The ONPE Project: A Case Study in the Most Disruptive Technological Shift of Our Lifetime

Technology

Introduction

The ONPE Project, developed by me, began with a clear purpose. I wanted to make electoral processes easier to understand for citizens. Elections are often communicated through complex terminology and institutional language that most people cannot easily interpret. The ONPE Project exists to bring clarity, transparency and accessibility to information that should be understandable to everyone.

You can explore the live site here: https://onpe.ozamora.com

The first working version of the ONPE Project was built in two hours. After more than thirty years in technology, I have never seen acceleration like this. The ONPE Project is not just a tool. It is a demonstration of how AI has rewritten the rules of building.

Focus of the Project: Peru Presidential Election 2026 First Round

The ONPE Project is specifically designed to help citizens understand the Peru 2026 Presidential Election First Round. The goal is to provide a clear and accessible explanation of how ONPE publishes preliminary and official data and how the reporting process evolves over time.

This includes:

  • How actas are processed and counted
  • Why percentages shift as more actas arrive
  • How to interpret processed versus contabilizadas
  • How data flows from local mesas to national aggregation
  • How to follow the progression of the first round in real time

The intention is to help citizens navigate the complexity of the first round of Peru’s 2026 election using open data and transparent methods. Users should always confirm election information with official ONPE sources.

Why I Built the ONPE Project

Transparency for Citizens

Citizens deserve to understand how results are processed, why numbers change and what each stage means. The ONPE Project was created to make this information clear and trustworthy. It is not political. It is civic. It is about clarity and public understanding.

Education Through Simplicity

Election systems are complex. AI allows us to translate that complexity into human readable explanations. This is not simplification. It is accessibility. It is the difference between information that exists and information that can be understood.

Demonstrating the Civic Potential of AI

The ONPE Project is also a statement. AI can strengthen democratic literacy by making public information easier to navigate. It shows how individuals can now build tools that previously required teams, budgets and long development cycles.

The Disruption: Built in Two Hours

Thirty Years in Tech and Nothing Like This

I have lived through the PC era, the internet boom, mobile, cloud, DevOps, SaaS and machine learning. None of those waves compare to what is happening now.

The ONPE Project was built using:

  • Agentic AI workflows
  • Autonomous code generation
  • Prompt driven development
  • AI assisted deployment

This was not coding faster. This was software assembling itself based on intent. I described the outcome. AI generated the architecture, the content, the layout and the deployment pipeline. For the first time in my career, the bottleneck is no longer skill. It is imagination.

What Makes This Disruption Different

The Build Cycle Has Collapsed

Idea to prototype to deployment now happens in a single conversation. This is not incremental improvement. This is a new physics of creation.

AI Native Development

The ONPE Project is not AI assisted. It is AI native. It was conceived, structured and iterated through agentic reasoning loops. This is a new category of software development.

Democratization of Civic Technology

You no longer need a development team, a design team, a content team or a DevOps pipeline. If you can articulate the purpose, AI can generate the product. This opens the door for transparency dashboards, public data explorers and educational explainers built by individuals.

A New Standard for Public Information

Citizens expect clarity. Institutions rarely deliver it. AI can bridge that gap. The ONPE Project is a small example of a much larger shift. Public information can now be made accessible, fast and at scale.

Open Source and Community Impact

The ONPE Scraper Source Code

The data extraction and normalization engine behind the project is fully open source.
Repository:
https://github.com/oscarzamora/onpeescraper

This scraper demonstrates how AI assisted development can accelerate data engineering tasks that traditionally required significant manual effort.

The Simple Analytics Iteration

The ONPE Project also builds on earlier work from the Simple Analytics iteration, which explored transparent election data visualization.
Repository:
https://github.com/oscarzamora/peruvoto2026

These iterations show a clear evolution. What once required days of manual coding and analysis now happens in minutes through AI driven workflows.

Why This Is the Most Transformational Shift in My Career

After thirty years in technology, I can say this with confidence. We have never seen acceleration like this. Not during the dot com boom. Not during the mobile revolution. Not during the rise of cloud computing. Not during the early AI wave.

AI is now a co creator. It writes. It designs. It codes. It deploys. It explains. It iterates. It reasons. And it does all of this at the speed of thought.

The ONPE Project is not remarkable because it exists. It is remarkable because of how it came into existence.

Key Takeaways

  • The ONPE Project focuses on the Peru 2026 Presidential Election First Round
  • The first version was created in two hours
  • AI has collapsed the distance between idea and execution
  • Agentic workflows are replacing traditional development
  • Civic tech is being democratized
  • This is the most disruptive shift I have seen in more than thirty years in technology

Technical Deep Dive

Architecture Generation Through Agentic Reasoning

The ONPE Project was generated using agentic reasoning loops. These loops break down a request into smaller tasks, generate code, validate it, correct it and assemble it into a working structure. This replaces the traditional design to code to test to deploy cycle.

Autonomous Code Generation

The system produced the routing, layout, content blocks and structure without manual intervention. This is not template based generation. It is dynamic synthesis. The AI evaluates intent and produces code that matches the described outcome.

Prompt Driven Development

Instead of writing functions, classes or components, I described the behavior and purpose. The AI translated those descriptions into working code. This is a shift from imperative programming to declarative intent.

AI Assisted Deployment

Deployment was handled through automated flows that abstract away infrastructure. There was no manual configuration. No CI or CD setup. No environment provisioning. The system generated a deployable artifact and published it.

Iteration at the Speed of Thought

Changes were conversational. I requested modifications. The system regenerated components. This creates a continuous loop of idea to implementation without friction.

The Future of AI Native Systems

The ONPE Project is an example of what AI native systems look like. They are built through conversation. They are deployed through automation. They evolve through reasoning. They represent a new era where individuals can build what once required entire teams.