I built an open-source CSV importer that I wish existed

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At my previous startup, CSV import was make-or-break for customer onboarding. We built the first version in three days. Then reality hit: Windows-1252 encoding, European date formats, embedded newlines, phone numbers in five different formats. We rebuilt that importer multiples over the next six months. Our onboarding completion rate dropped 40% at the import step because users couldn't fix errors without starting over. The real problem isn't parsing (PapaParse is excellent). It's everything after: mapping "Customer Email" to your "email" field, validating business rules, and letting users fix errors inline. Flatfile and OneSchema solve this but won't show pricing publicly. Most open source tools only handle pieces of the workflow. ImportCSV handles the complete flow: Upload → Parse → Map → Validate → Transform → Preview → Submit. Everything runs client-side by default. Your data never leaves the browser. This is critical for sensitive customer data - you can audit the code, self-host, and guarantee that PII stays on your infrastructure. The frontend is MIT licensed. Technical approach We use fuzzy matching + sample data analysis for column mapping. If a column contains @ symbols, it's probably email. For validation errors, users can fix them inline in a spreadsheet interface - no need to edit the CSV and start over. Virtual scrolling (@tanstack/react-virtual) handles 100,000+ rows smoothly. The interesting part: when AI is enabled, GPT-4.1 maps columns accurately and enables natural language transforms like "fix all phone numbers" or "split full names into first and last". LLMs are good at understanding messy, semi-structured data. GitHub: https://github.com/importcsv/importcsv Playground: https://docs.importcsv.com/playground Demo (90 sec): https://youtube.com/shorts/Of4D85txm30 What's the worst CSV you've had to import?

Visit product View on Hacker NewsAug 30, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, email · Missing: mac, agents, macos

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, ide · Missing: https docs, excited, just released

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: month, users · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, interface, users · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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