Insights on applied AI, AI-ready engineering teams, machine learning adoption, and practical ways companies use artificial intelligence to improve products, workflows, and team productivity.
For most engineers, a large part of the day used to be writing code: get a requirement, dig through the codebase, write the implementation, run it, debug what broke, and repeat until it worked. On projects that use an AI coding agent, that loop looks different. The engineer describes the problem, provides context, and lets…
Why classic interviews miss agentic skill, and how to evaluate AI-first engineers: interview questions, exercises, red flags and a scorecard.
You have probably seen the diagram: seven boxes stacked from Classical AI at the bottom to AGI at the top, with machine learning, deep learning, generative AI, and agentic AI in between. The diagram is useful. It separates terms that are often blurred together in product conversations. But it also suggests a progression that does…
Three-quarters of CFOs are raising their technology budgets for 2026, with nearly half increasing spend by 10% or more, according to Gartner. The days of AI in finance being confined to a sandbox pilot are over. The mandate has shifted from “should we build this” to “why hasn’t this shipped yet.” The gap between organizations…
What is a forward deployed engineer? A forward deployed engineer (FDE) is a senior software engineer who works directly inside a customer’s or business’s real environment to build, integrate and ship software, usually AI, that makes a product work there. Instead of building one feature for many users, an FDE owns the outcome for one…
Most people have heard of Salesforce. Far fewer can explain what it actually does — let alone what it’s become in the last two years. If you’ve searched “what is Salesforce” expecting a simple answer, here’s the honest version: it started as a CRM in 1999 and became the operational backbone of more than 150,000…
Every developer has had this experience: you describe what you want to an AI model, it generates something that looks right, you run it, and something breaks in a way you didn’t expect. You iterate. It breaks differently. You iterate again. Twenty minutes later you’re not sure if the AI saved you time or cost…
Artificial intelligence (AI) is knocking technology out of the park, especially regarding machine learning (ML).
Every team building with LLMs hits the same fork in the road eventually: should we use RAG, fine-tune the model, or build an agent? The three options are frequently discussed as if they’re interchangeable, but they solve fundamentally different problems. Picking the wrong one doesn’t just add cost, it creates architectural debt that’s painful to…
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