Yossi Matias

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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Exploring the LLM Ecosystem: Key Insights 🌐 The LLM Ecosystem is transforming industries with powerful AI models and tools. Here's a quick breakdown: 1️⃣ Available Large Language Models GPT-4 (OpenAI): Leading in conversational AI. PaLM (Google): Known for reasoning and cross-lingual capabilities. Claude (Anthropic): Prioritizing safety and ethical alignment. LLaMA (Meta) & Mistral: Efficient and open-weight models. 2️⃣ General Use Cases Customer Service: AI chatbots for support. Content Creation: Automated writing for blogs, reports, and more. Code Assistance: Tools for coding help and debugging. Language Translation: Real-time cross-language communication. Healthcare: AI-powered clinical support and research. 3️⃣ Specific Implementations Sales & Marketing: Content generation and lead scoring. Legal Tech: Document review and contract analysis. Education: Personalized learning and tutoring. Finance: Automating reports and customer interactions. 4️⃣ Tools & UIs APIs & SDKs: Easy integration via platforms like OpenAI. UI Platforms: Streamlit and Gradio for user-friendly LLM apps. Fine-Tuning: Hugging Face for model customization. The LLM ecosystem is unlocking opportunities for businesses and developers, driving innovation across sectors. Follow the AI Ka Doctor (Free AI & Data Science Resources)  channel on WhatsApp: https://lnkd.in/dCTCEKKc Follow Habib Shaikh For more such content.

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The 2025 OCP Global Summit kicked off yesterday. The keynotes from all the hyperscalers and GPU vendors point to the same reality - scaling AI isn’t just about re-architecting one part of the stack. It’s about rethinking power, cooling, networking, and trust as one coherent, open system. Some interesting takeaways... ✅ Inference workloads are exploding - exceeding 80% CAGR, significantly outpacing training workloads. Token generation has surged ~50× in just two years, fueled by model complexity, longer context windows, and compute intensity. Sustaining that momentum means revisiting how the data centers deliver power/dissipate heat, and how we architect every layer of the system. ✅ Traditional AC distribution is giving way to high-voltage DC (HVDC) designs such as Google and Microsoft’s Mt. Diablo architecture and Nvidia’s Kyber rack. These systems operate at ±400 V to 800 V DC. Several speakers talked about actively managing power spikes using predictive telemetry - powered by ML - to smooth load transients and stabilize the grid. In effect, the data center is evolving from a static load into an adaptive, interactive participant in the electrical ecosystem. ✅ Cooling has become equally central. Across the keynotes, the message was clear. Liquid cooling is now foundational. Nvidia, Google, Meta, and Microsoft highlighted rack-level liquid-cooling initiatives and standardized coolant-distribution interfaces. Cooling is now a co-engineered subsystem - scaling alongside power and compute lifecycles. 💡 Nvidia pushes deeper into networking. Their announcement of Spectrum-X deployments by Meta, Microsoft, and Oracle suggests Nvidia is positioning itself as a serious contender to Broadcom in scale-out Ethernet, including the Data Center Interconnect (DCI) domain, with it's spectrum-XGS. This is impressive. I always thought it is hard to win DCI market (Nvidia calls it "scale-across") without deep buffer switches for absorbing congestion. It would be interesting to learn about their distance-based load balancing for congestion control in scale-across networks. ❓ AMD joins the Ethernet for Scale-Up Networking (ESUN) work stream. This move raises intriguing questions: Is it linked to their recent collaboration agreement with OpenAI? How will they juggle both UALink and ESUN support simultaneously? 🤔 Plenty more to learn in the next few days, I guess - new technologies, new collaborations, and new directions for open infrastructure. Btw, if you’re there, don’t forget to stop by Astera Labs's booth (B33)!

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𝗛𝗼𝘄 𝗠𝗖𝗣 𝗦𝗲𝗿𝘃𝗲𝗿𝘀 𝗠𝗮𝗻𝗮𝗴𝗲 𝗦𝗲𝘀𝘀𝗶𝗼𝗻𝘀 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠𝘀 𝗮𝗻𝗱 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗪𝗶𝗻𝗱𝗼𝘄𝘀 Managing sessions in LLMs isn’t just about keeping track of prompts. It is about orchestrating memory, context, and tools seamlessly across multiple agents. That’s where MCP servers come in. Here’s the flow I captured visually → Context Window as Working Memory: Holds system prompt, user inputs, assistant responses, tool calls, and rolling context → MCP Servers: Each server maintains its own session state, ensuring consistency across interactions → System Prompt + Tools + Code Loops: Work together to power agentic behavior while grounding outputs in real tasks → LLM Integration: The context is constantly summarized and updated before being sent to the model This structure ensures scalable, multi-session orchestration, which is critical for enterprise AI where multiple tasks and tools interact simultaneously.

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