Show HN: Web Bluetooth remote for Canon cameras https://ift.tt/YCFpaPk

Show HN: Web Bluetooth remote for Canon cameras Published my Web Bluetooth demo that uses a reverse-engineered protocol of Canon camera remote (BR-E1) to provide a remote intervalometer that works directly from a Web page. P.S. Yes, as most other Fugu APIs, it works only in desktop and Android Chromium-based browsers. https://ift.tt/ASn1w7K March 6, 2023 at 04:46AM

Show HN: AskYC - Startup advice from GPT Q&A bot based on YCombinator's YouTube https://ift.tt/M57qmNa

Show HN: AskYC - Startup advice from GPT Q&A bot based on YCombinator's YouTube Direct link to the AskYC: https://ift.tt/FZ7TrGA AskYC is a GPT-based Q&A bot that's based on every video on YC's youtube channel. https://ift.tt/RbX9EGy March 6, 2023 at 01:29AM

Show HN: Lander, a lunar lander style web game https://ift.tt/zZkvEgY

Show HN: Lander, a lunar lander style web game I’ve been working on this game for the past few weeks. It’s written in plain JavaScript, mostly with canvas, with no dependencies. The code is here: https://ift.tt/CuHjq6a https://ift.tt/oGgaT0s March 6, 2023 at 12:05AM

Show HN: ExtensionKit – Chrome Extension Development Kit https://ift.tt/pbA4O96

Show HN: ExtensionKit – Chrome Extension Development Kit https://ift.tt/nf0R54j March 6, 2023 at 12:14AM

Show HN: ChatGPT, No Login Needed, Made with Official OpenAI API https://ift.tt/WfEno8N

Show HN: ChatGPT, No Login Needed, Made with Official OpenAI API https://chatgptbay.com/ March 5, 2023 at 12:45PM

Show HN: Llama-dl – high-speed download of LLaMA, Facebook's 65B GPT model https://ift.tt/RklrVjP

Show HN: Llama-dl – high-speed download of LLaMA, Facebook's 65B GPT model https://ift.tt/uGRBzr1 March 5, 2023 at 09:58AM

Show HN: gpt-graph. A simple, GPT-3 text to entity-relation graph generator https://ift.tt/k0iyXaC

Show HN: gpt-graph. A simple, GPT-3 text to entity-relation graph generator Hi HN! This is a simple text to entity-relation graph generator, powered by gpt-3 davinci model. The purpose is to feed it actual written data, to obtain a graph representation of entities and relationships mentioned in the text. Also, being able to identify entity attributes like gender, size, age ... My initial goal, was to make it able to process a large amount of text into a big single graph. The problem being the 4000 token limit the model has, I decided to take the approach of feeding the text in batches, and try to merge the incoming graph with the existing information each time. This is done by comparing the incoming node labels with those already in the graph, adding the new information to the existing nodes. This works somewhat, but sometimes entities get duplicated if they are mentioned slightly differently in the text. The comparation method could use some improvement clearly. A nice feature, is that you get to decide what types you want to extract. So if, for example, you are interested only in people, and companies in the text, you can tell the model to stick to that. You can also leave the types to the model discretion. Also, the application allows for saving / loading graphs to json files. These files can be used with Cytoscape Desktop Application, which is a nice side effect of using cytoscape.js. in the UI. I think tools like this can really be of help when going through dense documentation. To have a visual representation of the concepts, entities or whatever, can be really helpful in education, investigation, legal ... Would love to hear your thoughts on how this could be improved. https://ift.tt/Cf0Panz March 5, 2023 at 05:17AM