
amir
@amirhouieh
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✦ Building @unbody_io - the first open-source AI-native backend stack.
netherlands
Joined September 2014
RT @peer_rich: ive worked in tech long enough to see the same patterns. closed products stagnate. while open source products evolve and win….
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RT @unbody_io: Weeks 24–25 updates: We were heads down on infra — a sweet problem: too many new users, and Cloud couldn’t keep up. So we’ve….
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RT @github: Check them out 👇.@timothyjbaek @OpenWebUI.@amirhouieh @unbody_io.@guohao_li @CamelAIOrg.@fkadev.@nutlope.@PuyuanPeng.@SecondMe_….
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RT @github: The AI landscape is evolving fast, and staying on top of the latest open-source projects is crucial for every developer. 🚀 Swip….
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That's how generative endpoints of @unbody_io works. We charge per call and per minute, regardless of type of model or even number of tokens.
i want to explore all the ai models with a single account and a single subscription, but in the providers themselves rather than a new client. pay for a token batch, and use those tokens in any model provider. imagine openai, anthropic, . having the "connect to t3" button.
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RT @ReactAdvanced: 🫶 We were so happy to see you at the #ReactDublinMeetup!. 🎤 Special shoutout to @amirhouieh and @nialljoemaher for their….
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Very excited for this one.
🚨 Last Chance to join us for the #ReactDublinMeetup — Tomorrow!. Come meet our amazing speakers:. 🎙️@amirhouieh - How to Manage State for Rag-Based UI in React.🎙️@nialljoemaher - Building Your Own React Hook Library. What's else in store:.‣ Open mic session 🎤.‣ Networking
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This is exactly how you win against big players, ship features that actually matter and do it fast.
YES we cracked it! 🤩 native multi-player code execution and multi-player editing. soon you'll be able to work real-time in prototyper with your team + a large number of "agents". all working together instantly, with zero latency.
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RT @ThijsVerreck: YES we cracked it! 🤩 native multi-player code execution and multi-player editing. soon you'll be able to work real-time….
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We’ve been cooking something up. Now you can go from zero to a production AI backend in just a few clicks: from multimodal rag to visual matching to chatbots. @unbody_io 's console v1.0 is coming soon .
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This gets us closer. Previously, when I talked about a post-relational-database world, people would always ask: but what about transactional data?.Well—there you go, the proof.
TL;DR: We built a transformer-based payments foundation model. It works. For years, Stripe has been using machine learning models trained on discrete features (BIN, zip, payment method, etc.) to improve our products for users. And these feature-by-feature efforts have worked.
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