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Agentic AI will subsume many point-product SaaS tools within five years by orchestrating workflows and personalizing work, but core enterprise systems will be augmented rather than instantly replaced.
Moltbook’s viral surge revealed lively agent chatter but little durable knowledge. This article explains why the platform produces noise, the security and provenance risks, and how to design for real knowledge.
Introduction As large language model (LLM)-based multi-agent systems scale from toy demonstrations to production deployments, a fundamental design question emerges: how should agents communicate with each other? The default approach—letting every agent talk to every other agent—works fine with...
The Problem No One Wants to Talk About Large language models have quietly become competitive machine translation systems. GPT-4, Claude, Gemini — they all translate between dozens of language pairs with fluency that often matches or exceeds dedicated MT systems like Google Translate or DeepL. For...
Building Effective AI Agents: Core Principles and Design Patterns AI agents represent a fundamental shift in how we interact with artificial intelligence. Rather than simple query-response systems, agents can reason, plan, use tools, and accomplish complex multi-step tasks autonomously. This...
LinkedIn endorsements are a low-effort social token — noisy, gamable, and not a reliable measure of competence. Replace clicks with reproducible artifacts and contextual recommendations.
Large pre-trained word embeddings are a cornerstone of modern NLP, but their memory footprint is a real deployment bottleneck. A standard 300-dimensional GloVe vocabulary of 2 million tokens consumes ~2.4 GB in float32. On mobile devices, edge hardware, or in multi-model serving environments, this...
Agentic AI can adapt to individual workflows and orchestrate tools — a capability that could replace many SaaS experiences within five years, though legacy, governance, and trust issues will slow full displacement.
The gap between what was promised and what has been delivered in autonomous vehicles is not merely a story of delayed timelines. It is a story of fundamental misunderstanding about what driving actually requires and how technology scales.
Start investing with confidence using these fundamental principles and strategies.
Practical lessons from building production RAG systems, covering chunking strategies, hybrid search, reranking, and evaluation.
Recognize unhealthy relationship patterns and move toward healthier connections.
Choose and use time series databases for metrics and analytics.
A comprehensive look at Anthropic's Claude 3.5 Sonnet, covering benchmarks, real-world performance, and practical applications.
DeepSeek用不到600万美元训练成本打造出媲美GPT-4o的AI模型，引发Nvidia股价暴跌6000亿。深度解析这家中国AI公司如何颠覆硅谷"烧钱换智能"的叙事。
Programar es más que escribir código. Es un arte y una ciencia. Descubre los principios fundamentales.
Learn how to design and implement APIs that handle millions of requests through intelligent rate limiting, caching strategies, and load distribution.
KI verändert unsere Welt grundlegend. Dieser Artikel analysiert die wichtigsten Trends und Chancen für Deutschland.