PD

PDFClear – Browser-based PDF tools with local AI (WASM+Transformers.js)

Hacker News

PDFClear – Browser-based PDF tools with local AI (WASM+Transformers.js)

Hello HN, I’m the founder of PDFClear ( https://www.pdfclear.com ). It’s a suite of PDF tools (merge, split, compress, etc.) that runs entirely in the browser. I built this because I was tired of Googling "merge pdf" and landing on sites that require me to upload sensitive bank statements or contracts to an unknown server. I wanted a tool where the file never leaves the device. The Tech Stack: The app is built with React and Vite, but the heavy lifting is done via WebAssembly and Web Workers to keep the UI thread responsive. - PDF Manipulation: I’m using pdf-lib for standard operations (merge, split, rotate). - Compression & Encryption: For heavier tasks like compressing streams or handling encryption/decryption, I compiled QPDF to WebAssembly (qpdf-wasm). - OCR: Scanned documents are processed client-side using Tesseract.js. Local AI (The New Part): I recently added Semantic Search and Summarization without relying on OpenAI/Anthropic APIs. - It uses Transformers.js to run ONNX models directly in the browser. - Search: Uses different models (including nomic-ai/nomic-embed-text-v1.5 and Xenova/GIST-small-Embedding-v0) for embeddings. It chunks the text, stores vectors in IndexedDB (via idb-keyval), and performs cosine similarity locally. - Summarization: Uses onnx-community/text_summarization-ONNX (quantized) running in a Web Worker. Privacy: Because everything runs client-side, no documents are uploaded to my server. You can verify this by inspecting the Network tab. Once the app loads (and the AI models are cached), it works fully offline. I’d love your feedback on the performance of the local AI models, specifically on older devices.

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Actual performance

2points
1comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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