Posts

If Dinosaurs Knew AI was Coming — LinkedIn

Image
If dinosaurs had known The Meteorite was coming, they would have done something about it. We don’t have that excuse. AI has reshaped how I think, build, and work as an engineer. Not because it replaced anything I do, but because it forced me to decide what I refuse to give up: agency, intent, and engineering judgment . Recently, during an interview for a Senior .NET role, I was asked two questions that made me articulate that identity clearly. The answers weren’t interview answers — they were reflections of how I build systems and how I work with AI. I’m sharing them here because they capture the core of who I am as an engineer. 1. What’s a system you’ve built, and what architectural decisions shaped it? I’m the creator and sole developer of WORKS Commons , an AI‑native platform built on .NET 10, Blazor, EF Core, and LLM-driven signal processing. But the technology stack isn’t the point. The point is the principle behind it: AI contributes intelligence, but never intent. The system o...
Post a Comment
Read more

If Dinosaurs Knew AI was Coming

Image
If dinosaurs had known that a meteorite was going to be the trigger of their demise — they would have done something about it. Jose Marino, 2026 Recently, while applying for a Senior .NET position, I was asked two questions that forced me to think carefully about architecture, AI, and the way I work as an engineer. Rather than answering with generic interview talking points, I used the opportunity to describe WORKS Commons—the AI-native platform I have been building—and my philosophy for working with AI. I reproduce those answers here. Describe a system you’ve built or owned in production using your core stack—what key architectural or design decisions did you make, and why? I currently own and develop an AI-native platform called WORKS Commons (WC), built with .NET 10, Blazor, EF Core, and OpenAI integrations for signal processing. One of the primary architectural decisions was adopting the MVVM pattern. I wanted a clear separation between the UI and presentation/business logic so tha...
Post a Comment
Read more

Models do not Have a Thinking Hat — It is Engineering at its Finest

Image
We have reached a point where AI engineering resembles the frantic dungeon inventors of Chitty Chitty Bang Bang. Locked away under the intense pressure of corporate barons demanding immediate magic, the industry has traded engineering discipline for wizard robes. They throw smoke bombs of vocabulary—reasoning, understanding, cognition—to hide the fact that they are trying to summon a ghost out of an autocomplete engine. Inevitably, the atmosphere becomes thick, leaving us with an aching question —  "Are we building actual machines, or are we just singing in the dungeon?" My frustration is understandable. The Linguistic Wild West Many of us continue to grapple with the idea of using this awesome force available to Software Engineering. Marketing hype has hijacked the language of cognition treating LLMs like "thinking beings" with "reasoning", "planning,"..."autonomy." In a recent user group Meetup at a leading organization, such as  REDA...
Post a Comment
Read more

What I Got Wrong About AI-First

Image
When I first started thinking about AI-First development, I framed it like this: AI systems interpret intent, generate outputs, and orchestrate workflows. That sounds reasonable. It’s also incomplete. After building a system around these ideas (WORKS Commons), I realized I was attributing too much to the model—and not enough to the system. The Misconception I thought: AI orchestrates the system. What I learned: The system orchestrates. AI contributes. That distinction is not semantic. It clarifies where control actually lives. What Changed In practice, reliable systems don’t depend on AI making decisions about what happens next. Instead: The LLM produces structured signals Those signals are captured and persisted as state The system uses that state to drive workflow execution The intelligence does not sit in the model. It emerges from: structured state controlled workflows feedback loops The Key Shift I stopped thinking in terms of: prompt ...
Post a Comment
Read more

AI-First Development and Structured Workflow Systems

Image
A Practical View from WORKS Commons Introduction: The Shift to AI-First In today’s tech landscape, "AI-First" is a term that gets thrown around frequently, often generating both excitement and apprehension. For many engineers, there is a lingering concern that an AI-first approach implies a loss of agency—that we are handing the steering wheel to a black box. However, my experience building AI-driven systems suggests otherwise. AI-First development is not about removing control from engineers. It is about shifting from static, hand-coded workflows to systems that interpret intent through AI-generated signals, while maintaining deterministic control over execution. In this model, humans define goals and constraints. The system—not the model—controls workflow execution. LLMs contribute structured outputs that inform decisions within that system. The Concept: What is AI-First Development? To understand AI-First, it helps to distinguish it from "AI-Added" system...
Post a Comment
Read more

Nugget 16: WORKS Commons Now has a Working Agentic Interviewer.

Image
Below is a sample of the serialized interview metadata. This data feeds the “Trainer Agent” (or agentic process). Currently, the Interviewer itself is not an agent—the “intelligence” is orchestrated by WORKS Commons. That distinction is pivotal for the Interviewer → Trainer pipeline. I’ve learned a lot building this. I love it. Model prompt engineering is part discipline, part art. Good practices are essential to reduce hallucinations. A strong prompt must: - Define clear instructions and rules - Specify how the data should be used - Establish boundaries and limitations - Explicitly describe the data schemas involved Anything outside those boundaries should be treated as: “I do not know.” Clarity here matters. A lot. WORKS Commons does not rely on HITL (Human-in-the-Loop) to mitigate hallucinations. When we use generative tools and push back in the editor—adding constraints, refining instructions—we are participating in the generation process. We reclaim some agency. WORKS Commons tak...
Post a Comment
Read more