AI - Tag - Shengxu · Cloud Architecture & DevOps

Hands-On: From AI Semantic Search to AI Content Pipeline – How Static Blogs Continuously Evolve (Continued)

Sat, 06 Jun 2026 10:30:00 +0800

A few months ago, I wrote an article titled “Hands-on: Building Fully Automated AI Semantic Search with Cloudflare Vectorize and Gemini”. The problem it solved was clear: enabling semantic search for a static blog and capturing user queries that failed to find results as Content Gaps.

Once that architecture was running, I quickly realized: Search is just the last mile of the content lifecycle.

From the moment a Markdown article is written to when it’s actually discovered by readers, it must pass through summaries, translations, related recommendations, internal links, image optimization, search indexing, SEO, deployment, and quality checks. If these steps still rely on manual processing, even the smartest AI search is just a new entry point bolted onto a traditional publishing workflow.

Two Real Problems in AI Programming: Multi-Project Task Management and Multi-User Collaboration Isolation

Sat, 09 May 2026 16:28:25 +0800

In multi-project, multi-developer AI programming practice, the continuity of task status and the isolation of personal configurations are key pain points affecting efficiency. This article proposes an engineering solution based on “sub-project Source of Truth” and “local rule isolation,” aiming to address cross-project task breakpoint management and team configuration pollution, while providing a replicable directory structure, read/write boundaries, and backup strategy.

Once an engineer starts using AI agents to write code frequently, the problem they quickly encounter isn’t “Can AI write functions?” but a more practical set of issues.

Hands-On: Building an Automated AI Semantic Search with Cloudflare Vectorize and Gemini

Fri, 23 Jan 2026 15:30:00 +0800

In 2026, adding AI search to a personal blog is nothing new. But achieving it with zero cost, full automation, and high performance remains a technical topic worth exploring.

This article breaks down the technical architecture behind this site’s AI Search feature, showing how to combine Cloudflare Workers, Vectorize, D1, and Google Gemini to build a closed-loop RAG (Retrieval-Augmented Generation) system.

1. Core Architecture Design

Our goal is a fully automated workflow: write and deploy. The author only needs to push Markdown articles; everything else—vector generation, index updates, frontend deployment—is automated.