Do

Dodo – Deploy application by drawing on Canvas

Hacker News

Dodo – Deploy application by drawing on Canvas

Hi everyone, I want to share an update on my project dodo. Month ago I made an initial post which demonstrated capabilities of the dodo PaaS. After getting some feedback I realised UX/DX could be improved significantly. Canvas lets you define your application infrastructure by "drawing" visually. By drawing I mean dropping nodes on canvas and connecting them to enable discovery and communication between services. Backend is the same, and you can think of the Canvas as a visual dodo json configuration file editor. One can imagine how such UI can evolve. I plan on adding monitoring and log viewing capabilities to it in near future. Another update I have is that dodo has one actual client now (apart from me) who has been using hosted version of dodo daily for the past month. Please let me know what you think, and get in touch with me if you want to try it out. I'd be happy to send you an invite. Thanks. * Initial announcement: https://news.ycombinator.com/item?id=41927516

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, visual, using · Missing: mac, agents, macos
44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
32%32% 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
16%16% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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