AI Insights Blog

Explore practical writing on fintech AI, RAG chatbots, trading dashboards, payment integrations, automation, and full-stack AI product engineering.

Fintech AI RAG & Agents Full-Stack Engineering

Articles Built From Delivery Experience

These posts are shaped by production work across trading systems, fintech SaaS, AI-driven analytics, RAG assistants, mobile apps, payment integrations, and automation-heavy software.

Fintech & Trading Engineering

Market data, dashboards, alerts, charting, portfolio analytics, and the architecture behind live financial products.

AI Systems in Products

RAG pipelines, semantic search, intelligent assistants, and how AI features fit into complete applications.

Full-Stack Delivery Patterns

Django, FastAPI, React, React Native, databases, APIs, performance, security, and deployment workflows.

Latest AI Insights

7 articles
How a Small Number of Samples Can Poison LLMs of Any Size
Large Language Models LLM news LLM posion
Oct 26, 2025 5 min read 1619

How a Small Number of Samples Can Poison LLMs of Any Size

In recent years, the tremendous growth of large language models (LLMs) has transformed artificial intelligence — from generative chatbots to code assistants and domain-specific agents. With model sizes soaring into billions of parameters and training datasets spanning billions of tokens.

What is a Vector Database and How It Works
Large Language Models LLMs NLP
Oct 18, 2025 5 min read 837

What is a Vector Database and How It Works

A vector database is a specialized system designed to store, index, and search numerical vector embeddings generated from unstructured data like text, images, videos, and audio. These embeddings capture semantic meaning rather than exact keyword matches, enabling context-aware retrieval and reasonin

How to Use Google Cloud TPUs with Hugging Face Libraries
AI Optimization Hugging Face Large Language Models
Sep 11, 2025 5 min read 1415

How to Use Google Cloud TPUs with Hugging Face Libraries

The rapid growth of large language models (LLMs) and transformer architectures has driven the demand for specialized hardware. While GPUs have been the traditional choice, Google Cloud TPUs (Tensor Processing Units) offer significant acceleration for deep learning workloads, especially when working

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