Magma – Multiplayer AI for Artists

Hacker News

Hello HN community! I’m one of the founders of Magma, a multiplayer art platform. You might recall our earlier post ( https://news.ycombinator.com/item?id=30869131 ), and today we’re sharing a significant update with our artist-focused, multiplayer AI assistant, a first in the realm of collaborative creative tools. Hope you’ll like it! See how it works in this YouTube video: https://www.youtube.com/watch?v=ZESJfjwxLjk . For in-depth understanding, here’s our documentation ( https://help.magma.com/en/articles/6711598-beta-ai-assistant ) and our AI manifesto ( https://magma.com/aimanifesto ) which is a guiding document for us. We're inviting you to get hands-on with this new feature. Join any of these canvases (up to 50 live contributors each): https://magm.ai/qnss , https://magm.ai/ei74 , https://magm.ai/38mr , https://magm.ai/z1ti , https://magm.ai/zdub , https://magm.ai/ed93 , https://magm.ai/1l84 , https://magm.ai/xvu5 , https://magm.ai/gd9j , https://magm.ai/pu6e . All of these canvases have extra feature flags enabled but if you’d like to go beyond them, feel free to join our beta community https://magm.ai/magma-beta-artspace-invite Our artist-first approach is rooted in our belief that human creativity should remain the heart of artistry. With our AI handling routine tasks, artists can focus on true creativity. Importantly, our AI preserves artists' copyright as it provides a clear distinction between human-generated and AI-generated content. Beyond just art, Magma is a powerful tool for game dev and animation, offering powerful design & review tools for all stages of the creative process. Our Slack/GDrive-like workspaces (we call them Artspaces) expose API and even shell tools. One can even render any artwork in the terminal. :) Technically speaking, our collaborative drawing engine is powered by Typescript, Node.JS, WebGL, with a hint of WebAssembly for hand-optimized performance that even Chromebooks can handle. The backend also leverages a high performance Typescript Deepkit Framework https://deepkit.io Our AI assistant runs on a worker-based architecture akin to Gitlab CI workers, currently leveraging Stable Diffusion 2.1. Future developments will allow connecting your own AI worker, training custom models within Magma, and plugging in API keys from other AI backends. Feedback, questions, thoughts? Let's discuss! Happy creating with a helping hand of AI! P.S. A shout-out to the HN community, our last post here helped us connect with an amazing technical angel investor who has made significant contributions. Looking forward to more such productive connections!

Visit product View on Hacker NewsMay 31, 2023

Share card

Actual performance

28points

2comments

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: model, slack, new · Missing: mac, agents, macos

83%83% 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

78%78% 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

51%51% 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: platform · Missing: plus, intuitive, reviews

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

17%17% 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

Similar products

JBR42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

By artists, for artists.

Indie Hackers1art

Praxos – Multiplayer AI53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Praxos – Multiplayer AI

Hacker News6

Bunnies and Wolves (multiplayer pacman in HTML5)71%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Bunnies and Wolves (multiplayer pacman in HTML5)

Hacker News4

Multiplayer Pac-Man51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Multiplayer Pac-Man

Hacker News8

Asteroid Anarchy – Multiplayer Asteroids65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Asteroid Anarchy – Multiplayer Asteroids

Hacker News3

Multiplayer Untangle65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Multiplayer Untangle

Hacker News1

Multiplayer Snake in Go53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Multiplayer Snake in Go

Hacker News156

Multiplayer Snakes65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Multiplayer Snakes

Hacker News16

Tic-tac-toe, with a multiplayer twist50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Tic-tac-toe, with a multiplayer twist

Hacker News84

HTML5 Multiplayer Blackjack71%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

HTML5 Multiplayer Blackjack

Hacker News10

Browse all products Analyze your launch