Engineering Team
The engineering team at Originsoft Consultancy brings together decades of combined experience in software architecture, AI/ML, and cloud-native development. We are passionate about sharing knowledge and helping developers build better software.
In 2026, the gap between AI demos and real tools is defined by agency. This guide explains how to architect, orchestrate, and operate AI agents that can be trusted in production.
A deep dive into communication patterns for microservices. Learn when to use synchronous vs asynchronous communication and how to implement them effectively.
Moving Generative AI from demos to production is no longer about prompts. In 2026, success depends on architecture, cost discipline, observability, and trust at scale.
Running Kubernetes at scale can be expensive. Learn practical strategies to optimize costs without sacrificing performance or reliability.
Practical performance optimization techniques for React applications. From rendering optimization to code splitting, learn what actually matters.
Build reliable, fast CI/CD pipelines that your team will love. From testing strategies to deployment patterns, this guide covers it all.
A deep dive into how system design has evolved by 2026. Learn how to architect scalable, reliable systems for agentic workloads, data density, and operational efficiency.
A pragmatic look at modern mobile app development. Learn how architecture, logic sharing, server-driven UI, and delivery strategies matter more than framework choices.
Agents as a Service (AaaS) is poised to replace SaaS and create a new generation of millionaires. Learn what AaaS is, why it matters, and how to build your own AI agent business.
Swarm agents and multi-agent systems let specialized agents collaborate to solve complex workflows. This article walks through a Python implementation and explores why MAS matter.
A compact, hiring-manager-friendly ML pipeline that goes from raw CSV to reproducible experiments using DVC stages and MLflow tracking on DagsHub, with metrics and model artifacts logged every run.
A hands-on, copy-paste-ready walkthrough to track Keras/TensorFlow experiments in MLflow, run Hyperopt tuning with nested runs, register the best model, and serve it as a REST API.
A practical guide to building multi-agent workflows with CrewAI—how agents, tasks, crews, and tools fit together, plus six real scenarios like job search automation, lead generation, and trend analysis.
Meta’s VL-JEPA research challenges the foundations of modern AI. Explore why leading researchers believe token-based language models may not be the future of intelligence.
Prompt engineering is no longer enough. Learn why flow engineering and agentic workflows now define how reliable, scalable AI systems are built.
A statistics-flavored argument for bounded inequality: why extreme wealth outliers signal an unstable system, and why a narrower distribution can mean a healthier society.
