17 projects categorized with "AI"
- Python FastAPI with JWT Auth serving RAG by fake Embeddings (v1)
08-July-2026Python FastAPI with JWT Auth serving a RAG Application using Groq + fake embeddings (v1) - hosted at Vercel Cloud using Serverless Functions
A Starter FastAPI + JWT Auth + Retrieval-Augmented Generation (RAG) by Groq LLM + fake embeddings + OpenAPI / Swagger - secured by HTTPS
A PostgreSQL database was used with the pgvector extension
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 RAG by Hugging Face embeddings (v2)
08-July-2026Python FastAPI with JWT Auth serving a RAG Application using Groq + Hugging Face embeddings (v2) - hosted at Vercel Cloud using Serverless Functions
A Starter FastAPI + JWT Auth + Retrieval-Augmented Generation (RAG) by Groq LLM + Real embeddings by Hugging Face model + OpenAPI / Swagger - secured by HTTPS
A PostgreSQL database was used with the pgvector extension
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 a Tool-Calling AI Agent using LangChain
05-July-2026Python FastAPI with JWT Auth serving a Tool-Calling AI Agent using LangChain - hosted at Vercel Cloud using Serverless Functions
A production-style FastAPI backend built with LangChain, Groq LLMs, and JWT authentication. The system implements a modern tool-calling AI agent architecture that automatically decides when to use external tools such as Wikipedia for factual retrieval
A Starter FastAPI + JWT Auth + LangChain + AI Agent + 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 a Tool-Calling AI Agent
04-July-2026Python FastAPI with JWT Auth serving a Tool-Calling AI Agent - hosted at Render Cloud by Webservice
The AI agent implements a modern 3-phase Tool Agent Pipeline (Plan → Execute → Synthesize) where tools are safely selected, executed, and used to generate grounded responses using a minimal of Langchain
A Starter FastAPI + JWT Auth + AI Agent + 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 + Render Cloud
The Web API at GitHub - Python FastAPI with JWT Auth serving a ReAct-inspired AI agent system
03-July-2026Python 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
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-2026Python 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 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 Vue SPA at GitHub - AI Agent Design Patterns
07-June-2026ReAct (Reason + Act)
Concept: Alternate between reasoning steps and taking actions (tool calls, API calls, etc.)
Flow:
- LLM generates reasoning / thought
- Take action (call a tool or external system)
- 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:
- LLM receives input
- LLM selects which tool(s) to call
- Execute tool(s)
- 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:
- LLM generates a plan
- Execute each step using tools or computations
- 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:
- Generate multiple candidate answers
- Critique or evaluate each candidate
- 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:
- Retrieve relevant past memory
- Reason using current input + memory
- 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-2026Python 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
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-2026Python 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
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 - Python FastAPI with JWT Auth serving ML Inference API predicting house prices using Ames Dataset and PyTorch (v6)
05-May-2026Python FastAPI with JWT Auth predicting House Prices (v6) - 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
A Starter FastAPI + JWT Auth + Deep Learning + 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 - Python FastAPI with JWT Auth serving ML Inference API predicting house prices using PyTorch (v5)
03-May-2026Python FastAPI with JWT Auth predicting House Prices (v5) - hosted at Vercel Cloud using Serverless Functions
This version is using a synthetic generated Dataset predicting houce prices by a PyTorch-trained MLP model exported to ONNX format ready for running at various platforms
A Starter FastAPI + JWT Auth + Deep Learning + ONNX + House Price Predicting + 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 predicting house prices by Machine Learning using Linear Regression (v4)
22-April-2026Python FastAPI with JWT Auth serving a Machine Learning Application using Linear Regression and ONNX Predicting House Prices (v4) - hosted at Vercel Cloud using Serverless Functions
This version predict the house price by size, number of rooms, year built, location and condition and saves the model in ONNX. This format is great for Vercel Serverless as well as other platforms
A Starter FastAPI + JWT Auth + ML + Linear Regression + ONNX + Predicting house prices + 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 predicting house prices by Machine Learning using Linear Regression and logs Predictions by PostgreSQL (v3)
20-April-2026Python FastAPI with JWT Auth using PostgreSQL serving a Machine Learning Application Predicting House Prices using Linear Regression (v3) - hosted at Vercel Cloud using Serverless Functions
This version predict the house price by size, number of rooms, year built, location and condition and saves the model in pkl. In this version the predictions are saved in a PostgreSQL database hosted at Neon
A Starter FastAPI + JWT Auth + PostgreSQL + ML + Linear Regression + Predicting house prices + 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 predicting house prices by Machine Learning using Linear Regression (v2)
19-April-2026Python FastAPI with JWT Auth serving a Machine Learning Application Predicting House Prices using Linear Regression (v2) - hosted at Vercel Cloud using Serverless Functions
This version predict the house price by size, number of rooms, year built, location and condition and saves the model in pkl
A Starter FastAPI + JWT Auth + ML + Linear Regression + Predicting house prices + 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 predicting house prices by Machine Learning using Linear Regression (v1)
18-April-2026Python FastAPI with JWT Auth serving a simple Machine Learning Application Predicting House Prices using Linear Regression (v1) - hosted at Vercel Cloud using Serverless Functions
This version predict the house price by size and number of rooms and saves the model in pkl
A Starter FastAPI + JWT Auth + ML + Linear Regression + Predicting house prices + 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 - .NET 8 Machine Learning used for tasting wine
17-April-2026.NET 8 Console App for tasting wine using ML.NET with C#
Explore Artificial Intelligence and Machine Learning
This is a console application using Microsoft’s Machine Learning framework ML.NET for tasting wine
FastTree regression used to train the Model
The code at GitHub - .NET 8 Machine Learning to predict global temperature
17-April-2026.NET 8 Console App to predict the global temperature using ML.NET with C#
Explore Artificial Intelligence and Machine Learning
This is a console application using Microsoft’s Machine Learning framework ML.NET to predict the global temperatures
Singular Spectrum Analysis (SSA) model for univariate time-series forecasting using the method "ForecastBySsa" of the class "TimeSeriesCatalog" for training the Model
The code at GitHub