A Deep Dive into Our Teams ‘Show & Tell’ Virtual Session 

Artificial intelligence is the engine driving change in every corner of technology and business. This past August, the EverOps team ran an AI-first hackathon-style program throughout the month, setting aside an hour or two each week to learn, build, and test AI-first workflows, including vibe coding, prompt guardrails, and automation.

The results revealed far more than a sum of code and prompts. What emerged was a glimpse into the future of work, where technical ingenuity and team creativity meet, and where the boundaries of what can be accomplished in a few focused hours expand before our eyes.

This article outlines insights directly from the EverOps AI Show & Tell, a wide-ranging and candid virtual session that brought together engineers, product thinkers, and AI enthusiasts alike to share their hackathon progress. The narrative that follows is rooted in lived experience: the challenges faced, the tools explored, and the lessons learned that continue to echo across projects well beyond the hackathon itself. In a world increasingly reliant on intelligent automation, these insights matter now and will matter even more in the future.

Building With AI as Colleague & Catalyst

A recurring theme was treating AI like a collaborator rather than a passive tool. The hackathon-style program was designed to immerse the team in AI-first work as a practical discipline. The ultimate goal of the program was to create a minimal viable product (MVP) using vibe coding, a method that uses natural-language prompts to instruct the AI.

This approach flattened traditional roles. Team members from a wide range of technical backgrounds contributed meaningfully, dissolving the boundary between “coder” and “collaborator.”

Early on, the team focused on developing guardrails for vibe coding, best practices encoded directly into prompt-based rules. These included safeguards like preventing direct deployment from development environments and enforcing strict branching policies. 

The outcome was the ‘Fox Rule Compiler,’ a repository and interface for modular, prompt-based policies that produce token-optimized output suitable for pasting into an AI assistant’s rules/guardrails. The prototype was valuable as a learning vehicle. Since then, the team’s approach has continued to evolve, which is exactly what the hackathon was designed to accelerate.

The Fox Rule Compiler also embodied the hackathon’s central insight:

Treating AI as a collaborator makes building more modular, more creative, and much faster.

Tool selection further reflected this mindset and included: 

• n8n, the open-source automation platform, served as the backbone for blending deterministic logic with AI-driven inference. It was the most accessible way (at the time) to string deterministic logic together with AI-native steps, even with some limitations.
  
  
• For rapid iteration, EverOps expert Daniel found ‘Cloud Code’ on the command line especially effective for testing and experimentation.

Cross-pollination also played a critical role. Office hours led by experienced AI practitioners became crucibles for learning and problem-solving, transforming isolated experiments into a shared, accelerating journey.

As one participant put it, “The collaboration and the cross-pollination of ideas here were just amazing. We got to try and test different AI's, different solutions here and there.”

That sentiment captured the spirit of the EverOps hackathon: open, experimental, and deeply collaborative.

Prompt Mastery, Real-Time Context & the Joy of Creation

Every project built during the hackathon rested on the art and science of prompt engineering. The team quickly learned a simple truth that better prompts produce better outcomes. Even more powerful was the realization that AI itself could be prompted to improve prompts, creating a recursive loop of continuous refinement.

The Fox Rule Compiler made it easy to describe a rule in plain English, generate a richer version with examples, and then include it in a reusable guardrail set. 

Context management emerged as both a challenge and an opportunity. The team adopted MCP servers, modular context providers that inject real-time, relevant data into AI workflows. They framed MCP servers as a powerful new way to inject live context into AI workflows, almost like an “API for context,” while still grounded in deterministic back-end calls. They pointed to GitHub’s MCP as a concrete example, enabling real-time questions about pull requests, commits, and releases. This allowed workflows to span multiple systems while staying grounded in current organizational data.

This integration of application logic and live context signals a new direction for AI-powered engineering, including faster answers, better decisions, and systems that stay current by design.

Emotionally, the hackathon was electric. Vibe coding proved not just efficient, but deeply engaging. Another participant stated: “I wish my day-to-day was vibe coding all day because it’s just super entertaining and takes a lot of creativity.”

Creation became a collective performance, ideas bounced between humans and AI in real time, refined collaboratively, and brought to life with speed and enthusiasm. The AI didn’t replace human ingenuity, but amplified it.

Lessons, Next Steps & the Path Forward

The nearly month-long AI hackathon program left the team with both practical skills and renewed conviction. 

Participants walked away with hands-on fluency in:

• Prompt engineering and recursive prompt refinement
• Context management with MCP servers
• Tokens, context limits, and cost tradeoffs
• Orchestrating deterministic and creative workflows with n8n
• Adapting outputs for any downstream format, JSON, applications, or business rules

Most importantly, the team internalized a lasting shift, affirming that an AI-first mindset is not just a new trend, it’s an entirely new operating model.

Following the Show and Tell session, office hours still continue as an open invitation to experiment, ask questions, and quickly launch MVPs. As the spirit of the hackathon lives on, it's clear that this is a contagious, accessible, and growing part of this industry. 

As one participant shared in closing: “A lot of thanks to the company for putting this together, allowing us to have the time to do it, and everybody that participated, because you made it fun, and it was awesome to be a part of.”

This is what technical progress looks like when paired with culture.

Pushing the Boundaries of Ops

At EverOps, we turn cutting-edge innovation into real-world results for our clients. Our AI-driven hackathon-style programs are just one example of how we continually push the boundaries of ‘Ops,’ automation, and intelligent infrastructure. If your organization is ready to accelerate delivery, streamline operations, and harness the power of AI and automation in your development workflows, connect with our team today.

We help teams modernize todays most widely used ‘Ops’ practices, improve reliability, and responsibly explore AI-enabled automation. Whether you want to explore AI-powered automation, improve your deployment pipelines, or ensure robust, scalable infrastructure, EverOps brings expertise, creativity, and strategies to the table.

Let’s build what’s next, together! Schedule a consultation and discover how our expert solutions can unlock your team’s full potential.

Frequently Asked Questions

What is vibe coding in simple terms, and how does it change development?

Vibe coding is a way of building software by describing what you want in plain English and letting an AI system generate and refine the code. This August, the team used vibe coding to rapidly prototype ideas, test workflows, and explore how quickly a usable MVP could be created with limited time. The results were closely showcased throughout this post. 

How is vibe coding different from traditional software development?

Traditional development relies heavily on manual coding, deep familiarity with frameworks, and long build cycles. Vibe coding shifts the focus to intent and iteration, enabling teams to experiment faster by refining prompts and outputs rather than writing every line of code by hand.

Why was prompt quality such a big focus during the program?

The team saw firsthand that clearer, more structured prompts consistently produced better results. They also learned that AI can help improve prompts by expanding simple instructions into richer, example-driven guidance, creating a feedback loop of continuous refinement.

What are MCP servers, and why do they matter?

MCP servers provide AI systems with live, relevant context from external tools and services. Instead of relying only on static information, they allow workflows to reflect the current state, such as recent pull requests or releases, making AI-assisted decisions more accurate and useful.

What’s the biggest takeaway from August’s Hackathon event?

The biggest takeaway wasn’t a single tool or prototype, but a shift in mindset. Treating AI as a collaborative partner, combined with thoughtful prompts and well-managed context, opens new ways to build, test, and iterate faster than traditional approaches allow.

How does this program reflect EverOps’ approach to operations and automation?

The program reinforced EverOps’ belief that successful ‘Ops’ modernization blends strong engineering fundamentals with experimentation. AI August focused on combining deterministic systems, such as automation and infrastructure logic, with AI-driven flexibility, mirroring how EverOps approaches real-world systems design.

What should clients expect if they explore AI-enabled workflows with EverOps?

Clients can expect a practical, grounded approach. EverOps helps teams assess where AI adds real value, define clear guardrails, and integrate AI capabilities into existing Ops practices in ways that are measurable, maintainable, and aligned with business goals.