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HoundDog.ai

Ultra-Fast Code Scanner for Data Privacy

Details

External ID
46857993
Source
HN
Company
—
Product
HoundDog.ai
Website domain
github.com
Launched
Feb. 2, 2026
Cohort
—
Upvotes
16
Upvotes percentile
0.6556603773584906
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Description

Hi HN,I'm one of the creators of HoundDog.ai (https://github.com/hounddogai/hounddog). We currently handle privacy scanning for Replit's 45M+ creators.We built HoundDog because privacy compliance is usually a choice between manual spreadsheets or reactive runtime scanning. While runtime tools are useful for monitoring, they only catch leaks after the code is live and the data has already moved. They can also miss code paths that aren't actively triggered in production.HoundDog traces sensitive data in code during development and helps catch risky flows (e.g., PII leaking into logs or unapproved third-party SDKs) before the code is shipped.The core scanner is a standalone Rust binary. It doesn't use LLMs so it's local, deterministic, cheap, and fast. It can scan 1M+ lines of code in seconds on a standard laptop, and supports 80+ sensitive data types (PII, PHI, CHD) and hundreds of data sinks (logs, SDKs, APIs, ORMs etc.) out of the box.We use AI internally to expand and scale our rules, identifying new data sources and sinks, but the execution is pure static analysis.The scanner is free to use (no signups) so please try it out and send us feedback. I'll be around to answer any questions!

Enrichment

Theme
lightweight and on-device AI runtimes
Vertical
Security
Function
Dev tools
Audience
B2B
AI stance
AI feature
Project type
Commercial product
Normalized one-liner
code scanner for data privacy
Manually corrected
False

Could you build this?

No Building an ultra-fast static code scanner for data privacy requires deep compiler engineering, static analysis (AST parsing, data flow/taint analysis), and specialized privacy compliance expertise.

What it would actually take: A production-grade code scanner requires custom AST parsers (often leveraging Tree-sitter) paired with an interprocedural taint analysis engine written in Rust or Go to trace sensitive data sources to sinks. It requires sophisticated rule sets mapped to privacy regulations (GDPR, CCPA) and must execute in seconds without false-positive storms to run seamlessly in enterprise CI/CD pipelines. This demands senior compiler engineers, program analysis researchers, and data privacy security specialists.

Discussion

6 comments analyzed.

Competitors mentioned: Traditional SAST tools, LLM-based code analysis, Stripe, Datadog, OpenAI (third-party services mentioned as data sinks)

Concerns raised: LLMs are slow, expensive, and nondeterministic for this use case, Accuracy of PII detection across different naming conventions and languages

Feature requests: Support for additional privacy frameworks beyond GDPR and US Privacy Laws, Integration with privacy notice generation tools

Competitors

Other products that read as similar to this one — 56 launches clear the similarity bar, closest 8 shown.

Attention rank: #31 of 57 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 93 days after the earliest competitor.

Other launches for this product

Same idea, different domain

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