View the Projects by pagnition

A Blog full of AI Projects

View the Projects by the navigation at the bottom of the Page sorted by Date...

Python FastAPI with JWT Auth serving a ReAct-inspired AI agent system

03-July-2026

Python FastAPI with JWT Auth serving a ReAct-inspired AI agent system - hosted at Vercel Cloud using Serverless Functions

The AI agent system follows a lightweight ReAct-inspired flow. A simple router determines when to use a Wikipedia tool, and the retrieved context is passed to the model to generate the final answer using a minimal of Langchain

Try the demo by OpenAPI...

A Starter FastAPI + JWT Auth + AI Agent system + OpenAPI / Swagger - secured by HTTPS

During the development process, I used ChatGPT for assisting with code generation and Github Copilot for code inline suggestion

DevOps by VS Code + GitHub + Vercel Cloud

The Web API at GitHub
Python FastAPI with JWT Auth serving House Price Predicting using Deep Learning with the Ames Housing Dataset (v8)

23-June-2026

Python FastAPI with JWT Auth predicting House Prices using PyTorch and focusing on Tests (v8) - hosted at Vercel Cloud using Serverless Functions

This version is using the Ames Housing Dataset predicting houce prices by a PyTorch-trained MLP model exported to ONNX format ready for running at various platforms and focusing of different kinds of Tests and a Vue 3 SPA

Try the demo by OpenAPI...

Try the demo by a Vue 3 SPA...

A Starter FastAPI + JWT Auth + Deep Learning + Tests + ONNX + House Price Predicting + Ames Housing Dataset + OpenAPI / Swagger + Vue SPA - secured by HTTPS

During the development process, I used ChatGPT for assisting with code generation and Github Copilot for code inline suggestion

DevOps by VS Code + GitHub + Vercel Cloud

The Web API at GitHub

The Vue SPA at GitHub
AI Agent Design Patterns

07-June-2026

ReAct (Reason + Act)

Concept: Alternate between reasoning steps and taking actions (tool calls, API calls, etc.)

Flow:

  1. LLM generates reasoning / thought
  2. Take action (call a tool or external system)
  3. Observe output and feed it back into reasoning

Strengths: Multi-step problem solving, tool orchestration, grounded responses

Use case: Complex question answering, multi-tool agents, decision-making systems

Tool-Calling / Tool-Augmented Agents

Concept: The LLM acts as a controller that decides whether to call external tools

Flow:

  1. LLM receives input
  2. LLM selects which tool(s) to call
  3. Execute tool(s)
  4. Synthesize final answer from tool output

Strengths: Reduces hallucinations, improves grounding, enables use of APIs and external systems

Use case: Wikipedia-style assistants, math solvers, structured data retrieval

Reflex / Reactive Agents

Concept: Immediate response without planning or multi-step reasoning

Flow: Input → Response

Strengths: Very fast, low complexity, low cost

Use case: Chatbots, simple Q&A systems

Plan-and-Execute / Hierarchical Planning Agents

Concept: The agent first creates a plan, then executes steps sequentially

Flow:

  1. LLM generates a plan
  2. Execute each step using tools or computations
  3. Return final result

Strengths: Strong for complex workflows and multi-step tasks

Use case: Automation systems, workflow orchestration, research agents

Debate / Self-Reflection Agents

Concept: Multiple candidate outputs are generated and evaluated before final selection

Flow:

  1. Generate multiple candidate answers
  2. Critique or evaluate each candidate
  3. Select the best final output

Strengths: Reduces errors, improves reliability, reduces hallucinations

Use case: Code review, summarization, high-accuracy Q&A

Memory-Augmented Agents

Concept: The agent stores and retrieves long-term memory to maintain context

Flow:

  1. Retrieve relevant past memory
  2. Reason using current input + memory
  3. Update memory with new information

Strengths: Personalization, continuity, long-term context awareness

Use case: Personal assistants, long-running agents, adaptive systems

Summary
  • ReAct: reasoning + acting with tools in loops
  • Tool-Calling: LLM chooses external tools
  • Reflex: direct single-step response
  • Plan-and-Execute: plan first, then execute steps
  • Debate: multiple outputs + self-evaluation
  • Memory-Augmented: persistent context over time
Python FastAPI with JWT Auth serving ML Inference API solving the XOR Problem by PyTorch

01-June-2026

Python FastAPI with JWT Auth serving a PyTorch-trained MLP model exported to ONNX with strict XOR input validation - hosted at Vercel Cloud using Serverless Functions

Try the demo by OpenAPI...

A Starter FastAPI + JWT Auth + Deep Learning to solve the XOR Problem + OpenAPI / Swagger - secured by HTTPS

During the development process, I used ChatGPT for assisting with code generation and Github Copilot for code inline suggestion

DevOps by VS Code + GitHub + Vercel Cloud

The Web API at GitHub
Python FastAPI with JWT Auth serving House Price Predicting using Classic Machine Learning with the Ames Housing Datase (v7)

06-May-2026

Python FastAPI with JWT Auth prediction House Prices using Linear Regression (v7) - hosted at Vercel Cloud using Serverless Functions

This version is using the Ames Housing Dataset predicting houce prices by a model trained by Linear Regression exported to ONNX format ready for running at various platforms

Try the demo by OpenAPI...

A Starter FastAPI + JWT Auth + ML + Linear Regression + ONNX + House Price Predicting + Ames Housing Dataset + OpenAPI / Swagger - secured by HTTPS

During the development process, I used ChatGPT for assisting with code generation and Github Copilot for code inline suggestion

DevOps by VS Code + GitHub + Vercel Cloud

The Web API at GitHub

12345

AI - Machine Learning - Deep Learning - RAG