CDXE – Cyber Defence Exercise Environment

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A few years ago I needed a lightweight way to simulate a realistic internet-like environment for some training purposes. Sadly I couldn’t find something suitable for the requirements. Already being halfway down the rabbit hole, when I later had some time left over I got started building the tool I was missing. In May this year, I was finally really to pull the trigger and released the first public version of CDXE into the wild. CDXE is an all-in-one internet-like environment carefully designed to meet the requirements of smaller scale Cyber Defence Exercises(CDX). While the main focus is on CDXs, I’m working on a map feature that can graphically show live traffic flows across the visualized “internet”. This god view is interesting from many perspectives, but specifically for students learning about the inner workings of the internet for the first time. Letting them see how their pings, traceroutes, and other traffic traverses the “internet”. (If you have been to any of my presentations on CDXE, you have seen a demo of this view.) Another use case I’ve been told about more than once, is that CDXE could be used to improve the onboarding experience for new personnel in organizations with complex internet connected networks. Questions and feedback is greatly appreciated!

Visit product View on Hacker NewsJul 26, 2026

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

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

56%56% 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, way · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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