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Crossview – visualize Crossplane resources and compositions

Hacker News

Crossview – visualize Crossplane resources and compositions

Hi HN, Crossplane is powerful, but once you start working with multiple compositions, claims, and managed resources, it becomes hard to understand how everything is connected. We built Crossview to solve this problem for ourselves. It’s an open-source UI that visualizes Crossplane resources and compositions as graphs, making it easier to explore relationships, debug issues, and reason about complex setups. Some of the things we focused on: Visualizing relationships between compositions, claims, and managed resources Real-time views with search and filtering Multi-context support to switch between clusters easily The project is still early, and we’re actively improving it. We’d really appreciate feedback from people using Crossplane, Kubernetes, or building internal platforms: Is this useful in real-world setups? What features would you expect from a Crossplane visualization tool? What’s missing or unclear? Repo: https://github.com/corpobit/crossview Thanks for taking a look.

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1points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
81%81% 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: context, visual, using · Missing: mac, agents, macos
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
55%55% 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 · 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: platform · Missing: plus, intuitive, reviews
40%40% 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
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.

Incorrect prediction on native model

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