I

I remade my website in the Sith Lord Theme and I hope it's fun

Hacker News

I remade my website in the Sith Lord Theme and I hope it's fun

I used the time over Christmas and in between the years to redesign my website. This time I decided to make it in the theme of an evil Sith Lord that commands the Galactic Cookie Empire, because I found my previous cookie consent game a bit boring after a while. Here's the website's welcome page and the cookie consent game: https://cookie.engineer/index.html (the cookie consent game isn't started on any other page of my website, only on the welcome page) I also made a "making of" weblog article series, in case you're interested in the development process and how I implemented it and what kind of troubles I went through already: - Making of the Game: https://cookie.engineer/weblog/articles/making-of-my-website... - Making of the Avatar: https://cookie.engineer/weblog/articles/making-of-my-website... - Debuggers to toy around with: https://cookie.engineer/design/consent/index.html It "should" work on modern browsers. I tested it on Firefox on Linuxes, Chrome/Chromium on Linuxes, and Safari on Macbook. Don't have an iPhone so I can't test that, but my two old Android phones were also working fine with the meta viewport hack (I can't believe this is still the "modern" way to do things after 15 years. Wtf). Best experience is of course with a bigger display. On smaller screen sizes, the game will use a camera to zoom around the game world and follow the player's spaceship. Minimum window width is 1280 pixels for no camera, and I think 800 pixels to be playable (otherwise the avatar gets in the way too much in the boss fights). Oh, there's also a secret boss fight that you can unlock when you toy around with the Dev Tools :) What's left to do on the avatar animation side: - I have to backport CMUdict to JavaScript / ECMAScript. That's what I'm working on right now, as I'm not yet satisfied with the timings of the phonemes. Existing tools and pipelines that do this in python aren't realtime, which leads to my next point. - I want to switch to using the "waveform energy detection" and a zero cross rate detector to time phonemes more correctly. I believe that changes in waveforms and their structures can detect differences in phonemes, but it's a gut feeling and not a scientific fact. Existing phoneme animation papers were kind of shit and broken (see my making of article 2). The phoneme boundary detector is highly experimental though and is gonna need a couple weeks more time until it's finished. That's it for now, hope you gonna enjoy your stay on my website and I hope you gonna have fun playing the Cookie Consent Game :) Oh, also, because it might not be obvious: No LLMs were used in the making of this website. Pretty much everything is hand-coded, and unbundled and unminified on purpose so visitors can learn from the code if they want to. ~Cookie

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
92%92% 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.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
69%69% 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 HuntOn track for Day 1 leaderboard · Strong signals: mac, using, code · Missing: agents, macos, agent
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
44%44% 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
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
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.

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