---
title: "anckursingh (@anckursingh) | Hashnode"
description: "Learning development step by step. Read the latest articles by anckursingh on Hashnode."
url: "https://hashnode.com/@anckursingh"
---

@anckursingh

# anckursingh

@anckursingh · Amsterdam, Netherlands · Joined August 2026

Learning development step by step

### [Can AIKOQL Work Alongside PostgreSQL, Vector Databases, and Knowledge Graphs?](https://aikoql.hashnode.dev/can-aikoql-work-alongside-postgresql-vector-databases-and-knowledge-graphs?utm_source=hashnode&utm_medium=feed)

There is a question I expect almost every experienced engineer to ask when they first see AIKOQL: Why do we need another database? It is a fair question. Most organizations already have databases. T

16 min read · [aikoql.hashnode.dev](https://aikoql.hashnode.dev/can-aikoql-work-alongside-postgresql-vector-databases-and-knowledge-graphs?utm_source=hashnode&utm_medium=feed)

[Sep 11](https://hashnode.com/posts/can-aikoql-work-alongside-postgresql-vector-databases-and-knowledge-graphs/6aa38f80f8a1521433115dc0)

### [Why Not Just Use a Vector Database? The Case for Knowledge Infrastructure Beyond RAG](https://aikoql.hashnode.dev/why-not-just-use-a-vector-database-the-case-for-knowledge-infrastructure-beyond-rag?utm_source=hashnode&utm_medium=feed)

Vector databases have changed how AI applications work with information. They made semantic search practical. They made it possible to retrieve information based on meaning rather than exact keywords.

16 min read · [aikoql.hashnode.dev](https://aikoql.hashnode.dev/why-not-just-use-a-vector-database-the-case-for-knowledge-infrastructure-beyond-rag?utm_source=hashnode&utm_medium=feed)

[Sep 3](https://hashnode.com/posts/why-not-just-use-a-vector-database-the-case-for-knowledge-infrastructure-beyond-rag/6a995930ca44f9431ced4802)

### [From Documents to Knowledge: How Information Becomes a Knowledge Object](https://aikoql.hashnode.dev/from-documents-to-knowledge-how-information-becomes-a-knowledge-object?utm_source=hashnode&utm_medium=feed)

Most AI knowledge systems begin with documents. PDFs. Markdown files. Wiki pages. GitHub repositories. Support tickets. Logs. Architecture documents. Chat conversations. The usual pipeline looks famil

16 min read · [aikoql.hashnode.dev](https://aikoql.hashnode.dev/from-documents-to-knowledge-how-information-becomes-a-knowledge-object?utm_source=hashnode&utm_medium=feed)

[Sep 3](https://hashnode.com/posts/from-documents-to-knowledge-how-information-becomes-a-knowledge-object/6a994d488bd2ab69222f32c0)

### [Querying Knowledge: What Should an AI-Native Query Language Look Like?](https://aikoql.hashnode.dev/querying-knowledge-what-should-an-ai-native-query-language-look-like?utm_source=hashnode&utm_medium=feed)

In the previous posts, I explored Knowledge Objects, provenance, relationships, evidence, and the lifecycle of knowledge. But there is an obvious next question. «If knowledge has its own structure, st

15 min read · [aikoql.hashnode.dev](https://aikoql.hashnode.dev/querying-knowledge-what-should-an-ai-native-query-language-look-like?utm_source=hashnode&utm_medium=feed)

[Sep 3](https://hashnode.com/posts/querying-knowledge-what-should-an-ai-native-query-language-look-like/6a98fada77999edf98504908)

### [Knowledge Has a Lifecycle: Evidence, Contradiction, Supersession, and Time](https://aikoql.hashnode.dev/knowledge-has-a-lifecycle-evidence-contradiction-supersession-and-time?utm_source=hashnode&utm_medium=feed)

In most software systems, we think about data as something that changes. A database record is inserted. Then it is updated. Eventually, it may be deleted. But knowledge behaves differently. A piece of

15 min read · [aikoql.hashnode.dev](https://aikoql.hashnode.dev/knowledge-has-a-lifecycle-evidence-contradiction-supersession-and-time?utm_source=hashnode&utm_medium=feed)

[Sep 2](https://hashnode.com/posts/knowledge-has-a-lifecycle-evidence-contradiction-supersession-and-time/6a982a46353d7558dd90fe33)