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SQLite AI - Open-Source Extensions to Bring AI to SQLite, Everywhere

Hacker News

SQLite AI - Open-Source Extensions to Bring AI to SQLite, Everywhere

We’re excited to share SQLite AI, an open-source project designed to turn SQLite into a powerful AI infrastructure layer for cross-platform, local-first applications. Our vision is simple: to make SQLite the default database for AI at the edge, whether you're building mobile apps, IoT devices, desktop software, or even browser-based tools. To do this, we've created a suite of modular SQLite extensions that can be used independently or combined to unlock powerful capabilities: * SQLite-Vector: Efficient vector search directly in SQLite, with support for float32, float16, bfloat16, int8, and uint8 vectors. * SQLite-AI: Integration with language models, providing AI-assisted queries, summarization, and generation inside the database. * SQLite-JS: Seamlessly define and run JavaScript functions inside SQLite, perfect for local customization and edge scripting. * SQLite-Sync: Lightweight local-first synchronization with cloud coordination, so your data stays in sync without giving up control. Because everything is built on top of SQLite, you can use the same code across multiple languages and frameworks with minimal friction. Everything is cross-platform by design and tailored for both resource-constrained environments and full-scale deployments. The entire project is open source, and we welcome feedback, contributions, and collaboration from the community. Whether you’re building AI-powered apps, tools for edge devices, or just love SQLite, we think you’ll find something exciting here. More info at: https://sqlite.ai We’d love to hear what you think!

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps, models · Missing: mac, agents, macos
93%93% 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: created · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, ide · Missing: https docs, just released, exist
81%81% 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 · Strong signals: platform, efficient · Missing: plus, intuitive, reviews
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: mobile apps, apps · Missing: ios, personal, entrepreneurs
45%45% 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
14%14% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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