We

Web2Docx - easy conversion to docx documents

Hacker News

Web2Docx - easy conversion to docx documents

We, at Veneficus[0], build Web2Docx (http://www.web2docx.com) as an internal side project because we want to generate good looking, Word compliant reports for our clients programmatically. Since these reports contain a wide array of markup, charts and our company branding one of our primary concerns was the compatibility with Word. Our insights are of no value when they can't be seen. Therefore, we have made significant effort to match our output with Word 2010 as close as possible. We are succeeding at doing this. We are now ready to share this functionality and I'd like to ask the HN community what you think about our project. Do you have any suggestions for improvements? For example, we currently transform from HTML, shall we add conversion from markdown? [0]: Veneficus (http://www.veneficus.nl) is a research and consulting company that – from an econometrics background – helps to analyze all internal and external data. We take a fresh look at your data, spreadsheets and reports, and bring an insightful analysis. This will allow you to better anticipate market developments, understand the financial implications of your strategy and support all your decisions with facts.

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best fitHighest predicted score across all platforms for this description.
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63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
46%46% 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 · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
14%14% 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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