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Maginary.ai gets seedance2 and GPT-images2 support
Hacker NewsMaginary.ai gets seedance2 and GPT-images2 support
presenting maginary -- an image/video generator with a midjourney-like prompt syntax, using 40+ underlying models and acts like an abstractized openrouter for multimedia with an amazing ux, can try it right now with no cc this launch presents a major update: integration of seedance2 and gpt-images2 and a peek into what's next: mcp and x402 (agentic payments) try: maginary.ai read: maginary.ai/blog/adding-seedance-2-and-chatgpt-images-2 watch: https://player.vimeo.com/video/1213091495
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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, agentic · Missing: mac, agents, macos77%77% 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.
TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal66%66% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue23%23% predicted probability of success on Acquire.com, 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 released18%18% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency13%13% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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