Official account of Tencent Technology. A platform for publishing and analyzing Tencent's technological innovations and cutting-edge developments.
This article explains why Python 2 is no longer maintained, outlines the benefits of moving to Python 3—including security, performance, and modern library support—and provides practical step‑by‑step guidance for migrating projects on TencentOS Server 4.
This article explores Tomoro’s Lumos data‑intelligent agent, detailing its multi‑agent architecture, technical design, practical implementations, and performance optimizations that enable seamless, low‑threshold big‑data self‑service analysis powered by AI.
This article systematically examines the root causes of hallucinations in large language models, evaluates their pros and cons, and presents a comprehensive set of optimization techniques—including prompt engineering, RAG, sampling tweaks, supervised fine‑tuning, LoRA, RLHF, chain‑of‑thought reasoning, and agent/workflow designs—to build more reliable and trustworthy AI applications.
After four months of using the AI code assistant Cursor, the author shares a practical methodology that combines clear requirement articulation, pre‑planning, custom rules, and various Model Context Protocol (MCP) tools—such as feedback‑enhanced, sequential‑thinking, and better‑tapd—to streamline development, reduce token waste, and improve collaboration.
This article systematically compares major open‑source deep‑research agent frameworks—including DeerFlow, SmolAgents, LangChainAI, SkyworkAI, and Researcher—detailing their architectures, best practices, and commercial alternatives, to help developers and users choose the most suitable tool for automated research workflows.
The article gathers diverse opinions from eight engineers on whether systematic book study remains essential for programmers in the AI age, arguing that combining foundational reading with AI‑assisted practice builds deeper understanding while pure reliance on AI or rote reading alone falls short.
This article walks through the complete process of creating a WeChat mini‑program that uses large‑language‑model chat to recommend personalized fragrances, covering idea generation, tool selection, prompt design, AI‑assisted debugging, UI/UX refinement, mini‑store integration, and final publishing steps.
This article explores the evolution of AI infrastructure, comparing it with traditional backend systems, and details how hardware shifts to GPU-centric designs, software adaptations like deep learning frameworks, and engineering challenges in model training and inference can be addressed using established backend methodologies.
The A.S.E (AI Code Generation Security Evaluation) framework provides a comprehensive, project‑level benchmark for assessing the safety, quality, and stability of AI‑generated code across multiple languages and vulnerability types, helping developers and researchers evaluate and improve large language model coding assistants.
A collection of engineers' perspectives discusses Go's strengths, challenges, and future prospects over the next five to ten years, highlighting its role in cloud‑native infrastructure, AI limitations, industry adoption, and the importance of continuous learning for developers.
