How engineering teams use AI in software development: agentic coding, RAG, MCP and spec-driven development, plus what to look for when hiring AI engineers who can build with these tools.
Revenue Operations (RevOps) is no longer just a buzzword—it’s becoming the backbone of modern SaaS and tech growth. By breaking down silos between sales, marketing, and customer success, RevOps creates one engine that drives predictable and scalable revenue. And here’s the kicker: by 2026, 75% of the fastest-growing companies will run on RevOps. That’s huge.
According to PwC, over 70% of executives believe AI will significantly reshape their business in the next three years. Yet only about one in five organizations has a comprehensive responsible-AI policy in place. That gap isn’t academic—it’s expensive. We’ve all seen the headlines: biased hiring models quietly sidelining women; underwriting algorithms triggering class-action lawsuits; deepfake…
A year ago I wrote that by 2026 engineers would stop “calling an API” and start orchestrating AI systems. That happened faster than I expected. In most of the teams I talk to, engineers no longer write most of their code by hand. They direct agents, review what comes back and…
I’m Damian Wasserman, co-founder at BEON.tech. I wrote this as part of my ongoing exploration into how AI is transforming software engineering. Through these posts, I aim to share practical takeaways to help our 150+ BEONers — and the broader developer community — stay at the forefront of this new era of AI-augmented engineering because…
AI is everywhere, and executives are under pressure to deliver real results with it. But building AI-powered products isn’t about hiring the trendiest new role or throwing people at the problem — it’s about assembling the right engineering team. In this post, we’ll walk you through the AI engineer tech stack that actually delivers value…
It’s no secret that AI tools are reshaping how developers work. From automating boilerplate code to boosting daily productivity, artificial intelligence has evolved from a sidekick to a co-pilot — and in many cases, the pilot itself. The adoption of these tools is rapidly becoming the norm, with usage rates ranging from 70% to 82%…
The rapid rise of AI applications — from customer service chatbots to sophisticated generative design tools — has placed data quality at the heart of AI development. While high-quality training data is essential for building reliable, high-performing models, not all data is created equal. Broadly, AI training datasets fall into two main categories: Each type…
Code-generating large language models (LLMs) are transforming software development. By automating repetitive coding tasks, assisting with debugging, and even generating functional code from simple prompts, these models allow development teams to work faster, more efficiently, and with greater focus on complex challenges. As businesses increasingly adopt AI-powered tools to streamline operations and reduce development cycles,…
Artificial intelligence is no longer a future concept in software development—it’s a present day advantage. From streamlining tasks to accelerating development cycles, AI is becoming an essential ally in the engineering process. This shift has sparked an important question among tech leaders: Will AI replace software engineers? The short answer is no. While AI is…
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