Thoughts on building software, shipping AI features, and working with engineering partners — written from real project experience.
A survey of common RAG techniques, how RAG compares with other chatbot approaches, and a detailed walkthrough of a real RAG codebase.
How we package Python source into binary .so files to protect intellectual property when deploying on-premise for customers.
How our engineering team signed large documents with a USB Token directly in the browser: fast, lightweight, and secure — without downloading files to the workstation.
An architectural look at Redis Streams in distributed systems, compared with Redis Pub/Sub, and when to choose it for asynchronous task processing.
Explore how to design intelligent AI agents with LangGraph: from state-graph thinking (StateGraph) and smart routing to self-reflection and self-correction.
An introduction to the Model Context Protocol (MCP) — an open standard for safely connecting large language models (LLMs) to enterprise data sources and tools.
Triton is a high-performance bridge that takes trained models, manages GPU/CPU resources transparently, and exposes production-ready inference endpoints.
When an application must serve a large number of users, handling many requests at once becomes a real challenge. A comparison of multithreading and asynchronous programming in Python.
Edge computing moves computation and data storage from centralized data centers closer to where data is created. An overview of Edge AI and how it differs from cloud AI.
Tokenization is the most fundamental and decisive preprocessing step in every modern NLP pipeline — from basic sentiment classification to training large language models.
DagsHub = GitHub + DVC + MLflow + Label Studio. A complete MLOps platform for managing code, data, experiments, models, and collaboration in one place.
