Crypto presales are no longer judged the way they were a few years ago. The change did not come from a single market crash or regulatory decision. It came from experience. Investors, builders, and observers have simply seen enough outcomes to recognize patterns that were invisible before. What once passed as ambition is now often read as overreach, and what used to look slow can now signal discipline. From fast raises to structural credibility In earlier cycles, the […]

Ver mais

This article describes a hypothetical but realistic scenario, synthesized from patterns commonly observed in large enterprise and retail BI environments. While inspired by real-world experiences, the situation, figures, and timelines have been intentionally anonymized and generalized. Our team owned the data. We owned the ingestion pipelines. We owned the transformations. We owned the semantic model that powered enterprise reporting. The dashboard, however, was built by another team – on top of the model we maintained. That’s how self-serve […]

Ver mais

Organizations that have become more integrated into data science are changing from analytics to intelligence. Economic intelligence does not consist of building more models or dashboards; it is embedding a structured way of reasoning into decision systems. An econometrics-first approach provides a strong roadmap for designing those systems and to ensure that data-driven insights are communicated to action that is sound, and coherent. From Analytics Outputs to Decision Systems Conventional analytics pipelines usually have outputs of predictions or […]

Ver mais

Modern web applications commonly face imbalances between frontend and backend rendering and inefficient resource utilization in high-concurrency scenarios. This paper proposes a full-stack collaborative optimization framework integrating React Server Components with Node.js load-tiered scheduling. This approach first enables dynamic switching between frontend SSR and CSR through Async Render Tree Partitioning. It then introduces Nginx reverse proxy and multi-level Redis caching to reduce database access latency. Finally, it employs an event-loop-based Adaptive Task Scheduler on the backend to dynamically […]

Ver mais

Meta has released SAM Audio, a prompt driven audio separation model that targets a common editing bottleneck, isolating one sound from a real world mix without building a custom model per sound class. Meta released 3 main sizes, sam-audio-small, sam-audio-base, and sam-audio-large. The model is available to download and to try in the Segment Anything Playground. Architecture SAM Audio uses separate encoders for each conditioning signal, an audio encoder for the mixture, a text encoder for the natural […]

Ver mais

arXiv:2509.12889v3 Announce Type: replace-cross Abstract: This paper addresses the statistical estimation of Gaussian Mixture Models (GMMs) with unknown diagonal covariances from independent and identically distributed samples. We employ the Beurling-LASSO (BLASSO), a convex optimization framework that promotes sparsity in the space of measures, to simultaneously estimate the number of components and their parameters. Our main contribution extends the BLASSO methodology to multivariate GMMs with component-specific unknown diagonal covariance matrices. This setting is significantly more flexible than previous approaches, […]

Ver mais

This study addresses the challenge of long-term dependency modeling in agent behavior planning for long-horizon tasks and proposes a memory-driven agent planning framework. The method introduces hierarchical memory encoding and dynamic memory retrieval structures, enabling the agent to selectively retain and effectively utilize historical information across multiple time scales, thereby maintaining policy stability and goal consistency in complex dynamic environments. The core idea is to construct an interaction mechanism between short-term and long-term memory, where attention-guided retrieval integrates […]

Ver mais

Human-in-the-loop guidance has emerged as an effective approach for enabling faster convergence in online reinforcement learning (RL) of complex real-world manipulation tasks. However, existing human-in-the-loop RL (HiL-RL) frameworks often suffer from low sample efficiency, requiring substantial human interventions to achieve convergence and thereby leading to high labor costs. To address this, we propose a sample-efficient real-world human-in-the-loop RL framework named method, which requires fewer human intervention by actively selecting informative samples. Specifically, stable reduction of policy entropy enables […]

Ver mais

arXiv:2601.17060v1 Announce Type: new Abstract: Artificially intelligent systems have become remarkably sophisticated. They hold conversations, write essays, and seem to understand context in ways that surprise even their creators. This raises a crucial question: Are we creating systems that are conscious? The Digital Consciousness Model (DCM) is a first attempt to assess the evidence for consciousness in AI systems in a systematic, probabilistic way. It provides a shared framework for comparing different AIs and biological organisms, and for […]

Ver mais