Inspect and extract files from MSI installers directly in your browser

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Hey everyone! I'm excited to share a small web app I built that allows you to view and extract the contents of Windows MSI installers directly in your browser. It's essentially a web-based "lessmsi" powered by Pyodide. You can try it out at: https://pymsi.readthedocs.io/en/latest/msi_viewer.html My motivation for building this was from part of my day job -- I often get Windows MSI installers and need to extract files while preserving the relative directory structure and filenames, as they would appear after a full installation. The existing tools I found were good but limited in which platforms they support: lessmsi works great on Windows, while msitools works for Linux/macOS. Neither is a truly cross-platform solution that works on any major OS. So we developed pymsi (a pure Python library, available on GitHub at https://github.com/nightlark/pymsi ) to handle reading and extracting MSI files from Python. Then I realized that since pymsi has no native dependencies, it could potentially run in a web browser using Pyodide. After a bit of "vibe coding" and fixing some "hallucinated" functions/classes that don't exist in pymsi, the result was this client-side web app. If you need an MSI file to experiment with, older versions of PowerToys included the installer in .msi form, such as this one: https://github.com/microsoft/PowerToys/releases/download/v0.... Note that the underlying pymsi library hasn't been extensively tested against a bunch of MSI installers yet, so there might still be lingering bugs. If you come across any issues, please don't hesitate to report them in on the GitHub repository ( https://github.com/nightlark/pymsi/issues ). I'd love to hear your feedback and answer any questions!

Visit product View on Hacker NewsJun 20, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, using · Missing: agents, agent, cursor

79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, existing · Missing: https docs, just released, lua

70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

12%12% 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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