OmniScholar
The smart research platform for modern scholars
What it does
OmniScholar is your all-in-one, AI-powered research productivity platform that streamlines your academic workflow from discovery to publication.
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- IMI Made an Open Source Platform for Structuring Any Unstructured Data2024 · github.com · ▲88
Hey HN, I'm Adithya, a 20-year-old dev from India. I have been working with GenAI for the past year, and I've found it really painful to deal with the many different forms of data out there and get the best representation of it for my AI applications. That's why I built OmniParse—an open-source platform designed to handle any unstructured data and transform it into optimized, structured representations. Key Features: - Completely local processing—no external APIs - Supports ~20 file types - Converts documents, multimedia, and web pages to high-quality structured markdown - Table extraction,…
- ANA new way to write and publish research2022 · dystr.com · ▲39
Over the last two years, we’ve seen just how quickly science can move when it’s conducted out in the open, and the focus is on content rather than the publishing process. We’ve built a new way for researchers and scientists to communicate and share their findings with the world - rethought from first principles, with a lot more coming soon. Free to use, and available now for anyone with a university email address. We’re whitelisting organizations that conduct and publish research on a rolling basis. Please forward feedback and thoughts to [email protected] or comment here!
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 18d ago · simedw.com
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AI · 19d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 28d ago · cactuscompute.com
Source: Product Hunt launch ↗
Launched alongside, March 2025
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Mimic Human Research & Save Findings in AI Knowledge Base
AI · 2025 · sider.ai
