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CodeNodeX AI 社区管理员，关注 AI 编程、Next.js 和独立开发。
LLM inference is slow mainly in decode, where tokens are generated one at a time. This article explains KV Cache, which avoids recomputing history, and Speculative Decoding, where a small model drafts tokens for a large model to verify in parallel. Combined with continuous batching and quantization, they can raise throughput by an order of magnitude.
AI does not have to run in the cloud. Privacy, latency, offline access, and cost are making on-device inference relevant again. This article explains the state of on-device AI: models (quantized 1B–7B), frameworks (llama.cpp/MLC/ExecuTorch), environments (mobile, WebGPU browsers, and desktop), suitable use cases, and current limitations.
How do you know whether a new prompt or model is actually better? AI A/B testing must account for nondeterministic outputs, subjective quality, and large sample requirements. This guide covers metrics, offline evaluations, online experiments, and how to replace intuition with measurable product outcomes.
Cold starting AI products in 2026 is harder—homogenization, raised expectations, and demos that don't convert. Learn the reality of AI cold start, what to avoid before PMF, and three real differentiation paths (vertical depth, data flywheel, workflow closed loop) to find footing in the AI red sea.
Software engineering has TDD; AI engineering needs EDD, or eval-driven development. Build an eval set first to define "good," run it on every change, and block regressions. This article covers the workflow, its nondeterministic and probabilistic differences from TDD, implementation, and how EDD turns intuition-led AI tuning into an engineering feedback loop.
Copilot, Cursor, and Claude Code are great—but can you build your own code generator? This article breaks down the engineering core: context construction, generation strategy (completion/edit/agent), quality guardrails (syntax/test/diff), and how an internal code assistant differs from a general-purpose AI tool: scenario focus plus codebase awareness.
"AI apps are natively multilingual (models know dozens of languages), but 'answering in multiple languages' and 'product internationalization' are different. This article clarifies AI app i18n: UI translation, model output language control, regional compliance, cultural adaptation, multilingual RAG, and a next-intl architecture."
A pretrained model is an "autocomplete machine"; alignment methods such as RLHF and DPO turn it into a useful assistant. This article explains what alignment does, how RLHF and DPO differ, why alignment improves both safety and capability, and why application developers must understand its limits, including over-refusal and how to handle it.
Tables are the hard part of RAG: chunking breaks structure, vectors lose meaning, and retrieval misses them. This article covers the full table-RAG approach: detection and extraction, whole-table and row-level chunking, structured storage vs. vectors, Text-to-SQL, and hybrid retrieval.
AI accessibility has two layers: the product must be accessible, and AI can empower accessibility. This article covers AI product a11y essentials—semantic HTML/keyboard/ARIA/dynamic content, a11y control for AI-generated UI, AI empowerment (image descriptions/speech/text simplification), and an automated + manual a11y testing workflow.
Test debt hurts every team, and AI test generation can ease it—but also produce meaningless tests and fake coverage. Covers the right way: which tests suit AI, the generate→validate→accept workflow, avoiding the coverage trap, tool selection (Claude Code/Codium/in-house), and the parts AI can't replace.
Users' original questions are often poor search queries: they contain unresolved references, are too vague, or bundle multiple questions. Query rewriting is a high-ROI step in RAG and search. This article covers five techniques, when to use them, how to implement them, their costs, and how to evaluate rewriting itself.