Perete Harrison

Are you tired of AI coding agents churning out generic, flat, and uninspired frontend layouts? In this video, we dive into why standard LLM outputs often result in "AI slop" and how to fix it with the AXON framework. We explore AXON-COMPOSE, a specialized frontend design skill that forces AI agents to move beyond static, repetitive code. By enforcing composition-first principles, premium typography, and intentional motion, AXON-COMPOSE delivers high-fidelity UIs that are ready for production. In this video, we demonstrate: The common pitfalls of "Baseline" AI coding output. How AXON’s spec-driven development protocol changes the game. A side-by-side comparison of standard LLM results versus the high-fidelity outputs of AXON-COMPOSE. Stop the AI slop and start shipping predictable, visually stunning software with a protocol built for high-performance teams. Check out the AXON repository here: https://github.com/atopwebtechnologies/axon https://github.com/atopwebtechn...

This video features a step-by-step walkthrough of a Google code lab on building distributed multi-agent AI systems, led by Perete Harrison (DevOps Software AI Engineer) and Koye Adeyombo (Front-end Development Engineer) from Atop Web Technologies. Video Overview: Building Multi-Agent AI Microservices [RAW] In this comprehensive session, the engineers demonstrate how to go beyond simple chatbots to create a sophisticated, distributed system using the Agent Development Kit (ADK) and Google’s Gemini models. As this is a raw, unedited walkthrough, viewers will see the engineers encounter and troubleshoot real-world issues, such as credential and project resolution errors, in real-time. Perete Harrison explains the underlying concepts as they navigate the code lab, which was originally created by Amit Maharaj. They build a course creation system where specialized agents—a Researcher, a Judge, and a Content Builder—coordinate through an Orchestrator to produce high-quality educational conten...

  No doubt we have moved on from generation to verification . I mean, for the past few years, the amount of code I write with my own hands has dropped drastically. In some cases, almost to zero. Most times now, I am verifying output, reviewing generated code, checking if it actually solves the problem, catching hallucinations, and maintaining what comes out of the AI. That is a big shift. Years back, loose prototyping, implicit shortcuts, clever ways of doing things, all of that made sense. The developer was the one writing the code, so if it made sense in your head and saved you some time, fine. But now, when a lot of the code is generated for you, the question becomes different. Can I easily understand what this thing is doing? Can I verify it? Can I quickly spot when something is wrong? This is one reason I keep liking Go more. Of course, Go is my favorite programming language, so I may be biased. But seriously, Go code written by a senior engineer, a junior engineer, or generat...