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AI-Powered Receipt and Invoice Generator (LLM-Agnostic, Prompt-Based)

Hacker News

AI-Powered Receipt and Invoice Generator (LLM-Agnostic, Prompt-Based)

GitHub: https://github.com/WellApp-ai/Well/tree/main/ai-receipt-gene... Example output: https://imgur.com/a/YtFSodj We’ve open-sourced a small tool to generate synthetic receipts and invoices using LLMs — no templates, no HTML, just prompts. Why? We needed large, diverse, semi-realistic documents to evaluate our document extraction pipeline (OCR + AI). Most generators were either too rigid (static templates) or tied to a single provider. So we built our own: Features: LLM-agnostic (OpenAI, local models, etc) Prompt-driven JSON output (not rendered docs) Optional Faker-based fallback if the model isn't used Configurable schemas, locales, and output count Lightweight and hackable — runs with a single config This makes it easy to: Generate test data at scale Create edge cases (missing fields, odd currencies, broken totals) Evaluate or fine-tune document understanding models Example use case: You can ask the model to generate a receipt from a Vietnamese coffee shop, with broken math and a typo in the merchant name — and get structured JSON output that mimics what real OCR might return. Would love feedback from folks working on: LLM eval tooling Synthetic data generation OCR / document AI AI agents that touch financial data Happy to support anyone who wants to extend it or wire up other backends (Claude, Mistral, LM Studio, etc).

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, pipe · Missing: https docs, excited, just released
38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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
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