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GearSlots, Polygon NFT with tradable attributes

Hacker News

GearSlots, Polygon NFT with tradable attributes

Hello. I made Gear Slots. It's a loot-inspired NFT, with tradable attributes. Deployed on Polygon. I really liked the idea behind loot - to have game data stored on chain. However, not having solid randomness in the generation of the gear, as well as static bags of gear (not being able to trade for different gear) was a downside. So, I made Gear Slots. Gear Slots is built completely on-chain - there is no data stored on IPFS, S3, or any other host. The metadata, attributes, and images for Gear Slots are all stored on-chain. When minted, the contract uses randomness from ChainLink's VRF to randomly generate 9 pieces of equipment. Once you've minted a token, you can set a price on a single piece of gear within the token, a few pieces of gear, or every piece of gear. Other token holders can buy that gear from you at the price you set. You can browse gear for sale from other token holders as well. There is no fee to set prices on gear - only gas. With Gear Slots, you swap out individual pieces of gear with other token holders, without trading away your entire token. Gear Slots brings a unique twist to gaming NFTs with tradable attributes. The mint price is 1 MATIC, plus some gas. When you trade gear with other token holders, the fee is whatever the owner set on that gear, plus some gas. If you're not familiar with Polygon or how to setup your wallet, I'm happy to help. The site is https://gearslots.com, and after minting the tokens show on OpenSea as well. If you want to try the contract and token, but not the site, the contract has been verified on Polygonscan, so you can interact over there as well.

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Actual performance

25points
4comments
Made the leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
73%73% 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.
AppSumoStrong fit for a featured deal · Strong signals: plus, host · Missing: platform, intuitive, reviews
61%61% predicted probability of success on AppSumo, 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
52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: trading, way · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: single, open · Missing: mac, agents, macos
18%18% predicted probability of success on Product Hunt, 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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