[Nightwalker Tech] is the tech sharing channel of "Nightwalker", focusing on AI and large model technologies, internet architecture design, high‑performance networking, and server‑side development (Golang, Python, Rust, PHP, C/C++).
Looking back over ten years, the author revisits earlier blog posts and shares personal insights and judgments about the Go and Rust programming languages, concluding that choices often outweigh effort in shaping one’s technical path.
This article provides a comprehensive tutorial on building the core components of a Transformer model—including multi‑head attention, layer normalization, feed‑forward networks, encoder and decoder layers—and assembles them into a complete PyTorch implementation, supplemented with explanatory diagrams and runnable code.
This article explains how to build the input processing stage of a Transformer—including tokenization with Hugging Face tokenizers, token‑to‑embedding conversion using BERT models, custom BPE tokenizers, and positional encoding—providing complete Python code examples and test results.
This article explains what GPT (Generative Pre‑trained Transformer) is, traces its development from early neural networks to the latest GPT‑4 models, and details the three‑stage training pipeline of unsupervised learning, supervised fine‑tuning, and reinforcement learning with human feedback.
This article provides a comprehensive overview of AI‑generated content (AIGC) for image creation, explaining the role of diffusion models, the architecture of Stable Diffusion—including CLIP, UNet, and VAE—and the underlying mathematical concepts such as Markov chains, Langevin dynamics, and Gaussian distributions.
This article explains what AIGC is, how AI can generate text and images, and provides step‑by‑step guidance, prompt templates, and visual examples for using Midjourney to upgrade existing pictures, create original illustrations, and produce high‑fidelity artwork for various creative scenarios.
This report investigates the impact of large language model‑based AI coding assistants such as Copilot, Tabnine, CodeGeeX, and ChatGPT on frontend and backend development, evaluates their efficiency gains, compares features, pricing, and IDE integration, and provides practical adoption recommendations for development teams.
This article presents a comprehensive engineering guide for building a high‑performance e‑commerce search engine with Go (Kratos), Elasticsearch/OpenSearch, Redis, MySQL, and Kafka, covering architecture, index construction, query processing, ranking, user‑guidance features, and future extensions such as recommendation and visual search.
The discussion outlines practical anti‑spam strategies—including text length limits, keyword replacement, trie‑based data structures, AC automata, Bayesian and vector‑similarity algorithms, and PHP extensions such as libdatrie—while also sharing performance metrics and resource links for implementing robust content filtering systems.
A diverse group of practitioners share recommendations for books, technologies, real‑world use cases, and practical challenges when learning and applying big‑data processing, covering Hadoop, Spark, data visualization, ETL, and the relationship between data, algorithms, and business value.
