Is it possible to shift to local LLMs? Centralized APIs might be replaced by local LLMs, allowing developers to run powerful AI directly on their machines. This shift has the potential to revolutionize the way we approach AI development, giving…
What are the trends and challenges in agentic AI architecture? The landscape of AI is shifting towards more autonomous and goal-driven systems, leaving behind the single Large Language Model calls that dominated the past. Today, we’re witnessing a new wave…
AI agent testing faces challenges that are different than traditional software testing. Traditional testing methods often rely on predictable outcomes, but modern AI agents don’t play by those rules. Their non-deterministic outputs throw a wrench into the testing process, breaking…
Is everything fine or is AI replacing software engineers? There is lately often medialized decrease of software engineers in the last few years. You might be wondering if the fears of job displacement in the tech industry are a new…
Deciding whether to build an AI agent can be a puzzling task, especially when faced with the promise of cutting-edge technology. However, it’s essential to understand that AI agents are not a one-size-fits-all solution. They make sense when decisions require…
By 2028, 60% of software engineering teams will use AI evaluation and observability platforms – up from just 18% in 2025. The future of software engineering is rapidly evolving, and AI-powered tools are at the forefront of this revolution. One…
The adoption of AI in the engineering world has reached a boiling point. We’re not talking about early adopters or pioneers – we’re talking about the norm. Around 90% of engineering teams are now using AI tools daily, making it…
The Context Gap in AI Assisted Programming The biggest limitation of modern AI assisted programming tools is not intelligence – it is context. While tools like Claude or IDE agents can generate high-quality code, they remain blind to your local…
Resilience engineering is crucial for LLM reliability in production environments. This involves handling various types of failures, such as rate limits, timeouts, and model-specific errors, to ensure a seamless user experience. When it comes to Large Language Models, reliability is…
Semantic search over local Markdown documentation is a common backend requirement. Traditional tools like grep work well for keyword matching, but they fail when the query requires intent understanding. This is where RAG (Retrieval-Augmented Generation) becomes useful. To explore this…
