A

A full AR helicopter action game built as a Snap lens

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A full AR helicopter action game built as a Snap lens

I built a complete game using Snap's AR platform in about 6 weeks. I'd been intrigued by Snap's AR ambitions and wanted to kick the tires on their platform. The YouTube video description contains a link to the Lens: https://lens.snapchat.com/d04efc6953fd498c87e25c92fef727e7?s... How to play: - Joypad y axis moves the helicopter along the ray pointing from the camera. You can therefore tilt the phone up to fly "up". - Altitude is also controlled by aiming the phone. The helicopter tries to match the altitude of a point that is fixed in front of the camera. So you can move up by aiming up or by actually lifting the phone higher. - Joypad x axis strafes. - If you lose track of the helicopter, a blue arrow will appear after about 5 seconds. - Blow up enemies and try to stay alive :) How it works under the hood: - Loads of JavaScript. Lenses are surprisingly programmer-friendly. As a non-artist, this is great. Almost everything can be driven completely by code, although most creators opt to use pre-made components with minimal scripting. - The game uses a custom physics engine with OBB-based collision detections that I wrote from scratch. Snap now has integrated physics that seem to be based on PhysX (i.e., the same as Unity's) but this was released well into my development process after I had already rolled my own. - I opted not to use environmental mesh reconstruction because it is performance intensive and gives dubious results on lower-end phones. When I started development, it was only enabled for Lidar-based phones but is since available everywhere. - I instead detect horizontal planes using an API that Snap hasn't really documented. I found a snippet of code in their API docs and opened up the Lens Studio .exe to find the names of the other plane events I needed. - Planes are used to create collision boxes that extend to the floor so the helicopter can bump into furniture. - AI-controlled enemy helicopters plot routes using a heightmap generated from the plane data to avoid colliding with obstacles when possible. Takeaways: - This has arguably become my favorite AR prototyping platform. Much faster idea-to-PoC iteration time than anything else out there. - The platform is surprisingly full-featured. - The Lens Studio Editor is remarkably polished and, despite some bugs here and there, is very stable. - The deployment story is second to none. Deploying to the phone involves a single click and is done over the Internet (no need for any tethering or wireless pairing). - Debugging on-device is still challenging (all you can do is log a few lines) but I was assured that an actual debugger is coming soon. - Economic viability: unfortunately, it is not really possible to directly monetize lenses. Most creators contract with brands to create branded lenses. Future plans: - I'll definitely be keeping an eye on Snap's ecosystem. - No plans for any more full-fledged lenses but I do have some ideas for prototypes I'd like to try in anticipation of commercially viable mixed reality HMDs hitting the market soon (e.g., Meta's Project Cambria). - I may update the game with a tutorial mode and a co-located multiplayer mode (co-op play or PvP) provided I get the necessary support from the Snap dev team. Hope you enjoy and are inspired to dive into AR if you haven't already :D

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
97%97% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: single, using, code · Missing: mac, agents, macos
83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, friendly, soon · Missing: plus, intuitive, reviews
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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