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AnyModal – Train Your Own Multimodal LLMs

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AnyModal – Train Your Own Multimodal LLMs

I’ve been working on AnyModal, a framework for integrating different data types (like images and audio) with LLMs. Existing tools felt too limited or task-specific, so I wanted something more flexible. AnyModal makes it easy to combine modalities with minimal setup—whether it’s LaTeX OCR, image captioning, or chest X-ray interpretation. You can plug in models like ViT for image inputs, project them into a token space for your LLM, and handle tasks like visual question answering or audio captioning. It’s still a work in progress, so feedback or contributions would be great. GitHub: https://github.com/ritabratamaiti/AnyModal

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, visual · Missing: mac, agents, macos
86%86% 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 · Strong signals: latex · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released
61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, 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 · Strong signals: audio · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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