by jsendak | Aug 20, 2025 | DS Articles
It’s easy to assume that more data—or cleaner dashboards—will automatically lead to better decisions. But after working in product analytics at MAANG and top fintech companies, I’ve learned the hard way: the link between data and decision-making isn’t automatic. It’s something you have to design for. Even in data-rich environments, I’ve seen brilliant teams make… Read More »Why good data doesn’t guarantee good decisions
Key Findings: Data Doesn’t Automatically Lead to Better Decisions
The text highlights a key point seen in many businesses and industries today – simply having more data or cleaner dashboards does not inherently equate to improved decision-making. This insight is especially significant in the modern world where, due to technological advancements, companies have an abundance of data at their disposal. While one might think that more data would naturally lead to better decisions, this isn’t always the case without appropriate design and interpretation.
Long-term Implications
The assumption that higher volumes of data automatically generate quality decisions can impact businesses significantly in the long run. Not only can it lead to ineffective decisions and policies, but it can also cultivate an over-reliance on quantity over quality of data. Over time, this scenario could result in stagnation or decline in business performance and competitiveness as crucial details may be masked or overlooked in the vast amount of data.
Possible Future Developments
Moving forward, it’s likely that we’ll see more emphasis on data interpretation and analysis within organizations. We might see the role of data scientists and analysts becoming more crucial, working alongside business teams to turn raw data into actionable solutions. Also, as artificial intelligence (AI) and machine learning (ML) technologies advance, businesses may start using these tools to help analyze and interpret their data.
Actionable Advice
- Focus on Quality, Not Quantity: Rather than putting all your efforts into collecting as much data as possible, focus on collecting data that is relevant and necessary for your business.
- Invest in Data Analysis and Interpretation: The value in data lies in the insights you can derive from it. Investing in data analysts or services can help you make the most of the data you have.
- Adopt data-driven culture: Encourage all members of the team to understand the value of data and to use it in their decision-making process. The key is to make data accessible, understandable, and actionable for everyone.
- Leverage Technology: Explore how technologies such as AI and ML can be applied to your data to uncover insights and trends that may not be immediately apparent.
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by jsendak | Aug 20, 2025 | Namecheap
Understanding TikTok’s Insight Spotlight for E-commerce Success
As e-commerce continues to burgeon, digital marketers are relentlessly seeking innovative ways to harness social media for engaging potential customers. TikTok, the social media phenomenon, has emerged as a valuable platform in this pursuit, offering unparalleled access to real-time user data through its feature, Insight Spotlight. In an environment where trends evolve at breakneck speed, TikTok’s Insight Spotlight provides e-commerce marketers with what might be considered a new kind of superpower: a dynamic, real-time window into the ever-changing landscape of user interests, behaviors, and community shifts.
Critical Analysis of Insight Spotlight’s Features
Treading beyond traditional analytics, Insight Spotlight offers a granular look into the TikTok community, presenting both opportunities and challenges for marketers. As we delve deeper into its capabilities, we will explore how this tool stands to redefine audience engagement and what it means for the future of e-commerce marketing. The following themes will be critically examined to provide readers with a comprehensive understanding of TikTok’s new feature:
- Real-time Data: The advantages of having access to instantaneous user data and how it can inform marketing strategies on the fly.
- User Interests and Behaviors: Deciphering the complex web of TikTok user engagement to tailor content that resonates with target demographics.
- Community Shifts: Keeping pace with the dynamic TikTok community and leveraging trends for optimal brand positioning.
- Competitive Edge: How Insight Spotlight can be utilized to gain a competitive advantage in the highly saturated e-commerce marketplace.
- Marketing Innovations: The role of Insight Spotlight in fostering creative marketing solutions that align with the unique TikTok ecosystem.
By critically engaging with these topics, our article prepares the reader for an in-depth exploration of how Insight Spotlight is not just a tool but a gateway into the nuanced world of TikTok e-commerce marketing. In the digital age where timing is everything, and the next trend is just a scroll away, it is imperative for marketers to comprehend the power of Insight Spotlight and how it could transform their approach to reaching consumers.
Looking Ahead: The Evolving Role of Social Media Insights in Business
The journey into TikTok’s Insight Spotlight reflects the broader narrative of how businesses must continually adapt to the evolving digital landscape. The feature is a testament to the growing importance of social media insights in shaping not only marketing campaigns but overall business strategies. As we dissect the implications of TikTok’s novel offering, we also pave the way for discussion on the future intersection of social analytics and business intelligence.
“The insightful marketer is one who can surf the waves of social change with agility and finesse, and TikTok’s Insight Spotlight could just be the surfboard they need in the sea of e-commerce marketing.” – An E-commerce Strategist
TikTok’s Insight Spotlight gives e-commerce marketers a new kind of superpower: a real-time window into user interests, behaviors, and community shifts.
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by jsendak | Aug 20, 2025 | Computer Science
arXiv:2508.13402v1 Announce Type: new
Abstract: Live streaming has experienced significant growth recently. Yet this rise in popularity contrasts with the reality that a substantial segment of the global population still lacks Internet access. The emergence of Low Earth orbit Satellite Networks (LSNs), such as SpaceX’s Starlink and Amazon’s Project Kuiper, presents a promising solution to fill this gap. Nevertheless, our measurement study reveals that existing live streaming platforms may not be able to deliver a smooth viewing experience on LSNs due to frequent satellite handovers, which lead to frequent video rebuffering events. Current state-of-the-art learning-based Adaptive Bitrate (ABR) algorithms, even when trained on LSNs’ network traces, fail to manage the abrupt network variations associated with satellite handovers effectively. To address these challenges, for the first time, we introduce Satellite-Aware Rate Adaptation (SARA), a versatile and lightweight middleware that can seamlessly integrate with various ABR algorithms to enhance the performance of live streaming over LSNs. SARA intelligently modulates video playback speed and furnishes ABR algorithms with insights derived from the distinctive network characteristics of LSNs, thereby aiding ABR algorithms in making informed bitrate selections and effectively minimizing rebuffering events that occur during satellite handovers. Our extensive evaluation shows that SARA can effectively reduce the rebuffering time by an average of $39.41%$ and slightly improve latency by $0.65%$ while only introducing an overall loss in bitrate by $0.13%$.
Expert Commentary on Live Streaming over Low Earth Orbit Satellite Networks
Live streaming has become increasingly popular in recent years, but there remains a digital divide due to the lack of Internet access for a significant portion of the global population. The emergence of Low Earth orbit Satellite Networks (LSNs), like SpaceX’s Starlink and Amazon’s Project Kuiper, offers a promising solution to bridge this gap. However, a recent measurement study has highlighted a significant challenge – the frequent satellite handovers in LSNs can cause disruptions in video streaming, leading to rebuffering events that affect the viewing experience.
This is where the concept of Satellite-Aware Rate Adaptation (SARA) comes into play. By intelligently modulating video playback speed and leveraging insights from the unique network characteristics of LSNs, SARA enhances the performance of live streaming over these satellite networks. By working in conjunction with existing Adaptive Bitrate (ABR) algorithms, SARA helps in making informed bitrate selections to minimize rebuffering events during satellite handovers.
From a multi-disciplinary perspective, SARA’s approach combines elements of network optimization, machine learning, and video streaming technologies. By integrating seamlessly with ABR algorithms, SARA showcases the potential of collaboration between different domains to address complex challenges in multimedia information systems.
Looking ahead, advancements in technologies like SARA could have implications beyond live streaming over LSNs. The principles of satellite-aware rate adaptation could also be applied to other areas such as animations, artificial reality, augmented reality, and virtual reality, where smooth and uninterrupted delivery of multimedia content is crucial.
The results of the evaluation of SARA are promising, with significant reductions in rebuffering time and slight improvements in latency, showcasing the potential of this middleware to enhance the viewing experience over LSNs. As the demand for high-quality multimedia content continues to grow, innovative solutions like SARA will play a pivotal role in shaping the future of live streaming and multimedia information systems.
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by jsendak | Aug 20, 2025 | AI
arXiv:2508.13167v1 Announce Type: new
Abstract: Recent advances in large language models (LLMs) and multi-agent systems have demonstrated remarkable capabilities in complex problem-solving tasks such as deep research, vibe coding, and mathematical reasoning. However, most existing multi-agent systems are built upon manual prompt/workflow engineering with sophisticated agent frameworks, making them computationally inefficient, less capable, and can not benefit from data-centric learning. In this work, we introduce Chain-of-Agents (CoA), a novel paradigm of LLM reasoning that enables native end-to-end complex problem-solving in the same way as a multi-agent system (i.e., multi-turn problem solving with multiple tools and multiple agents) within one model. In chain-of-agents problem-solving, the model dynamically activates different tool agents and role-playing agents to simulate multi-agent collaboration in an end-to-end fashion. To elicit end-to-end chain-of-agents problem-solving abilities in LLMs, we introduce a multi-agent distillation framework to distill state-of-the-art multi-agent systems into chain-of-agents trajectories for agentic supervised fine-tuning. We then use agentic reinforcement learning on verifiable agentic tasks to further improve the models’ capabilities on chain-of-agents problem solving. We call the resulting models Agent Foundation Models (AFMs). Our empirical studies demonstrate that AFM establishes new state-of-the-art performance across diverse benchmarks in both web agent and code agent settings. We make the entire research, including the model weights, code for training and evaluation, and the training data, fully open-sourced, which offers a solid starting point for future research on agent models and agentic RL.
Expert Commentary: Innovations in Chain-of-Agents (CoA) Model for Problem-Solving
The recent developments in large language models (LLMs) and multi-agent systems have significantly advanced the capabilities of complex problem-solving tasks, including deep research, vibe coding, and mathematical reasoning. However, traditional multi-agent systems often rely on manual prompt/workflow engineering, which can be computationally inefficient and less adaptable to data-centric learning processes.
This work introduces an innovative approach called Chain-of-Agents (CoA), which revolutionizes LLM reasoning by enabling native end-to-end complex problem-solving within a single model. CoA mimics the dynamics of multi-agent collaboration by activating different tool agents and role-playing agents in a seamless, multi-turn problem-solving process. This paradigm shift allows for more efficient and effective problem-solving strategies.
To enhance the capabilities of LLMs in chain-of-agents problem-solving, the authors propose a multi-agent distillation framework to train Agent Foundation Models (AFMs). By distilling state-of-the-art multi-agent systems into CoA trajectories and leveraging agentic reinforcement learning, the AFMs achieve superior performance across various benchmarks in web agent and code agent settings.
Multi-Disciplinary Implications
The concepts presented in this research have profound multi-disciplinary implications. The integration of language models, multi-agent systems, and reinforcement learning techniques opens up new possibilities for solving complex problems across diverse domains. By combining insights from natural language processing, artificial intelligence, and cognitive science, the CoA model exemplifies the power of interdisciplinary collaboration in pushing the boundaries of problem-solving capabilities.
Furthermore, the decision to make the research transparent and open-source sets a precedent for fostering collaboration and innovation in the field of agent models and agentic reinforcement learning. The availability of model weights, training code, and data enables researchers to build upon this work and drive further advancements in AI-driven problem-solving methodologies.
In conclusion, the Chain-of-Agents model represents a significant step forward in enhancing the capabilities of LLMs for complex problem-solving tasks. By leveraging multi-agent collaboration dynamics and reinforcement learning, the AFMs demonstrate state-of-the-art performance and pave the way for future developments in the intersection of language models, agent systems, and AI technologies.
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by jsendak | Aug 20, 2025 | GR & QC Articles
arXiv:2508.13221v1 Announce Type: new
Abstract: We present the most general class of charged black hole solutions in third-order Lovelock gravity within even-dimensional spacetimes in the presence of an electromagnetic field. These solutions feature nonconstant-curvature horizons that affect geometry when n>=8. The near-origin behavior of the metric reveals a timelike singularity for electrically charged cases, in contrast to the spacelike singularity found in the uncharged case. We investigate thermodynamic stability in both the grand canonical and canonical ensembles. In the grand canonical ensemble, stability is determined by the positivity of both the Hessian determinant and the temperature. In the canonical ensemble, the sign of the heat capacity governs stability. We identify both first- and second-order phase transitions, including a van der Waals-like behavior characterized by instability at intermediate black hole sizes. Our results reveal a rich phase structure influenced by Lovelock corrections and electromagnetic fields, and demonstrate how conserved charges affect black hole evaporation and stabilization.
Conclusions
The study of charged black hole solutions in third-order Lovelock gravity within even-dimensional spacetimes has revealed a rich phase structure influenced by Lovelock corrections and electromagnetic fields. These solutions feature nonconstant-curvature horizons that impact geometry in significant ways, particularly when n>=8. The near-origin behavior of the metric shows a timelike singularity for electrically charged cases, unlike the spacelike singularity seen in uncharged scenarios. Thermodynamic stability in both the grand canonical and canonical ensembles depends on various factors, including the Hessian determinant, temperature, and heat capacity. First- and second-order phase transitions, as well as van der Waals-like behavior, have been identified, highlighting the complexity of black hole systems under these conditions.
Future Roadmap
- Explore further the implications of Lovelock corrections on black hole thermodynamics
- Investigate the interplay between electromagnetic fields and black hole stabilization mechanisms
- Study the effects of conserved charges on black hole evaporation processes
- Develop numerical simulations to analyze the behavior of black hole solutions in third-order Lovelock gravity
- Consider applications of these findings to other areas of theoretical physics, such as string theory and holography
Potential Challenges
- Complexity of mathematical frameworks in higher-order gravity theories
- Computational limitations for analyzing black hole solutions in multidimensional spacetimes
- Difficulty in extrapolating results to real-world observational data
Opportunities on the Horizon
- Enhanced understanding of the thermodynamic properties of charged black holes in exotic gravity theories
- Potential insights into fundamental aspects of black hole physics and gravitational interactions
- Implications for the development of novel theoretical approaches to studying complex spacetime geometries
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