How I Built a Review Site with 800+ Articles Using AI
The article discusses the creation of a review site focused on Chinese consumer brands using AI. It outlines the technology stack, content workflow, and lessons learned from the process. The author emphasizes the importance of real user feedback and consistent publishing over SEO tricks.
- ▪The review site features over 800 articles across 19 categories.
- ▪AI is used for research and formatting, while humans ensure quality and depth.
- ▪The site has seen steady organic traffic growth since its launch.
DEV.to (Top) files mainly under programming. We currently carry 4,924 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/_1a008d053e73e4a54d13a/how-i-built-a-review-site-with-800-articles-using-ai-5fle |
| Publication time | Sat, 23 May 2026 04:35:47 +0000 |
| Retrieval time | 2026-05-23T05:07:24.929Z |
| Last seen | 2026-05-23T05:07:24.929Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 12oduVpSeLMO |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3947051) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } 武乐丹 Posted on May 23 How I Built a Review Site with 800+ Articles Using AI #webdev #nextjs #ai #showdev How I Built a Review Site with 800+ Articles Using AI The stack, the workflow, and what actually worked A few months ago, I wanted to build a review site for Chinese consumer brands — products like GaN chargers, USB-C hubs, smart home devices, and laptops that are popular in Asia but don't get much coverage in English-language tech blogs.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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