ScanOS – normalizing visual inputs into persistent LLM memory

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I built a small ingestion layer that turns visual inputs (screenshots, photos, exports) into structured, machine-readable memory for LLM assistants. Instead of treating each image as a one-off prompt, scanOS normalizes recurring visual inputs into the same schema over time — even when the source formats differ. The goal is to accumulate state from visual data, not just extract text. It’s not an OCR tool and doesn’t rely on embeddings, RAG, or fine-tuning. The output is either human-readable text or explicit machine-readable JSON that can be stored, inspected, and reused as part of a file-based memory system. scanOS is part of a larger file-based architecture I’m using daily, but this module stands on its own. Code + docs: https://github.com/johannes42x/scanOS

Visit product View on Hacker NewsDec 23, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, visual, using · Missing: agents, macos, agent

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

19%19% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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