Core Insights - Seer, Inc. has launched the Proteograph Product Suite, which includes the Proteograph ONE Assay and SP200 Automation Instrument, aimed at enhancing mass spectrometry-based proteomics for large-scale studies [1][2][3] Product Launch and Features - The new Proteograph workflow significantly improves scalability, reproducibility, and affordability of deep proteomic analysis, addressing historical limitations in the industry [2][4] - The streamlined workflow can process over 1,000 samples per week on a single SP200 instrument, doubling throughput without compromising performance [7] - The per-sample cost of proteomic analysis has been reduced by approximately 60% compared to the initial release in 2021, making population-scale mass spectrometry proteomics more accessible [3][7] Impact on Research - The Proteograph ONE workflow enables large-scale biomarker discovery, longitudinal disease studies, and comprehensive multi-center research, facilitating advances in personalized medicine [4][8] - The product suite is expected to drive significant advancements in understanding complex diseases such as Alzheimer's and cancer, as showcased at the ASMS 2025 conference [6][10] Scientific Collaboration and Data Presentation - Seer and its collaborators will present new scientific data at the ASMS 2025, highlighting the transformative impact of the Proteograph Product Suite in various research areas [6][10] - Notable presentations will include studies on dementia classification and cancer biomarker discovery, demonstrating the practical applications of the new technology [10][12] Availability - The Proteograph Product Suite will be commercially available starting June 1, with options for instrument and kit purchases as well as services through the Seer Technology Access Center [13]

Core Insights - The AI industry is currently experiencing a significant debate over the effectiveness of pre-training models versus first principles, with notable figures like Ilya from OpenAI suggesting that pre-training has reached its limits [1][2] - The shift from a consensus-driven approach to exploring non-consensus methods is evident, as companies and researchers seek innovative solutions in AI [6][7] Group 1: Industry Trends - The AI landscape is witnessing a transition from a focus on pre-training to exploring alternative methodologies, with companies like Sand.AI and NLP LAB leading the charge in applying multi-modal architectures to language and video models [3][4] - The emergence of new models, such as Dream 7B, demonstrates the potential of applying diffusion models to language tasks, outperforming larger models like DeepSeek V3 [3][4] - The consensus around pre-training is being challenged, with some experts arguing that it is not yet over, as there remains untapped data that could enhance model performance [38][39] Group 2: Company Perspectives - Ant Group's Qwen team, led by Lin Junyang, has faced criticism for being conservative, yet they emphasize that their extensive experimentation has led to valuable insights, ultimately reaffirming the effectiveness of the Transformer architecture [5][15] - The exploration of Mixture of Experts (MoE) models is ongoing, with the team recognizing the potential for scalability while also addressing the challenges of training stability [16][20] - The industry is increasingly focused on optimizing model efficiency and effectiveness, with a particular interest in achieving a balance between model size and performance [19][22] Group 3: Technical Innovations - The integration of different model architectures, such as using diffusion models for language generation, reflects a broader trend of innovation in AI [3][4] - The challenges of training models with long sequences and the need for effective optimization strategies are critical areas of focus for researchers [21][22] - The potential for future breakthroughs lies in leveraging increased computational power to revisit previously unviable techniques, suggesting a cycle of innovation driven by advancements in hardware [40][41]

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Laughing Water Capital is a concentrated, long biased investment partnership open to accredited investors. We focus on owning pieces of businesses that are suffering from temporary problems or that are misunderstood by the market due to the vagaries of GAAP accounting or some sort of structural impediment. We consider our portfolio companies to be our partners, and we look for our management teams to have significant equity ownership in our companies. Properly incentivized, we expect our management teams to ...