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EtherTale – Onchain Interactive Storytelling with AI

Hacker News

EtherTale – Onchain Interactive Storytelling with AI

EtherTale is a decentralized platform that integrates AI-generated narratives with blockchain technology to enable a new form of interactive storytelling. The platform utilizes advanced language models to generate dynamic story snippets based on user votes, while the Minimal Anti-Collusion Infrastructure (MACI) ensures vote integrity and transparency. The backend, built with Python, efficiently handles user inputs and generates AI-driven story content. The frontend, developed using React and Next.js, provides a responsive user interface for an immersive storytelling experience. WebSocket technology enables real-time collaboration between users. EtherTale implements MACI for secure voting, deploys smart contracts on the Linea blockchain, and tests MACI contracts on the Optimism network, pushing the limits of decentralized narratives. We invite the Hacker News community to explore EtherTale and contribute to the future of interactive storytelling, where decentralized technologies and collective creativity merge to create engaging, collaborative narratives.

Visit productView on Hacker NewsMar 25, 2024
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: efficiently · Missing: supports, reddit linkedin, podcasting
89%89% 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: mac, model, user · Missing: agents, macos, agent
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, interface, efficient · Missing: plus, intuitive, reviews
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released
41%41% 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: users · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, active · Missing: mrr, revenue, profit
10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · 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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