My open-source tool for visualizing Crossplane resources

Hacker News

Hi HN, We just released Crossview 3.1.0, an open-source tool to visualize Crossplane compositions, managed resources, and claims as interactive graphs. Since the initial launch, we’ve focused on making Crossview more powerful and reliable for real-world Crossplane users. In this release, notable improvements include: Enhanced multi-cluster context support for seamless switching Faster rendering and smoother UI interactions Advanced search and filtering capabilities for large-scale setups Bug fixes and improved stability Crossplane setups can get complex quickly, and Crossview aims to help teams understand and debug their infrastructure more effectively. We’d love to get feedback from the community: How do you handle Crossplane visualization and debugging today? What features would make Crossview even more useful for you? Are there any challenges you face with Crossplane UIs that we haven’t addressed yet?

Visit product View on Hacker NewsDec 24, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, context, visual · Missing: mac, agents, macos

93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

44%44% 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

11%11% 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.

Incorrect prediction on native model

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