, 550+ publications, 130,000 citations, and more patents than most folks have unread emails, and you've got a team calibrated for moonshots, not MVPs. Their product? A fully autonomous lab, an A-Lab, capable of running 50 to 100 times more materials science experiments than traditional setups, without needing a human to refill the coffee pot. It's AI, #robotics, and molecular #quantum mechanics choreographed to a single tempo. Inside is TorchSim, their PyTorch-native #simulation engine running atomistic calcs at speeds that make DFT look like dial-up. They've built their own supercomputer. Created the world's fastest #MachineLearning Interatomic Potential. Dropped the largest known #datasets in materials science. Then mapped out a nine-tool full-stack robotic lab and launched the OS to run it. And they did all this in a year. Radical AI isn't playing the game. They're changing the field. Their platform doesn't treat discovery, creation, and deployment as a sequence. It's one flow, one intelligence, merging #physics with robotics and computation into an autonomous loop. That's not disruption. That's reconstitution. And the industries paying attention? #Defense. #Energy. #Biotech. #Space. #Semiconductors. All the arenas where slow science bottlenecks fast futures. #Startups #StartupFunding #VentureCapital #SeedRound #AI #Materials #MaterialsTech #Research #Robotics #RobotTech #DeepTech #Infrastructure #Technology #Innovation #TechEcosystem #StartupEcosystem If engineering peace of mind is what you crave, Vention is your zen.

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Introducing Agentic Culture — an open-source experiment in collective memory and in-context cultural accumulation for multi-agent systems. A hot topic from the recent Andrej karpathy // dwarkesh podcast is that LLMs don't yet have culture. >>> So we built the scaffolding for them to develop one. Agno Agents can now tap into a shared cultural memory — knowledge that persists beyond individual sessions, users or memories. Culture is collective, evolving context that shapes agent behavior over time. Now they can create, read, explore and learn from their culture. You can use the `CultureManager` class to create and manage `CulturalKnowledge` objects. These are stored in your database of choice (SQLite for now) and automatically retrieved as in-context cultural grounding for your agents. To give your agents access to shared culture, just set `add_culture_to_context=True`. That's it. Agents now learn from collective experience. Explore the code and README: https://lnkd.in/e7E3AcJX Agno is open source — come build with us: https://agno.link/gh

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NEW 🤘 Owkin MEGA COOL MODEL RELEASE. Open for the community of course We’re thrilled to publicly launch HistoPLUS, a state-of-the-art model for pan-cancer nuclei detection, segmentation and classification in H&E-stained pathology images. The model weights and an open implementation are now available. Key Highlights: 🔬 Unlocks robust detection, segmentation & classification of 13 cell types — including understudied immune populations such as neutrophils and eosinophils. 📈 Outperforms SOTA with 5% better detection and 24% higher F1 classification, using 5x fewer parameters. 🌍 Generalizes across multiple cancer types. 🧑🏼‍💻 GitHub Repository: https://lnkd.in/eGFCa-c9 🤗 Model Weights: https://lnkd.in/eFpBB7x4 📄 Preprint: https://lnkd.in/etPtSKph HistoPLUS is built on top of the powerful H0-mini foundation model, developed in collaboration with our dear Bioptimus’ team. This study also makes use of data generated by the MOSAIC research consortium Gustave Roussy, CHUV / Centre hospitalier universitaire vaudois, Universitätsklinikum Erlangen, Charité - Universitätsmedizin Berlin, University of Pittsburgh 🙏 Huge thanks to the expert pathologists whose invaluable annotations made this work possible—

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