Alternatives
Products that do what Lore Engine – Turn 10-hour lectures into 2 hours of comprehensive notes does
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Built a ~9M param LLM from scratch to understand how they actually work. Vanilla transformer, 60K synthetic conversations, ~130 lines of PyTorch. Trains in 5 min on a free Colab T4. The fish thinks the meaning of life is food. Fork it and swap the personality for your own character.
Apr 2026 · github.com
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Hi HN, University lectures are long, dense, and fast-paced. Like many students, I used to record them thinking I’d revisit them later — but that never really worked. Even transcripts felt like raw logs. So I built [Notorium](https://www.notorium.app) — an AI assistant that records live lectures and automatically turns them into structured notes, flowcharts, and mind maps— within minutes after class. What it does: - Record lectures inside the app (in-person, live) - Transcribe using Whisper - Send the full transcript to LLM - Use a custom system prompt to: - Summarize the lecture -…
2025 · notorium.app
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Bunge Bits summarizes long parliamentary sessions from the Kenyan National Assembly and Senate. Built with Rust, Whisper v3, and GPT-4o. Sessions are typically 3–7 hours long, mixing English and Swahili. This tool transcribes, chunks, and summarizes them to make political content more accessible and searchable for the public. https://bungebits.ke/summaries
2025 · github.com
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Here is a tool I built initially for myself to help with my German and Greek language studies. It started as a hack for creating Anki cards from native language audio. It extracts the words, finds their base forms (lemmas) and groups the examples by the lemma. At some point I realised that I have a transcription with word level timestamps that opens a lot of other opportunities. So I added a mode to click the first and last word in the transcript and it starts looping with the right gap and repeat count. Another feature I use a lot is selecting an audio fragment, sending a predefined prompt…
Jun 2026 · lingochunk.com
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Hey HN. I’m Ivan, hacker from Ukraine. For about a year, I was working on Listenly — an app to listen to text content with OpenAI's natural-sounding text-to-speech model. At some moment, I realized that it would be cool to take all the public domain e-books and create audio versions for them. So I did it... kind-of. It would cost an immense amount of money to generate all the audio right away (OpenAI TTS costs approximately $0.84/hour of audio; 11labs, for comparison, is 10 times more expensive). So, I took a more gradual approach. I took all the metadata from the Project Gutenberg…
2024 · listenly.io
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i take a lot of notes but rarely find time to organize them so the value of most notes in my personal context quietly disappears. Notecast is a local note engine i've been building to help me with that. it runs a three stage LLM pipeline (Classify -> Organize -> Consolidate) that automatically builds and maintain a knowledge graph from the notes. the theme hierarchy emerges by subdivision as notes accumulate. Any change generates a proposal that can be edited and commited by the user. It is early stage and a lot of architectural and domain logic decisions might change but the core is working…
May 2026 · github.com
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AI recaps the last 24 hours of meetings for your commute in
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2013 · document.ly
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