VisionLaya: Jev with Vision capabilities

Hacker News

This model makes calibrated, typed decisions about an image plus optional text. It answers choice, score and noul (yes/no probability) questions in one forward pass, with no text generation. It adds image input to Laya by replacing Laya's ModernBERT encoder with SmolVLM-256M-Instruct. Laya's predict(state, questions) API, proper-scoring-rule training and temperature calibration are unchanged. check out the live demo at https://huggingface.co/spaces/thaitea/laya-vision-demo and the source - https://huggingface.co/thaitea/laya-vision-smolvlm-256m - https://github.com/r33drichards/laya-vision

Visit product View on Hacker NewsSep 19, 2026

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Actual performance

4points

1comments

Did not reach leaderboard

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: answers · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: model, code · Missing: mac, agents, macos

34%34% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

18%18% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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