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| title: Pensieve | |
| emoji: 🎙️ | |
| colorFrom: gray | |
| colorTo: red | |
| sdk: gradio | |
| sdk_version: 6.17.3 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: Speak a thought, get a markdown note, then chat with them | |
| tags: | |
| - gradio | |
| - build-small-hackathon | |
| - track:backyard | |
| - sponsor:modal | |
| - sponsor:cohere | |
| - achievement:offbrand | |
| - best-demo | |
| models: | |
| - Qwen/Qwen3-8B | |
| - Qwen/Qwen3-Embedding-0.6B | |
| - CohereLabs/cohere-transcribe-03-2026 | |
| # Pensieve | |
| Speak a thought and Pensieve turns it into a clean markdown note in the background. Browse your | |
| growing collection of notes and ask questions across everything you have captured. | |
| Capture is asynchronous: stop recording and the transcribe, summarise and index pipeline runs as a | |
| background job, so you can record the next thought right away. | |
| I built this for my dad, who is always going on walks and recording voice notes of his thoughts. | |
| Pensieve allows him to build a catalogue of his thoughts and recall them easier. | |
| ## Demo | |
| - Video: https://huggingface.co/spaces/build-small-hackathon/pensieve/resolve/main/pensieve-demo.mp4 | |
| - Social post: https://www.linkedin.com/feed/update/urn:li:activity:7472430389475610624/ | |
| ## How it works | |
| The front end is a dark, minimalist Gradio app with a bottom tab bar that installs to the home | |
| screen on an iphone as a progessive web app (PWA). All AI inference runs on Modal, and every model is under 32B parameters. | |
| - Record: capture audio, then a background job runs transcribe, summarise and index. | |
| - Jobs: a live view of each pipeline job and its stage. | |
| - Knowledge: a Chat and Notes view. Chat answers with RAG over your notes and cites sources. Notes | |
| lets you search and read your captured notes. | |
| ## Models (all < 32B) | |
| | Role | Model | Runs on | | |
| |------------|--------------------------------------|---------------| | |
| | ASR | CohereLabs/cohere-transcribe-03-2026 | Modal, L4 GPU | | |
| | Embeddings | Qwen/Qwen3-Embedding-0.6B | Modal, CPU | | |
| | LLM | Qwen/Qwen3-8B | Modal, L4 GPU | | |
| ## Future work | |
| First order of business would be to move the data into some user owned data storage (like google drive). | |
| right now its on a dataset repo, but its not private as I have access, although each user can't see eachothers data. | |
| I could would then speed up inference by using per-token cost API's instead of having to cold-start GPUs. | |
| I am currently using memory snapshots and that does seem to speed things up a lot. | |
| Improve RAG and prompting, currently using hybrid RAG with reciprocal rank fusion (RRF), but a re-ranker couldn't hurt. | |