Beyond Generative AI, find the right AI solutions based on your needs
Hi HN, My name is Othmane and I’ve been in the ML field (building and shipping models) for the last ~5years. Today, as many people out there, I come across new AI tools every week. However I was a bit surprised to see little to no mention of established AI vendors that existed before chatGPT and how most use cases are heavily biased toward content generation (text/image) or conversational AI (chatbots). I built a tool that helps you find the right AI solution/provider based on your use case. It uses a curated database of 100+ solutions from established vendors. It covers things…
What it does
In the maker’s words, at launch
Hi HN, My name is Othmane and I’ve been in the ML field (building and shipping models) for the last ~5years. Today, as many people out there, I come across new AI tools every week. However I was a bit surprised to see little to no mention of established AI vendors that existed before chatGPT and how most use cases are heavily biased toward content generation (text/image) or conversational AI (chatbots). I built a tool that helps you find the right AI solution/provider based on your use case. It uses a curated database of 100+ solutions from established vendors. It covers things beyond generative/conversational AI: Image classification, detection, moderation, document understanding, fraud detection, etc. Instead of just listing these solutions, I manually checked each one and mapped the tool (technology) to how it can be used (use cases). Referenced things like pricing, data needed and features of each one. Hope it helps ! Feel free to reach out for any questions or feedback!
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Outlines is a Python library that focuses on text generation with large language models. Brandon and I are not LLM experts and started the project a few months ago because we wanted to understand better how the generation process works. Our original background is probabilistic, relational and symbolic programming. Recently we came up with a fast way to generate text that matches a regex (https://blog.normalcomputing.ai/posts/2023-07-27-regex-guide...). The basic idea is simple: regular expressions have an equivalent Deterministic-Finite Automaton (DFA) representation. We…
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