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Choose your own adventure style Presentation

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Choose your own adventure style Presentation

Hello good folks! So... TL;DR: I find presentations boring. I find Choose your own adventure style books not boring. I married the two. Now, you can have presentations where the people you present two have the ability to choose how your presentation proceeds! And you can construct your presentation using plain markdown, start a server, your audience opens the `/voter` link you open the `/presenter` link and start your presentation. Whenever there is a question, they will choose and the presentation proceeds according to the choice. Longer version: In the years I partook on presentations I always liked the ones that are more interactive. Not in a I ask questions and then wait uncomfortably for people to shout out something, no. In a way where I, as a viewer, got something to do! Makes me more interested in the presentation as well, and I'll be learning and remembering things more as well. I also like choose your own adventure type of books. So I wondered, how could I make these two come together? So I wrote this little tool called adventure-voter. Not a very good name, but meh... The point is that you'll have a backend and a frontend to deal with votes and deal with following forks in your presentation. Going back from a fork if the fork ended up in death or a failed route. ( you procastinated, your backend didn't start, you server didn't come up, etc whatever makes sense as an end in your presentation ). And then you can explore a different route. Imagine, you are presenting something about Kubernetes. And one of the questions is, okay you are now bringing up etcd. How do you configure it? Do you... and the vote begins. This makes the presentation a little bit more enjoyable I think. Also, the framework is super easy. You have your presentation in Markdown and the frontend is a lightweight parser with tailwind that does things and makes it look relatively nice. ( I'm not a frontend dev, sorry ). And you can link together steps and stories with `next: slide-1b` or whatever. Granted, you'd have to work a bit more to get a presentation that makes sense, but honestly, I think it will make for a very interesting talk. Something I'm aiming to do on the next KubeCon in Atlanta. I'm going to be using this framework to present something. ( If I get in. :)) ) Lastely, I want the presentation to be enjoyable and not boring. :) And that's my main goal. On KubeCon you sit through presentation after presentation after presentation and hopefully this one will be ( if I get accepted ) something that you enjoy and don't fall asleep on. :) I hope this is useful. Enjoy folks. :)

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Product HuntOn track for Day 1 leaderboard · Strong signals: presentations, using, open · Missing: mac, agents, macos
91%91% 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
82%82% 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
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
54%54% 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
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, active · Missing: mrr, revenue, profit
13%13% 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.

Correct prediction on native model

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