Fine-tuning is the process of adapting a general, pre-trained AI model by training it further on a smaller, high-quality, domain-specific dataset. This critical step refines the model’s focus, allowing it to accurately use client-specific jargon, understand business nuances, and achieve greater precision in tasks like code generation or complex document analysis.
When you’re in the business of helping companies with IT staff augmentation, you learn a thing or two about building up tech teams quickly and efficiently. Growing your software development team might seem easy — post the job online, conduct a few interviews, hire, and you’re done, right? Not quite. Depending on your own technical know-how […]
This post explains what is Temporal, how its Temporal workflow model works in Go, and how to use Temporal to make microservices reliable through retries, backpressure, and durable state. You’ll see a real example of Temporal + Golang, learn how to start function execution from beginning in Golang safely, and understand what does Temporal do […]
AI coding tools give non-technical founders incredible speed – but also hidden architectural risks that only appear once the product feels nearly finished. This post explains why AI-assembled MVPs can become unmanageable, how to tell a prototype from a real platform, and what to do when your project starts “breaking under its own weight”. AI […]
