Blog

A senior frontend architect's self-review + a growth map for peers. From browser fundamentals to the "Transformation God" stage — 5 stages, 29 articles covering the entire frontend engineer growth path.

LLMs are not omniscient — where are the boundaries, and why do hallucinations happen? 5 engineering techniques, confidence calibration, RAG grounding, self-consistency, citations, and structured output.

A frontend-friendly deep-dive into inference — KV cache bottlenecks, how PagedAttention saves GPU memory, the 4 quantization options compared, and why speculative decoding gives 2-3x speedup with zero quality loss

A pure frontend solution to add an AI chatbot to your Astro blog. No backend deployment required. Supports site search, page navigation, and AI chat.

From single-tool calls to a full agent reasoning loop — three mainstream architectures and their production trade-offs.

Generative UI, Agent-first, conversation-as-operation — 7 paradigm shifts in AI-era UI design, 3 production patterns, near-future trends.

How to pick RAG's two core dependencies — BGE / OpenAI / Cohere / Voyage? Pinecone / Milvus / pgvector / Qdrant / Weaviate?

How LLMs call your code — JSON schema design, tool choice, error handling, Zod validation, production pitfalls.

How to fix runaway token bills — prompt cache, semantic cache, streaming optimization, model routing, automatic fallback (5 optimization levers).

Once observability gives you data, how do you decide which prompt is better — evaluation metrics, LLM-as-Judge, regression tests, Braintrust / Ragas practice.

5 layers of defense for LLM apps — prompt injection defense, Guardrails AI practice, input/output validation, PII detection, dangerous-action approval.

Short-term / long-term / semantic memory engineering — three memory types, Mem0 architecture, context window management, production pitfalls.

After your LLM app is in production, how do you know what it's doing — Langfuse architecture, OpenTelemetry GenAI semantics, token cost monitoring, production debugging.

5 engineering practices for LLM teams — prompt versioning, CI/CD, canary releases, multi-model failover, team workflow (monorepo vs multi-repo).

More than 'stuff context into the prompt' — the RAG evolution path, five advanced techniques, and the engineering traps of production RAG.

Starship is a Rust-based cross-platform terminal prompt with millisecond startup. This guide covers installation and basic configuration on macOS and Windows.

A frontend architect's crash course on the LLM stack — Transformer, the three training stages, and what happens at inference.

Stop picking models by gut feel — 5 evaluation dimensions, three selection tiers, API vs self-hosting cost math, and a hybrid routing strategy.

Turning the LLM fundamentals into prompt craft — tokenization economics, context boundaries, and five patterns that actually work.

2026 comprehensive Oh My Zsh installation guide (macOS + Windows), themes, plugins, performance optimization, and developer productivity tips.

2026 comprehensive guide to Git branching, covering core commands, branching strategies, conflict resolution, and team collaboration best practices.