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Dirty data is a quiet disaster. Duplicate contacts, inconsistent formats, missing fields, & records across Mailchimp, HubSpot, Klaviyo, Shopify, and Salesforce -- most teams live with this mess because cleaning it properly requires hours of manual work nobody has time for. CleanSmart fixes that.
It runs your data through four AI-powered steps in a single pass: SmartMatch finds and merges duplicate records using semantic similarity (so "Jon Smith" and "John Smith" at the same company get caught), AutoFormat standardizes phone numbers, emails, and addresses, SmartFill predicts and fills missing values, and LogicGuard flags anomalies before they corrupt your analytics.
Every change gets a confidence score. High-confidence fixes happen automatically. Anything the AI isn't sure about gets routed to you for review -- with the original value, the suggested value, and the reasoning behind it. You stay in control. Nothing changes in your data without a full audit trail.
Connect directly to your existing platforms via OAuth. No CSV exports, no re-importing, no duct tape. Built by a consultant who spent 20 years watching businesses lose revenue to data they couldn't trust. CleanSmart is the tool I kept wishing existed.
GitHub CleanSmartCleanSmart's answer:
Marketing Ops, RevOps, and SalesOps practitioners at growing businesses who manage customer data across multiple platforms and don't have a dedicated data engineering team. These are the people manually deduplicating CRM records, fixing formatting inconsistencies before a campaign send, and dealing with bounced emails from bad data. They know the problem is costing them time and revenue -- they just haven't had a tool built specifically for them.
CleanSmart's answer:
Most data cleaning tools make you run separate processes for separate problems -- one tool for duplicates, another for formatting, something else for missing values. CleanSmart handles all four in a single automated pass: semantic duplicate detection, format standardization, missing value prediction, and anomaly flagging. What makes that technically different is the confidence-based review layer -- high-confidence changes happen automatically, low-confidence ones get routed to you for approval. Nothing changes in your data without a full audit trail, and every decision is reversible.
CleanSmart's answer:
CleanSmart is built for the people who actually live with messy data -- Marketing Ops, RevOps, and SalesOps practitioners -- not data engineers. There's no code to write, no complex configuration, and no need to export and re-import files manually. It connects directly to HubSpot, Salesforce, Mailchimp, Klaviyo, and Shopify via OAuth and cleans your data where it already lives. The semantic duplicate detection catches matches that traditional string matching misses -- "Jon Smith" and "John Smith" at the same company get flagged, not treated as two separate contacts. And the human-in-the-loop review workflow means you stay in control of every change the AI makes.
CleanSmart's answer:
CleanSmart was built by William Flaiz, a digital transformation executive with 20+ years of enterprise software and MarTech consulting experience. After repeatedly watching businesses lose revenue to data they couldn't trust -- duplicate leads, inconsistent formats, records scattered across disconnected systems -- and spending countless hours cleaning that data manually before it could be useful, he built the tool he kept wishing existed. The product went from concept to working beta in four months, built with AI-assisted development and informed by direct feedback from RevOps and MarOps practitioners who shaped its core features.
CleanSmart's answer:
CleanSmart is built on a React and TypeScript frontend with a FastAPI Python backend. The AI capabilities use sentence-transformers for semantic similarity matching and scikit-learn for anomaly detection and missing value prediction. Data is stored in PostgreSQL in production. Platform integrations connect via OAuth 2.0. The infrastructure runs on DigitalOcean.
Based on our record, GitHub seems to be more popular. It has been mentiond 2469 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
The real fragility is in trying to constrain arguments. The docs are explicit that a pattern like Bash(curl http://github.com/ *) fails to do what it looks like it does. It won't match curl -X GET http://github.com/... (option before the URL), curl https://github.com/... (different protocol), curl -L http://bit.ly/xyz (redirects to GitHub), URL=http://github.com && curl $URL (variable), or curl http://github.com... - Source: dev.to / about 5 hours ago
Fallback chains โ og:title โ twitter:title โ- SSRF protection โ if you fetch user-supplied URLs, you MUST block
localhost, RFC-1918 ranges, and internal hostnames, or your preview endpoint is a proxy into your own infrastructure- Caching โ you do not want to re-fetch a URL on every render
- Rate limiting โ a public...
- Source: dev.to / 2 days ago
$ git pull Remote: Repository not found. Fatal: repository 'https://github.com//.git/' not found. - Source: dev.to / 4 days ago
// ==UserScript== // @name GitHub -> Obsidian Task // @namespace obsidian // @version 1.0 // @match https://github.com/*/*/issues/* // @match https://github.com/*/*/pull/* // @grant GM_setClipboard // ==/UserScript== (function () { 'use strict'; function getTitle() { return document.querySelector("bdi")?.textContent.trim(); } function copyTask() { ... - Source: dev.to / 6 days ago
Import requests From bs4 import BeautifulSoup From datetime import datetime Def fetch_github_trending(): url = "https://github.com/trending?since=daily" response = requests.get(url) soup = BeautifulSoup(response.text, 'html.parser') repos = [] for article in soup.select('article.Box-row'): repo_link = article.select_one('h2 a')['href'] stars_today =... - Source: dev.to / 7 days ago
GitLab - Create, review and deploy code together with GitLab open source git repo management software | GitLab
CSV Cleaner - Clean messy CSV files in seconds.
BitBucket - Bitbucket is a free code hosting site for Mercurial and Git. Manage your development with a hosted wiki, issue tracker and source code.
Bulk Phone Normalizer - Clean messy CSV phone columns before CRM, dialer, or API import. Convert safe rows to E.164, preserve the rest of your data, and split risky numbers into a needs-review file in your browser.
VS Code - Build and debug modern web and cloud applications, by Microsoft
Clean Spreadsheets - Automatically clean customer data with a few clicks
