VisuaLeaf – A Modern MongoDB Workspace

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Visualeaf is a MongoDB GUI I’ve been building over the past year. Stack is Electron + Angular + Spring Boot. There’s a live playground on the site if you want to try it without installing or putting in your connection (I provided one). The goal was to combine a visual workflow with the depth needed for real development work. Most existing MongoDB tools tend to optimize for either beginners or power users, but not both in the same interface. Core features: Query builder that supports full MongoDB query expressiveness + being able to drag and drop elements from the collection to the query builder Form based aggregation builder with synchronized JSON view Schema visualization and generation tools GridFS viewer with MP4 streaming support (streaming mp4 was pretty tricky ) IDE style split panels and multiple workspaces Import/export transformations (mask/edit fields during export ) Tree view ( finding a way to expand recursively thousands of nodes was a challenge) Table view (I had to build my own take on AG Grid focusing on optimizing horizontal and virtual scrolling to get it to scroll smoothly on thousands of rows and columns) A lot of the work ended up being performance engineering. It currently loads ~500MB of data into the UI in about 5 seconds on an M1 MacBook. And can even easily display over 20k documents of an average size (12kb) . Here’s a walkthrough of all its features:: https://www.youtube.com/watch?v=WNzvDlbpGTk Happy to answer questions! Thank you so much!

Visit product View on Hacker NewsApr 29, 2026

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

85%85% 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: mac, user, visual · Missing: agents, macos, agent

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% 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

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.

Correct prediction on native model

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