Literature review

 ที่ม สุรนารี ใช้คำภาษาไทยว่า ปริทัศน์วรรณกรรม

Algorithms for Competitive Programming

 https://cp-algorithms.com/index.html

AI for creating VR world

https://labs.google/projectgenie

https://www.youtube.com/watch?v=YxkGdX4WIBE

UML creation scripting (similar to LaTex)

• https://plantuml.com/
• plantuml in VS' marketplace : https://marketplace.visualstudio.com/items?itemName=jebbs.plantuml
• try the following code in this editor https://editor.plantuml.com

@startuml

Alice -> Bob : hello

@enduml

Multilingual AI detector (not free)

https://app.originality.ai/home/scan 

It supports Thai language.

Call number codes

 

Superlinear, Linear, Sublinear

Superlinear คือ slop เพิ่มขึ้นเรื่อยๆ (โตเร็วกว่า linear)

Sublinear คือ slope ลดลงเรือยๆ (โตช้ากว่า linear)

Example:

Settrade streaming pro

 

Speech recognition model by OpenAI

Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification.

https://github.com/openai/whisper

Major types of recommendation systems

Collaborative Filtering (finding similar users/items based on past behavior), Content-Based Filtering (recommending items with similar features to what a user liked), and Hybrid Systems (combining both for better results). Other advanced types include Knowledge-Based, Deep Learning, and Demographic systems, which leverage different data points like item attributes, complex patterns, or user demographics to provide personalized suggestions. 

Collaborative Filtering (CF):

• Concept: "People who liked X also liked Y." It finds patterns in user-item interactions (ratings, purchases).
• Sub-types: User-based (If User A buys Item A, and a neighboring User B (who is found to be highly similar to User A based on their shared past interactions like rating or purchases) also buy Item B but User A has not yet bought it, then the system should recommend Item B to User A.) and Item-based (If a user likes item A, and many users who liked item A also liked item B, then the system should recommend item B to that user.).
• Techniques: Matrix Factorization (like SVD), Nearest Neighbors.

Content-Based Filtering:

• Concept: Recommends items similar to those a user has liked before, based on item features (e.g., movie genre, director, actors). If a user liked item A, they will also like item B if item B shares many of the same features as item A.
• How it works: Builds a user profile from features of liked items, then matches it to other items based on similarity. The features can also include those of the user like age and gender.

Hybrid Recommendation Systems:

• Concept: Merges CF and Content-Based methods, or other techniques, to overcome individual limitations (like the cold-start problem). 
  

--Gemini

LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback.

LightFM can solve cold-start problem: For a new user (cold-start user), LightFM uses their provided features (e.g., "age: 25, gender: female") to do content-based filtering to recommend items that share similar features with the user's profile. As the cold-start user interacts with items, the model gradually updates their information, shifting from pure content-based to a more personalized collaborative prediction.

https://making.lyst.com/lightfm/docs/home.html#examples

 

Google AI Studio vs Colab (answered by Gemini)

 

Daysie AI-Edge PaaS by Nectec

https://daysie.io/

https://www.conferencethaiseries.com/wp-content/uploads/2025/10/%E0%B8%9B%E0%B8%B2%E0%B8%90%E0%B8%81%E0%B8%96%E0%B8%B2-6-The-Rise-of-Edge-Computing-AI-Beyond-the-Cloud-%E0%B9%82%E0%B8%94%E0%B8%A2-%E0%B8%94%E0%B8%A3.%E0%B8%9E%E0%B8%99%E0%B8%B4%E0%B8%95%E0%B8%B2-%E0%B8%9E%E0%B8%87%E0%B8%A9%E0%B9%8C%E0%B9%84%E0%B8%9E%E0%B8%9A%E0%B8%B9%E0%B8%A5%E0%B8%A2%E0%B9%8C.pdf

infrastructure as code

 AWS Cloudformation

Terraform (https://developer.hashicorp.com/terraform/tutorials/aws-get-started/infrastructure-as-code)

Google Optimization Tool

OR-Tools is an open source software suite for optimization, tuned for tackling the world's toughest problems in vehicle routing, flows, integer and linear programming, and constraint programming.

https://developers.google.com/optimization

Online learning vs Offline learning

Online learning (or online machine learning) is a method in machine learning where the model continuously learns from a sequential stream of data, updating its parameters incrementally with each new data instance or small batch of data.

It is a dynamic process that allows a model to adapt to new patterns and changes in the data distribution in real-time.

Online learning is the opposite of the more traditional Batch Learning (or Offline Learning) approach.

Low code RAG development tool

Workflow automation tool

https://n8n.io/ 

AI builder tool

https://www.langflow.org/

Evaluate RAG

https://towardsdatascience.com/evaluating-your-rag-solution/

Notebooklm is RAG as we can add files to let users ask anything about the files.