Official Tencent Cloud community account that brings together developers, shares practical tech insights, and fosters an influential tech exchange community.
This article walks through the end‑to‑end design of IMA's AI‑driven knowledge base, covering its definition, core business flow, architecture evolution, data ingestion pipelines, management challenges, asynchronous processing, permission modeling, and the business value demonstrated by the prototype.
This article explains the ReAct paradigm—combining reasoning, action, and observation—to turn large language models into controllable agents, detailing its core concepts, architecture, workflow, code implementation, application scenarios, advantages over other methods, and future research directions.
At the end of 2025, Tencent’s Shannon Lab’s neural codec TNC achieved top rankings in both the VCIP low‑complexity end‑to‑end image compression contest and the PCS high‑compression intelligent image compression challenge, demonstrating superior PSNR gains, low decoding complexity, and innovative VAE‑INR architecture across image and video tracks.
This article walks through the complete workflow of AI large language models, from turning user queries into token matrices via tokenization and embedding, through the Transformer’s self‑attention and multi‑head mechanisms, to decoding logits into human‑readable text, while also covering position encoding, long‑context strategies, generation parameters, and practical engineering tips.
This article traces the three‑decade evolution of recommender systems—from early collaborative‑filtering prototypes like Tapestry, through the Netflix Prize era and deep‑learning breakthroughs such as Wide&Deep and DIN, to the current generative‑AI wave driven by large language models—highlighting key milestones, technical shifts, industrial deployments, and future challenges.
This article explores how to create a self‑thinking AI data analyst by detailing agent fundamentals, core modules such as planning, memory and tool scheduling, practical development steps, multi‑agent collaboration, evaluation benchmarks, and real‑world examples like stock backtesting.
Since ChatGPT's debut, the LLM landscape has progressed through four stages—prompt engineering, chain orchestration, autonomous agents, and multi‑agent systems—each enhancing intelligence and automation, with this article detailing their evolution, advantages, drawbacks, and practical implementation examples in Go.
This article demystifies the concept of architecture by tracing its origins, explaining the 4+1 and C4 modeling approaches, introducing TOGAF‑4A, and clarifying the role of enterprise architecture in aligning people, processes, technology, and data to manage complexity across large organizations.
This article walks you through the evolution from basic mathematical functions to modern large‑scale models, explaining activation functions, forward and backward propagation, loss calculation, gradient descent, regularization, dropout, word embeddings, RNNs, and the core mechanics of the Transformer architecture.
This article explores the difficulty of establishing a shared language in software engineering, outlines three essential domains and mindsets for developers, delves into demand analysis, product value, user experience, and strategy, and contrasts ToC and ToB approaches while highlighting practical documentation outputs.
