This page describes how technical content is produced, fact-checked, and updated on AI Code Invest. The goal is transparency about the editorial process and the resources used to ensure each article is accurate, current, and useful.
Editorial principles
Three principles guide every article published on this site:
- Accuracy first. Every technical claim is verified against primary documentation, official references, or original research papers before publication. Code that does not run does not ship. Numbers that cannot be sourced do not appear.
- Practical over theoretical. Articles are written for engineers and researchers who need to implement, evaluate, or troubleshoot a system. Each post includes either runnable code, a reproducible configuration, or a concrete technical pattern that the reader can apply directly.
- Honest about limitations. When a method has known failure modes, they are stated. When research findings are contested, both sides are presented. When the author does not know something, the article says so.
How articles are produced
The blog is written and edited by kongastral, a software engineer with hands-on experience in machine learning systems, distributed data pipelines, and back-end engineering. Each technical article follows the same end-to-end workflow:
- Topic selection. Topics arise from problems encountered in real engineering work — debugging sessions, code reviews, system design decisions, or research papers worth understanding deeply. Topics are not chosen primarily for search-traffic potential.
- Source research. The author reads the original paper, official documentation, source code, and authoritative references for the topic. Secondary aggregator sites are not treated as primary sources.
- Outline. A written outline determines the article’s scope, the code examples that will appear, and the figures that will illustrate the concept.
- Drafting. The author writes the article. Modern writing tools, including spell checkers, grammar checkers, and reference-management software, are used throughout — as is standard in technical writing.
- Code verification. Every Python, SQL, YAML, or shell snippet in a post is executed locally before publication. Output is checked. If a snippet does not produce the stated result, it is corrected or removed.
- Mathematical and conceptual verification. Equations are checked against the original papers or textbooks they reference. Conceptual claims are cross-checked against multiple sources.
- Source verification. Every external link is followed by the author to confirm that the page exists, points to a legitimate reference, and supports the claim in the body.
- Final edit. The author conducts a complete read-through for clarity, accuracy, and tone.
- Publication. Once the article meets the publication bar, it goes live with the author byline.
Fact-checking and corrections
Despite the verification process, errors will sometimes appear in print. The correction policy is straightforward:
- Readers who identify an error are invited to write to kongastral@gmail.com. Constructive corrections are received with appreciation.
- Substantive corrections — factual errors, broken code samples, incorrect citations — are made within seven days of being reported and are noted with an updated date on the post.
- Minor edits — typos, awkward phrasing — are made silently as they are encountered.
- If a substantial retraction ever becomes necessary, it will include a clear notice explaining what changed, why, and when.
Update cadence
Technical content has a finite shelf life. The following practices keep articles current:
- Articles covering specific software versions, library APIs, or research benchmarks are revisited when the underlying technology changes meaningfully.
- Code examples are periodically re-executed against current package versions to confirm they still function as written.
- The “Last updated” date shown on a post reflects substantive content revisions, not minor typographical fixes.
Sources and citations
Where an article relies on a specific paper, benchmark, dataset, or external claim, the source is treated as follows:
- The source is linked at the point of citation in the body and is also listed in the References section at the end of the article.
- Wherever possible, the link points to the original paper, official documentation, or primary data source — not to a secondary aggregator.
- Paraphrased material from a source is attributed to that source.
- Citations that cannot be supported by a real reference are not used. If a claim cannot be sourced, the claim is removed.
Conflict-of-interest disclosures
- The author does not receive compensation for the inclusion of any specific tool, library, framework, vendor, or service in any article.
- When the author personally uses a tool that is mentioned in an article, that fact is stated in the body.
- The site displays Google AdSense advertising. AdSense placements are not aware of post content at the editorial stage, and editorial decisions are not made on the basis of expected ad revenue.
Scope
The site publishes technical articles in two areas:
- Applied AI and machine learning — anomaly detection, domain adaptation, self-supervised and semi-supervised learning, Gaussian Processes, time-series modelling, and related topics.
- Software and data engineering — Apache Kafka, Apache Airflow, Debezium CDC, dbt, Docker, Kubernetes, Python, FastAPI, and related infrastructure.
The site does not publish content outside these areas. The site does not provide financial, medical, legal, or other professional advice of any kind.
Comments and community
Constructive comments and corrections from readers are welcome. The site moderates comments to remove spam, abuse, and irrelevant promotional content. Comments that simply disagree with an article’s conclusions are not removed.
Contact
For questions about content, corrections, or editorial policy, please write to kongastral@gmail.com. A reply can be expected within a few working days.
Last updated: June 2026.