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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.
This article analyzes the architecture, core capabilities, and workflow orchestration of LLM‑native application platforms Tasking AI and Dify, comparing their microservice designs, plugin management, multi‑tenant isolation, and GraphEngine execution to highlight strengths, trade‑offs, and future development trends.
In a series of candid interviews, dozens of seasoned programmers share their career paths, current tech stacks, favorite AI tools, moments of professional crisis, and personal philosophies, offering valuable insights for anyone navigating the fast‑changing world of software development.
The article details how Tencent News' PUSH platform was re‑architected—consolidating modules, unifying the tech stack to Go, building an in‑house message channel, and introducing batch IO and priority scheduling—resulting in a 70% cost cut, 3.5‑fold throughput increase, and dramatically lower latency.
The article explains that large language models are fundamentally probabilistic predictors, causing their success rate to drop exponentially with task complexity (the pⁿ dilemma), and proposes a systematic, human‑centered approach—using deterministic tools, narrowing prompt scope, and delivering incremental results—to create reliable AI‑human collaborative systems.
This article explores how AI‑assisted pair programming can transform the entire software development lifecycle—from planning to production—by applying the PDCA cycle, prompt engineering, and context engineering, offering practical tips, real‑world scenarios, and future outlooks for developers.
