Why I Declined Reviewing a Course for a Major Learning Platform

 

• I recently declined reviewing a course for one of the world's largest learning platforms.

• The content was largely AI-generated, and reviewing it line by line would have required rewriting it from an engineering perspective rather than simply approving it.

• Content that sounds polished is not necessarily content built from real-world experience.

• AI can generate explanations, but it cannot replace years of building, failing, learning, and making difficult engineering decisions.

• As AI-generated learning becomes more common, be thoughtful about where you learn. Experience still matters.

AI Can Generate Content. Experience Still Has to Be Earned.

Keep Thinking!!!

ESG leadership exploring domain‑specific ESG solutions? Let’s talk.

• Common pain points I hear: fragmented plant/supplier data, manual ESG/BRSR/CSRD spreadsheets, and generic tools that don’t fit textiles/fashion/manufacturing realities.
• If you’re a CSO/Head of ESG, CFO/CIO for ESG data & systems, or an ESG/impact investor interested in sector‑specific ESG infra, I’d love to connect, understand your current challenges, and explore a low‑risk pilot or partnership.
• phygitalytics.com is actively working in this space.

Let’s talk.

Vector Databases Reads

 Milvus Notes - Index / consistency / availability options

#1. Index type - usecase

• IVF_FLAT - High-speed query
• IVF_PQ - Very high-speed query
• HNSW - High-speed query

Inverted File (IVF): An IVF index divides the vector space into several clusters and holds an inverted file for each cluster, recording which vectors belong to the cluster.

IVF Flat: This is a combination of IVF and flat index. It uses the IVF index to partition the data into clusters and then uses the flat index (brute-force search) within each cluster.

Hierarchical Navigable Small World (HNSW): HNSW builds a multi-layer navigation graph to represent the vector space.

#2. Consistency levels - Strong, Bounded, Session or Eventually

• Strong - Most strict
• Bounded staleness - allows data inconsistency during a certain period of time.
• Session - Like dirty reads 
• Eventually - weakest level among the four.

#3. HA - In-memory replicas help Milvus recover faster if a query node crashes.

#4. Vector search & Hybrid Search params offset, limit

offset - Number of results to skip in the returned set

limit - Number of the most similar results to return

How indexing and querying works

• Trees – ANNOY -  Annoy (Approximate Nearest Neighbors Oh Yeah)
• Proximity graphs - HNSW Hierarchical Navigable Small World (HNSW) Graphs
• Clustering - FAISS
• Hashing -  LSH - Locality-Sensitive Hashing (LSH)
• Vector compression - PQ or SCANN. - ScaNN (Scalable Nearest Neighbors). Product Quantization (PQ): PQ index compresses vectors into compact codes and is beneficial for large-scale, high-dimensional data.

More Reads

• Utilizing Few-shot and Zero-shot learning with OpenAI embeddings
• Query Comparison
• Accelerating Similarity Search on Really Big Data with Vector Indexing

Keep Exploring!!!