HN SQLite: Complete Hacker News archive searchable offline in your browser.

What it does

HN SQLite is an offline archive of Hacker News spanning 2006 to 2025. The entire dataset runs as a searchable database in the browser using SQLite compiled to WebAssembly. Users download the archive and query it locally without needing an internet connection or server.

Who it is for

People interested in accessing the complete Hacker News archive for research, analysis, or historical browsing. Users who prefer working with data offline or who want to avoid repeated API calls to the live site. Developers building tools or conducting studies on HN discussion patterns.

Pricing

The site does not show prices.

How it stands out

The key differentiator is the local-first approach. Rather than querying a remote server, the entire archive loads into the browser as a SQLite database compiled to WebAssembly. This means no rate limits, no server dependency, and no latency beyond initial download. The dataset covers 20 years of posts and comments, making it comprehensive for historical queries.

The technical approach is relatively simple: data flows from BigQuery through ETL into a format served as static files. This eliminates ongoing hosting costs and creates no vendor lock-in. Users own a copy they control entirely.

What a founder should check

First, verify the actual size and completeness of the archive. The launch mentions "GBs" but doesn't specify the exact download size or whether all content (posts, comments, metadata) is included. Test whether search performance remains usable at scale in a browser environment, since SQLite performance degrades with database size.

Second, examine switching costs and incumbent advantages. The official Hacker News API provides free access to recent items and live data. Tools like Algolia already offer indexed HN search. A founder should test whether the offline model creates enough value over these free alternatives—perhaps for bulk analysis, offline access, or research without rate limits.

Third, assess the moat. Once someone has downloaded the archive, little prevents competitors from offering the same dataset in similar formats (different UI, different database engine, incremental updates). The data itself is public. The barrier is distribution and keeping the archive fresh. Check whether the creator plans incremental updates, which would require ongoing maintenance.

Thinking of building something like this?

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