About the Observatory
A research surface built for calm curiosity.
An open-source digital observatory for tracking public signals across AI, open source, developers, startups, internet infrastructure, security, and digital culture.
What counts as a signal?
A signal is a public, observable event or empirical measurement: a software release, package registry spike, benchmark run, vulnerability disclosure (CVE), legislative filing, standard specification, or ecosystem inflection point.
Observation model: 4 layers of inquiry
To avoid conflating measurement with conjecture, every observation respects four distinct layers:
- Measurement: What the source directly reports (e.g., download figures, telemetry counts, Git commits, RFC text).
- Derived metric: Deterministic calculations performed on the raw measurement (e.g., percentage delta, acceleration, normalized ratios).
- Context: Documented external events occurring in the same temporal window (e.g., upstream releases, regulatory deadlines, security advisories).
- Interpretation: Cautious explanations of what the evidence may indicate, always accompanied by explicitly disclosed uncertainty.
Why Markdown-first?
The source of truth for every article is portable, version-controlled Markdown in content/posts/. This keeps the research diffable, auditable in Git, accessible to AI research agents, and independent of ephemeral databases or closed CMS vendor locks.
Search, Provenance, and AI Discovery
The Observatory publishes canonical URLs, descriptive Open Graph assets, BlogPosting and ProfilePage structured data, XML sitemaps, RSS feeds, and machine-readable agent indexes (llms.txt). These allow humans and autonomous systems to verify provenance and quote claims accurately without speculative SEO tricks.
Contributing & Open Source
Digital Observatory is licensed under the MIT License. Contributions — whether adding verified observations, correcting a source URL, proposing new data collectors, or improving accessibility — are welcomed via GitHub pull requests.