What is nirvacana.com (Recent Thoughts – Thinking in Systems)?

Updated

nirvacana.com is an English-language personal blog titled Recent Thoughts – Thinking in Systems. It publishes long-form essays and framework-style articles on artificial intelligence, data science careers, and systems thinking. It is best suited to readers who want conceptual explanations and career-oriented guides rather than news, product reviews, or technical documentation.

What the site covers

The blog's visible content clusters around three themes:

Theme Example article What it offers
AI agentic systems "The AI Agentic Systems Ladder" (January 6, 2026) A ladder-style framework for thinking about levels of AI agent capability, introduced through the analogy of Snakes & Ladders and its origin, Moksha Patam
AI fundamentals "Demystifying Artificial Intelligence. What is Artificial Intelligence & explaining it from different dimensions." (December 27, 2017) An explainer that approaches AI from multiple conceptual dimensions rather than a single definition
Data science careers "Becoming a Data Scientist – Curriculum via Metromap" (July 8, 2013) A curriculum guide structured as a "metromap," i.e., a route-map metaphor for a learning path

What kind of writing to expect

The articles are essays, not tutorials or reference pages. Two patterns stand out:

  • Frameworks and metaphors as organizing devices. The AI agentic systems piece builds on a board-game analogy; the data science piece builds on a transit-map analogy. The value is in the structure the author imposes on a broad topic, not in step-by-step instructions.
  • Long time span, uneven currency. The three visible posts are dated 2013, 2017, and 2026. The data science curriculum reflects the state of the field in 2013, and the AI explainer reflects 2017. Only the agentic systems article is recent. Treat older posts as conceptual background, not current tooling or hiring advice.

The author also notes in the 2017 post that the earlier data science curriculum article "received a rousing feedback" and that readers have used it as a guide over the years — a signal that the career-path content has had practical use for some readers, though that is the author's own account.

Who it is for

  • Readers who want a mental model of AI agent capability levels rather than a product comparison.
  • People exploring a data science career who want a structured, map-like overview of what to learn — with the caveat that the map is from 2013.
  • Anyone interested in systems thinking applied to technology topics, since that is the stated framing of the blog.

It is not a good fit if you need current pricing, tool comparisons, API documentation, release notes, or up-to-date job-market data. The site shows no pricing signals, no payment links, and no indication of paid content or login requirements — but absence of those signals is not a statement that everything is free or unrestricted; it simply is not addressed in the available material.

How to read it efficiently

  1. Start with the most recent post, "The AI Agentic Systems Ladder," if your interest is AI agents — it is the only clearly current piece.
  2. Read "Demystifying Artificial Intelligence" for conceptual framing, keeping its 2017 date in mind.
  3. Use "Becoming a Data Scientist – Curriculum via Metromap" as a historical learning-path outline, then cross-check every recommended skill against current job postings before relying on it.
  4. Check dates before acting on anything: on a blog with posts spanning more than a decade, the publication date is the single most important filter.