Subscribe • Previous Issues US vs. China: Who Wins the Critical AI Diffusion Battle? When comparing the United States and China in artificial intelligence, the spotlight is on the development of foundation models. At first glance, America maintains a comfortable numerical lead, producing 40 notable models in 2024 compared to China’s 15. However, looking deeper reveals aContinue reading “The Real AI Race: It’s About Diffusion”
Category Archives: Uncategorized
Navigating Huawei’s AI Hardware: Bright Spots, Backdrop, Barriers
In response to Western technology restrictions, Huawei has advanced its artificial intelligence hardware capabilities, improving manufacturing yields for its Ascend 910C chips while introducing the next-generation Ascend 920. Rather than pursuing peak per-chip performance, where it faces external constraints, the firm employs a scale-out strategy embodied by its CloudMatrix 384 system. This architecture aggregates 384Continue reading “Navigating Huawei’s AI Hardware: Bright Spots, Backdrop, Barriers”
Agents at Work: Navigating Promise, Reality, and Risks
Agents are top of mind for people working in AI. Still, when I talk to professionals building AI applications, many express frustration, highlighting the gap between the intense interest in agents and their relatively limited presence in live enterprise environments. Part of this skepticism is justified—as evidenced by the systemic failure modes we recently exploredContinue reading “Agents at Work: Navigating Promise, Reality, and Risks”
Real-World Lessons from Agentic AI Deployments
Subscribe • Previous Issues Agents at Work: Navigating Promise, Reality, and Risks Agents are top of mind for people working in AI. Still, when I talk to professionals building AI applications, many express frustration, highlighting the gap between the intense interest in agents and their relatively limited presence in live enterprise environments. Part of this skepticism isContinue reading “Real-World Lessons from Agentic AI Deployments”
Beyond the Hype: The Reality Gap in Multi-Agent Systems
The allure of multi-agent systems (MAS), where teams of LLM-based agents collaborate, is undeniable for tackling complex tasks. The theoretical benefits seem clear: breaking down problems, parallelizing work, and leveraging specialized skills promise more sophisticated AI solutions than single agents can deliver. Yet as teams building these systems are discovering, translating this promise into reliableContinue reading “Beyond the Hype: The Reality Gap in Multi-Agent Systems”
Google’s AI Revival
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Why Your Multi-Agent AI Keeps Failing
Subscribe • Previous Issues Beyond the Hype: The Reality Gap in Multi-Agent Systems The allure of multi-agent systems (MAS), where teams of LLM-based agents collaborate, is undeniable for tackling complex tasks. The theoretical benefits seem clear: breaking down problems, parallelizing work, and leveraging specialized skills promise more sophisticated AI solutions than single agents can deliver. Yet asContinue reading “Why Your Multi-Agent AI Keeps Failing”
Model Context Protocol: What You Need To Know
Table of Contents Understanding MCP Basics What is the Model Context Protocol (MCP)? What fundamental problems does MCP aim to solve? Why are these context management problems significant for AI applications? Can you provide a concrete example of how context fragmentation affects AI applications? Current Approaches and Their Limitations What methods do developers currently useContinue reading “Model Context Protocol: What You Need To Know”
Choosing the Right AI Model: Performance, Cost, and Task Specificity
In building AI applications and solutions, three best practices have clearly emerged. First, design your system to remain agnostic about the model provider. Given the steady stream of highly capable models from proprietary vendors like OpenAI, Anthropic, and DeepMind, as well as open-weight providers such as Meta, DeepSeek, and Alibaba. Second, prepare to further customizeContinue reading “Choosing the Right AI Model: Performance, Cost, and Task Specificity”
The AI Model Selection Mistakes You Can’t Afford to Make
Subscribe • Previous Issues Choosing the Right AI Model: Performance, Cost, and Task Specificity In building AI applications and solutions, three best practices have clearly emerged. First, design your system to remain agnostic about the model provider. Given the steady stream of highly capable models from proprietary vendors like OpenAI, Anthropic, and DeepMind, as well as open-weightContinue reading “The AI Model Selection Mistakes You Can’t Afford to Make”