Fi

Filling PDF forms with AI using client-side tool calling

Hacker News

Filling PDF forms with AI using client-side tool calling

Hey HN! I built SimplePDF Copilot: an AI assistant that can interact with the PDF editor. It fills fields, answers questions, focuses on a specific field, adds fields, deletes pages, and so on. It's built on top of SimplePDF that I started 7 years ago, pioneering privacy-respecting client-side pdf editing, now used monthly by 200k+ people. As for the privacy model: the PDF itself never leaves the browser. Parsing, rendering, and field detection all run client-side. The text the model needs (and your messages) goes to whatever LLM you point at. By default that's our demo proxy (DeepSeek V4 Flash, rate-capped), but you can BYOK and point it at any cloud provider, or go fully local (I've been testing with LM Studio). Unlike the existing "Chat with PDF" tools that only retrieve the text/OCR layer, Copilot can act on the PDF: filling fields, adding fields (detected client-side using CommonForms by Joe Barrow [1], jbarrow on HN with some post-processing heuristics I added on top), focusing on fields, deleting pages, and so on. I built this because SimplePDF is mostly used by healthcare customers where document privacy is paramount, and I wanted an AI experience that didn't require shipping PII to a third party. Stack is pretty standard: - Tanstack Start - AI SDK from Vercel - Tailwind (I personally prefer CSS modules, I'm old-school but the goal since I open source it, I figured that Tailwind would be a better fit) The more interesting part is the client-side tool calling: events are passed back and forth via iframe postMessage. If you're not familiar with "tool calling" and "client-side tool calling", a quick primer: Tool calling is what LLMs use to take actions. When Claude runs grep or ls, or hits an MCP server, those are tool calls. Client-side tool calling means the intent to call a tool comes from the LLM, but the execution happens in the browser. That matters for: speed, you can't go faster than client-to-client operations and also gives you the ability to limit the data you expose to the LLM. For the demo I do feed the content of the document to the LLM, but that connection could be severed as simply as removing the tool that exposes the content data. The demo is fully open source, available on Github [2] and the demo is the same as the link of this post [3] What's not open source is SimplePDF itself (loaded as the iframe). I could talk on and on about this, let me know if you have any questions, anything goes! [1] https://github.com/jbarrow/commonforms [2] https://github.com/SimplePDF/simplepdf-embed/tree/main/copil... [3] https://copilot.simplepdf.com/?share=a7d00ad073c75a75d493228...

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, mcp · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting
89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · Missing: https docs, excited, just released
64%64% 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 · Strong signals: calls · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, month, monthly · Missing: mobile apps, ios, entrepreneurs
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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