I Built an ML-Powered Email Validation API
The article discusses the development of an ML-powered email validation API designed to identify disposable emails. It utilizes an XGBoost model to assess the legitimacy of email addresses based on various features. The API combines traditional validation methods with machine learning to improve accuracy in distinguishing between valid and disposable emails.
- ▪The API uses an XGBoost model to catch auto-generated disposable emails that traditional methods may miss.
- ▪It supports batch validation of up to 30 emails per request and excludes SMTP validation to reduce latency.
- ▪The model analyzes features such as digit count, length, and consonant/vowel ratios to predict email legitimacy.
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
inspect →
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/jp1/i-built-an-ml-powered-email-validation-api-1o86 |
| Publication time | Tue, 19 May 2026 10:17:47 +0000 |
| Retrieval time | 2026-05-19T10:34:57.546Z |
| Last seen | 2026-05-19T10:34:57.546Z |
| 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 | OIgRViZXCfvi |
| 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 === 3939733) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Ozhaya Posted on May 19 I Built an ML-Powered Email Validation API #python #ai #api #machinelearning I built an ML model using XGBoost to catch auto-generated disposable emails when blacklists can't keep up. Most validators rely on MX records, SMTP checks, or blacklists - disposable emails have real mailboxes so MX and SMTP return valid. That's why I added an ML model to determine the risk of accepting an email based on the username and domain.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
Discussion
0 commentsMore from DEV.to (Top)
