Why I Use RainYun for an Early-stage Website
My first-hand notes on using RainYun for an early-stage content website: what feels stable in my workload, why the price is manageable and what I still check before recommending it.
Field notes, operating decisions and tested methods—organized so you can find the next useful answer in seconds.
17 articles
My first-hand notes on using RainYun for an early-stage content website: what feels stable in my workload, why the price is manageable and what I still check before recommending it.
My practical notes on using WildAI for a ChatGPT subscription, then using GPT and Codex for research, coding, testing and deployment—with limits and account checks included.
Prepare useful pages, crawlable links, metadata, canonical URLs, robots and sitemap, then verify the site and submit it through Google Search Console and Baidu Search Resource Platform.
Create a small operating routine for a first website: health checks, disk and memory monitoring, versioned backups, off-server copies and a tested restore procedure.
Understand Caddy automatic HTTPS, verify DNS and ports in order, inspect service logs and solve common certificate failures without destructive guesswork.
Upload a local HTML and CSS site to a safe web directory, install Caddy, configure the domain, validate the configuration and publish without exposing a development server.
Point a root domain and www host to a first server, understand A, AAAA, CNAME and TTL, verify public DNS and remove conflicting records.
Choose a readable domain, compare registration and renewal terms, keep ownership under your control and secure the registrar account before changing DNS.
Explain the supported connection path, network boundaries, verification and common failures without inventing version-specific values. This evidence-led draft helps AI developers and teams building knowledge applications verify assumptions, test the complete path and document a reversible decision before publication.
Explain the decision through application workload, latency, model size and budget rather than generic hardware rankings. This evidence-led draft helps Developers and small teams buying AI infrastructure verify assumptions, test the complete path and document a reversible decision before publication.
Explain crawling controls, discovery, common mistakes and verifiable examples without claiming robots.txt controls indexing. This evidence-led draft helps Developers, site owners and technical marketers verify assumptions, test the complete path and document a reversible decision before publication.
Build a decision framework covering workflow control, integrations, deployment, observability and team fit. This evidence-led draft helps Developers, founders and automation teams verify assumptions, test the complete path and document a reversible decision before publication.
Teach a method for estimating memory and storage while clearly separating model facts from environment-dependent performance. This evidence-led draft helps Developers and privacy-focused AI users verify assumptions, test the complete path and document a reversible decision before publication.
Compare product scope, deployment, workflow design, API use and operational trade-offs using verifiable facts. This evidence-led draft helps AI developers and teams building knowledge applications verify assumptions, test the complete path and document a reversible decision before publication.
Cover prerequisites, isolated environment, installation, model placement, launch and validation without hard-coding unstable versions. This evidence-led draft helps AI image creators and technical operators verify assumptions, test the complete path and document a reversible decision before publication.
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Understand Dify's documented minimum, why a complete Compose stack needs headroom and which measurements should drive production sizing.