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PDFHippo – Dynamic PDF Generator API Using Excel

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PDFHippo – Dynamic PDF Generator API Using Excel

PDFHippo is an API tool that lets you design a PDF in Excel, add functions and variables to parse dynamic values from the API to Excel and save it. While looking to integrate a PDF component into a no-code application my team and I were building, we went down the rabbit hole of PDF creation in the backend. I couldn't find a tool to edit/parse pdf in a simpler manner. Creating an in-house drag-drop widget for PDF was way too complex to print in PDF format. Tools like pdfkit , pdf-lib, and pdfjam were not compatible with the designs we wanted to create. Other API tools were too expensive considering our pricing models. We didn't want to pass down the price to our clients, given their intention for PDFs for the no-code product is for simpler use-cases like invoices, reports, checklists etc., which they currently solve by Excel or some version of Docs. Our tool retains the styles and text formatting from Excel while replacing the variables from the API. Once the variables are replaced, we print via Unoconv, a CLI interface for the LibreOffice server. We built a script to keep the listeners from LibreOffice active and work across concurrent PDF conversion requests. To keep the pricing low, the API returns only a buffer/blob and doesn't store any data in our infrastructure. Please do give it a go, and we are all ears for feedback!

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Indie HackersFits the IH revenue-focused audience · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, using · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
57%57% 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: way · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
20%20% 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.

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