The new Deep Seek version 3 model revolutionizes AI performance, boasting three times the speed of its predecessor and outperforming competitors in coding, math, and reasoning tasks. It is significantly more cost-effective, running locally and allowing users to generate code with live visualizations. Deep Seek Artifacts, a free open-source tool, enables users to create applications effortlessly, showcasing functionalities through various examples like a Twitter clone and a finance tracker. Despite limitations with raw HTML, the model showcases advanced capabilities, making it a powerful tool for developers and creatives alike.
Deep Seek version 3 is three times faster and cheaper than Sonic 3.5.
Deep Seek Artifacts enables live visualizations while generating code effortlessly.
Successfully generates a functional Twitter clone within seconds.
Generates a finance tracking app, showcasing the model's versatility.
Deep Seek version 3 exemplifies the rapid advancements in AI efficiency, particularly in coding. With a processing speed threefold that of its predecessor, it addresses both performance needs for developers and cost constraints, making it a favorable option for startups and individual developers alike. The ability to run locally opens doors for sensitive projects, ensuring data privacy in a world where cloud dependencies often raise security concerns.
The integration of live visualizations in the Deep Seek Artifacts tool fundamentally enhances the user experience. This design-centric approach not only simplifies coding for users but also fosters a more interactive development process, akin to collaborative platforms. Tools that prioritize user engagement will likely increase productivity, making this advancement a significant step towards democratizing AI technology accessibility.
The latest version outperforms competitors and is cost-effective for local deployment.
It allows users to create applications easily via an intuitive interface.
The tool supports React to enable efficient front-end development.
Its products are benchmarked against Deep Seek to demonstrate capabilities in coding tasks.
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It hosts datasets and models that interact with Deep Seek responses.
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