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VexFS – A Kernel-Native Semantic Filesystem for AI Agents

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VexFS – A Kernel-Native Semantic Filesystem for AI Agents

Hi HN, I started building VexFS yesterday — a kernel-native filesystem that stores vector embeddings alongside files, supports semantic search (brute-force now, HNSW later), and exposes everything through a minimal IOCTL + mmap interface. Think of it as: A semantic memory layer for local AI agents RAG without a vector DB Vector search as an OS primitive It’s early. It barely works. But it boots. Why? Because if memory’s not snapshotable, it’s not memory. And maybe, just maybe, agents deserve a /mnt/mem they can mount natively. When I asked Gemini what it thought of the idea, it said: “An OS-level semantic context layer like this could enable more powerful, context-aware, and efficient AI systems.” Not sure if it's a brilliant idea or a kernel panic waiting to happen. Either way, I’d love your feedback (and flames). → https://github.com/lspecian/vexfs

Visit productView on Hacker NewsMay 26, 2025
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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, context · Missing: mac, macos, cursor
95%95% 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: supports, started, gemini · Missing: reddit linkedin, podcasting, created
61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: filesystem, ide, io · Missing: https docs, excited, just released
57%57% 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: way · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface, efficient · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · 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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