My Reddit AMA on the Law of Web Scraping

📝 Discussion Summary (Click to expand)

Ubiquity mistaken for legality – Many assume that because a behavior is common, it must be lawful.

djoldman: “This breaks my students’ brains. If it’s so ubiquitous, it must be clearly legal…right?”
Power asymmetry in gray‑area laws – Vague regulations tend to protect large corporations while exposing ordinary people to risk.

wand3r: “As with most things it depends on who you are. If you are Spotify, Google, OpenAI or Anthropic, its fine… gray areas and unsettled laws are so malicious. They protect the rich and powerful and can be used to absolutely destroy regular people.”
Analogies to controversial cases (e.g., Aaron Swarts) and data‑scraping concerns – The discussion links current LLM training practices to past legal battles over scraping, warning of similar repercussions.

wand3r: “It is adjacent to scraping, but Aaron Schwartz is a good example. He faced decades in jail for what is effectively best practice for an LLM data pipeline.”

🚀 Project Ideas

Generating project ideas…

Scans codebases and configuration for legal risks in data scraping, licensing, and LLM training data usage.
Core value proposition: gives developers an early‑warning system to avoid costly lawsuits and compliance violations.

Key	Value
Target Audience	Developers, data engineers, ML engineers building data pipelines or LLM training workflows
Core Feature	Static analysis that detects use of web‑scraping libraries, unverified API calls, ambiguous data licenses, and potential GDPR/ToS violations, outputting a risk score and remediation tips
Tech Stack	Python (AST/tree‑sitter), Rust for high‑performance scanning, SPDX license database, legal knowledge graph stored in Neo4j
Difficulty	Medium
Monetization	Revenue-ready: SaaS subscription, tiered by monthly scan volume (e.g., $0 for <1k scans, $20/mo for up to 10k, custom enterprise)

HN commenters worry about “gray areas and unsettled laws” that “protect the rich and powerful” (wand3r) – this tool makes those gray areas visible to everyday builders.
Enables discussion on what constitutes acceptable data use and could become a reference point for open‑source projects seeking clarity.

A searchable, community‑driven repository where developers submit real‑world cases, anecdotes, and legal opinions about gray‑area tech practices.
Core value proposition: crowdsourced risk assessments and plain‑language summaries help individuals gauge whether a practice is likely safe, risky, or legally uncertain.

Key	Value
Target Audience	Indie hackers, startups, software lawyers, tech policy enthusiasts
Core Feature	Users tag submissions (e.g., scraping, speed‑limit analogy, LLM data pipeline) with safety votes, link to statutes or case law, and receive a aggregated risk level; includes API for integration
Tech Stack	Node.js/Express backend, React frontend, Postgres for storage, Elasticsearch for full‑text search, optional LLM‑based summarization
Difficulty	Low-Medium
Monetization	Hobby (open‑source, community‑run) – could add premium API tiers later if needed

Reflects wand3r’s observation that “gray areas … are so malicious” and that regular people fear being crushed – the compass lets the community share experiences and reduce that fear.
Sparks practical utility: a go‑to reference for developers unsure if a new scraping tool or LLM data source is permissible, fostering informed debate on HN and elsewhere.

A managed platform that ingests training data, checks licenses, detects personal data, and verifies terms‑of‑service compliance to produce a certifiable compliance report.
Core value proposition: gives AI teams confidence that their data pipeline meets legal standards, reducing risk of litigation and enabling faster product launches.

Key	Value
Target Audience	AI startups, research labs, enterprises building or fine‑tuning large language models
Core Feature	Automated data lineage, license detection (via Licensee/SPDX), PII/NER scanning, ToS violation detection via scraping of public policies, generation of PDF/HTML compliance audit report
Tech Stack	Python (Prefect or Apache Beam for pipelines), spaCy/stanza for NER, PostgreSQL for metadata, S3‑compatible storage, React dashboard, optional Rust for high‑speed scanning
Difficulty	High
Monetization	Revenue-ready: pay‑per‑project (e.g., $500 per data‑set audit) or subscription model ($199/mo for unlimited audits up to TB scale)

Addresses the fear expressed by djoldman that “if it’s so ubiquitous, it must be clearly legal…right?” by providing concrete evidence of legality (or lack thereof) for data used in LLM pipelines.
Encourages discussion on what constitutes permissible training data and could become a trusted benchmark that HN users cite when debating AI ethics and lawsuits.