AI Code Invest

About

k

kongastral

Software engineer and long-term investor. I write about applied AI, machine learning systems, data engineering, and evidence-based investing.

About AI Code Invest

AI Code Invest is a personal blog covering the intersection of three topics I work with daily: artificial intelligence and machine learning, software and data engineering, and long-term investing in U.S. markets. Every post is written to be the article I wish existed when I first researched the topic—practical, hands-on, and grounded in real implementation.

Who writes here

Hi, I’m kongastral. I’m a software engineer with a background in machine learning systems, data pipelines, and quantitative investing. I started this blog because I kept writing the same explanations and notes for myself across projects—on Gaussian Processes, on Kafka pipelines, on options Greeks, on portfolio construction, and figured someone else might find them useful too.

Areas I write about

  • Applied AI/ML: anomaly detection (Deep SVDD, OCSVM), domain adaptation (DANN), self-supervised and semi-supervised learning, Gaussian Processes, time-series forecasting
  • Software & data engineering: Apache Kafka, Apache Airflow, Debezium CDC, dbt, Docker, Python, FastAPI, time-series databases
  • Investing: U.S. stocks, ETFs, options, dividend investing, dollar-cost averaging, tax-efficient strategies, portfolio construction, behavioral finance

Editorial process and AI disclosure

I want to be transparent about how content is produced here, because trust is the most important thing a writer has.

How I write: Every article is researched, structured, edited, and fact-checked by me. I use AI tools (primarily Claude) to help with drafting, summarizing technical papers, generating code examples, and producing diagrams. The final edit, the technical accuracy, the opinions, and the publishing decisions are all mine. I treat AI as a writing partner—like a research assistant—not as a replacement for editorial judgment.

What this means in practice

  • Code examples are tested. If a Python snippet appears in a post, it has been run.
  • Math and formulas are verified. Equations are cross-checked against original papers and textbooks.
  • Sources are real. External links point to original papers, official documentation, or recognized references, no hallucinated citations.
  • Figures are original. SVG diagrams are produced specifically for each post to illustrate the concept being explained.
  • Updates happen. When I find an error or when content becomes outdated, I update the post and note the modification date.

Investment disclaimer

Important: Articles in the Investment category are for informational and educational purposes only. They are not investment advice, financial advice, trading advice, or any other sort of advice. I am not a licensed financial advisor. Investing involves risk, including the risk of losing your entire principal. Always do your own research and consult a qualified professional before making financial decisions.

How to reach me

If you find an error, want to discuss a post, or have a topic suggestion, the best way is to leave a comment on the relevant post or reach out via the email address below.

What this site is not

  • It is not a paid newsletter.
  • It is not a financial advisory service.
  • It is not a trading signal service.
  • It is not affiliated with any company, broker, or fund mentioned in posts.

The site does display Google AdSense ads, which is how it covers hosting costs. I do not write posts to promote specific products or services in exchange for compensation.

Privacy

For information on how visitor data is handled, please see the Privacy Policy.

Last updated: May 2026.