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Smart Blur

Auto-Blur PII in the Browser, with a OpenAI Local Model

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This is 1 of 119 launches in ai photo editing and enhancement tools — see how it stacks up on momentum and crowding →

46 other launches read as similar to this one →

Details

External ID
49997240
Source
HN
Company
—
Product
Smart Blur
Website domain
smartbuildlabs.com
Launched
Oct. 7, 2026
Cohort
—
Upvotes
7
Upvotes percentile
0.39
Tags
—

Description

Hi folks, I'm Vicky. I build privacy tools that use local AI, on late nights and weekends.Smart Blur is a Chrome extension that blurs sensitive info on any web page before anyone else sees it. There are already a few tools like this, so why another one? Two reasons:Local AI for unstructured PII. Most existing tools use regex, which handles emails, phones and card numbers but can't find names or addresses in plain text. Smart Blur runs OpenAI's Privacy Filter (Apache-2.0) locally on WebGPU. The model is a one-time ~810 MB download from Hugging Face, cached in the browser, and it needs WebGPU. Share Shield. It detects when a page starts sharing your screen (Meet, Teams, Zoom web) and turns blurring on in every tab, then turns it off when you stop.To be upfront: pattern blurring (emails, phones, cards, SSNs, IPs, API keys, custom regex), click/draw-to-blur and hiding tab titles are free. The local AI and Share Shield are a paid Pro upgrade. It's Chrome/Edge only, works on web pages only (not desktop apps), and the AI works best on English.No account and no server. The only network calls are the model download and the optional payment, which you can check in DevTools. There's a practice page with fake customer data at smartbuildlabs.com/apps/smart-blur/try/.I'd love to hear where detection misses or over-blurs.

Enrichment

Niche
ai photo editing and enhancement tools
Vertical
Security
Function
Compliance & governance
Audience
Prosumer
AI stance
AI-native
Project type
Commercial product
Normalized one-liner
in-browser pii blurring using local ai models
Manually corrected
False

Could you build this?

Partial While a Chrome extension UI is straightforward, integrating and running an optimized local NER/PII model via WebGPU entirely client-side requires non-trivial on-device ML engineering.

What it would actually take: The stack involves a Manifest V3 Chrome extension using Transformers.js / ONNX Runtime Web with WebGPU acceleration. The difficult part is compiling and quantizing a performant token-classification or small LLM model that can run inference on arbitrary DOM changes in near-real-time without lagging the user's browser, along with handling dynamic mutation observers across complex web pages.

Discussion

4 comments analyzed.

Concerns raised: Extension and AI model download size, Detection accuracy

Competitors

Other products that read as similar to this one — 46 launches clear the similarity bar, closest 8 shown.

Attention rank: #23 of 47 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 308 days after the earliest competitor.

Other launches for this product