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Django Control Room – All Your Tools Inside the Django Admin

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Django Control Room – All Your Tools Inside the Django Admin

Over the past year I’ve been building a set of operational panels for Django: - Redis inspection - cache visibility - Celery task introspection - URL discovery and testing All of these tools have been built inside the Django admin. Instead of jumping between tools like Flower, redis-cli, Swagger, or external services, I wanted something that sits where I’m already working. I’ve grouped these under a single umbrella: Django Control Room. The idea is pretty simple: the Django admin already gives you authentication, permissions, and a familiar interface. It can also act as an operational layer for your app. Each panel is just a small Django app with a simple interface, so it’s easy to build your own and plug it in. I’m working on more panels (signals, errors, etc.) and also thinking about how far this pattern can go. Curious how others think about this. Does it make sense to consolidate this kind of tooling inside the admin, or do you prefer keeping it separate?

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Product HuntOn track for Day 1 leaderboard · Strong signals: single · Missing: mac, agents, macos
84%84% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting
69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
57%57% 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 · 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: para · Missing: mobile apps, ios, personal
40%40% 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
19%19% 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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