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Tencent Cloud Developer

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Latest from Tencent Cloud Developer

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Tencent Cloud Developer

Jan 27, 2026 · Cloud Computing

Deploy Clawdbot on Tencent Cloud Lighthouse in Minutes – Step‑by‑Step Guide

This tutorial walks you through deploying the AI‑powered Clawdbot agent on Tencent Cloud Lighthouse, covering server creation, SSH access, one‑click template installation, interactive onboarding configuration, model and Discord integration, gateway startup, pairing, and verification to achieve a continuously running personal AI assistant.

AI AgentClawdbotLighthouse

0 likes · 15 min read

Deploy Clawdbot on Tencent Cloud Lighthouse in Minutes – Step‑by‑Step Guide

Tencent Cloud Developer

Jan 20, 2026 · Artificial Intelligence

From Transformers to Agents: A Complete Timeline of Large Language Model Evolution

This article traces the evolution of large language models from the 2017 Transformer breakthrough through successive milestones such as BERT, GPT‑3, RL‑HF alignment, multimodal extensions, open‑source alternatives, and the rise of retrieval‑augmented generation, AI agents, and emerging protocols that shape modern AI applications.

Open-source ModelsRAGlarge language models

0 likes · 44 min read

From Transformers to Agents: A Complete Timeline of Large Language Model Evolution

Tencent Cloud Developer

Jan 14, 2026 · Artificial Intelligence

Turn Simple Text into Detailed AI Image Prompts: A Step‑by‑Step Guide

This guide explains how to use advanced AI models such as Gemini, Midjourney, and Stable Diffusion to expand brief, informal user descriptions into comprehensive, high‑quality English prompts that include visual style, subject details, environment, lighting, and camera parameters for image or video generation.

AI Prompt EngineeringMidjourneyPrompt Design

0 likes · 14 min read

Turn Simple Text into Detailed AI Image Prompts: A Step‑by‑Step Guide

Tencent Cloud Developer

Jan 7, 2026 · Artificial Intelligence

How Context Engineering Powers the Next Generation of AI Agents

Transitioning from simple chatbots to sophisticated agents, this article explains how expanding context becomes a core variable, detailing the evolution from prompt engineering to context engineering, the challenges of managing growing context, and practical solutions like structured context, tool integration, and the MCP framework for reliable AI systems.

AgentLLMTool Integration

0 likes · 20 min read

How Context Engineering Powers the Next Generation of AI Agents

Tencent Cloud Developer

Dec 30, 2025 · Backend Development

Mastering Microservices: Design Principles, Service Modeling, Integration, and Scaling Strategies

This comprehensive guide explains microservice fundamentals, when to adopt them, key design principles, service modeling techniques, integration patterns, versioning, data handling, monolith decomposition, Conway's law, scaling tactics, and the situations where microservices may not be the right choice, providing actionable insights for building resilient backend systems.

ArchitectureIntegrationMicroservices

0 likes · 23 min read

Mastering Microservices: Design Principles, Service Modeling, Integration, and Scaling Strategies

Tencent Cloud Developer

Dec 24, 2025 · Backend Development

How IMA Scaled Its AI Knowledge Base from Monolith to Micro‑services

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.

AI architectureAsynchronous ProcessingKnowledge Base

0 likes · 14 min read

How IMA Scaled Its AI Knowledge Base from Monolith to Micro‑services

Tencent Cloud Developer

Dec 23, 2025 · Artificial Intelligence

How ReAct (Reasoning + Acting) Empowers LLM Agents to Solve Real‑World Tasks

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.

AI automationLLM agentsreasoning and acting

0 likes · 29 min read

How ReAct (Reasoning + Acting) Empowers LLM Agents to Solve Real‑World Tasks

Tencent Cloud Developer

Dec 17, 2025 · Artificial Intelligence

How Tencent’s TNC Neural Codec Won 2025 Image & Video Compression Challenges

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.

AICompression ChallengeINR

0 likes · 17 min read

How Tencent’s TNC Neural Codec Won 2025 Image & Video Compression Challenges

Tencent Cloud Developer

Dec 9, 2025 · Artificial Intelligence

How Do Large Language Models Turn Text into Math? A Deep Dive into Transformers

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.

Self-AttentionTokenizationinference optimization

0 likes · 29 min read

How Do Large Language Models Turn Text into Math? A Deep Dive into Transformers

Tencent Cloud Developer

Dec 4, 2025 · Artificial Intelligence

From Tapestry to LLMs: 30+ Years of Recommender System Evolution

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.

Deep LearningIndustrial Deploymentcollaborative filtering

0 likes · 38 min read

From Tapestry to LLMs: 30+ Years of Recommender System Evolution