MongoDB stack

 MERN stack: MongoDB, Express, Angular, Node

https://www.mongodb.com/resources/languages/mern-stack

Supabase

Supabase is the Postgres development platform. Start your project with a Postgres database, Authentication, instant APIs, Edge Functions, Realtime subscriptions, Storage, and Vector embeddings.

When to use Unstructured/Semi-structured Data?

Unstructured/Semi-structured Data is well-suited for data that doesn't fit neatly into the rows and columns of a traditional table, such as chat logs, various-structured notification messages, product catalogs with varied attributes, or IoT data. 

Pyvis

 A framework for Interactive network visualization

LLM temperature

LLM temperature is a hyperparameter (typically 0 to 2) that controls the randomness and creativity of an AI's output by adjusting the probability distribution of predicted tokens. Lower temperatures (0-0.3) produce deterministic, focused, and factual results, while higher temperatures (>0.8) create more diverse, random, or "creative" text.

https://dagshub.com/glossary/llm-temperature/

The following article shows how to use Bash script to interact with Ollama

https://www.inferable.ai/blog/posts/model-temperature-first-principles

Zero Temperature (0.0): Makes the model completely deterministic. It will always choose the token (word or syllable) with the absolute highest probability at that exact step.

While setting the temperature to $0$ forces the model to choose its "best guess" every single time, the model's best guess can still be completely wrong.

How Hallucinations Are Actually Mitigated

Because temperature is just a decoding hyperparameter, engineers and researchers use structural architectural patterns to combat hallucinations:

• Retrieval-Augmented Generation (RAG): Grounding the model by supplying it with verified, external documents to reference before it generates an answer.

• Fine-Tuning & Reinforcement Learning from Human Feedback (RLHF): Training the model specifically to say "I don't know" when it lacks data, rather than guessing.

  RLHF: Human evaluators rank llm answers from best to worst based on quality, accuracy, and safety.

• System Prompt Constraints: Explicitly instructing the model (e.g., "If you do not find the answer in the provided context, state that you do not know.").

If you are working on a system where factual accuracy is paramount, keeping the temperature low (around 0.0 to 0.2) is a great baseline practice—just don't mistake determinism for truth.

Tech stack

A tech stack (technology stack) is the combination of programming languages, frameworks, libraries, databases, front-end tools, back-end tools, and APIs used to build and run a software application. It acts as the "solution stack" or, foundation, organizing tools into layers—client-side (front-end) and server-side (back-end)—that work together to create a functional, scalable application. 

Key Components of a Tech Stack:

• Front-End (Client-Side): What users interact with, including HTML, CSS, JavaScript, and frameworks like React (for building web app with JavaScript), React Native (for building Mobile app) or Angular.
• Back-End (Server-Side):
   The "under the hood" logic, including programming languages like Python, Java, or Node.js, Docker, and frameworks like 
  Django
   or 
  Express
  .
Database: Where data is stored, such as MySQL, PostgreSQL, or MongoDB.

Popular Examples: LAMP: Linux, Apache, MySQL, PHP/Python/Perl.

IDE: Android Studio, Xcode, VScode

UX/UI Design: Figma

Backtracking

Backtracking is a class of algorithms for finding solutions to some computational problems, notably constraint satisfaction or enumeration problems, that incrementally builds candidates to the solutions, and abandons a candidate ("backtracks") as soon as it determines that the candidate cannot possibly be completed to a valid solution.

Example is Maze Solving (เขาวงกต): Moving through a maze and turning back when you hit a wall to try the other fork in the road.

Backtracking is implemented by means of recursion.

Poisson distribution (& Binomial distribution & Gaussian distribution)

Poisson distribution expresses the probability of a given number of events of the same type (e.g., counting the number of emails received between 9:00 AM and 10:00 AM. (one event type: emails)) occurring in a fixed interval of time if these events occur with a known constant mean rate and independently of the time since the last event.

Poisson distribution vs Binomial distribution

Gaussian distribution is aka Normal distribution often used to examine students' scores

--Gemini & Wikipedia

Optimization algorithms for Deep learning

 https://neptune.ai/blog/deep-learning-optimization-algorithms

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