Forseti461 is an AI agent that automatically moderates citizen contributions to participatory democracy platforms — approving only concrete, constructive, locally relevant ideas while rejecting personal attacks, spam, off-topic posts, or misinformation, and always explaining decisions with respectful, actionable feedback.

What if we could protect civic discourse at scale?

Goal: Create a systematic approach to test and improve our AI-powered charter validation system.

For the Encode Hackathon first submission, we focused on building the infrastructure to ensure Forseti 461 (our charter validation agent) catches all violations reliably. The key insight: you can't improve what you can't measure.

Here is the assessment of the catch-up call between @jnxmas and Victor regarding the Ò Capistaine project status and immediate priorities.

Summary of the Call​

@jnxmas and Victor discussed the immediate roadmap for the Opik/Commit to Change Hackathon MVP submission (deadline: ~1 day, 14 hours). Victor has successfully downloaded approximately 4,000 PDFs (including ~3,965 deliberation documents), though he noted some potential duplicates and that the download process was synchronous and could be optimized later. He has committed changes to a development branch but not yet merged them, preferring to use GitHub as a medium to exchange the code while keeping the large PDF dataset local (or shared via a specific sub-directory). The team agreed on a strategy for the Hackathon demo deployment. Instead of using Vercel, which complicates environment variable management for their specific security setup (ngrok, multiple API keys for Opik, Firecrawl, Gemini, etc.), @jnxmas will run the demo from his local machine using a secure, paid ngrok tunnel (ocapistaine.ngrok.app). This setup allows the jury to interact with the Streamlit UI (restricted to Ollama for the external demo) while the team can continue testing other models (Gemini) locally. The architecture involves Locki.io -> Vaettir Orchestration -> Local Machine (Ocapistaine Agent).

Context: Audierne 2026 Election Platform

This document outlines how we will leverage the Google Gemini ecosystem (AI Studio, Flash models, and Agentic workflows) to accelerate the development of the Locki project. By utilizing these tools, we aim to bridge the gap between human ideation and automated N8N workflows, specifically for the Commit to Change hackathon and the subsequent election period.

Strategy Update: Leveraging Gemini & Agent Workflows​

This document summarizes key insights from the recent team sync following Jean-Noël's attendance at the Google Gemini workshop. The focus is on accelerating the Locki / Ò Capistaine project by transitioning from manual human analysis to automated agent workflows.

Project Context Update (Mid-to-Late January 2026)​

Project: Locki / Ò Capistaine (audierne2026) - AI-powered civic transparency & participatory democracy platform for Audierne 2026 local elections (France) Core Mission: Build a neutral, source-based RAG chatbot to answer citizen questions about 4 municipal programs, automate contribution validation against charter rules, crawl/process municipal data (150+ links, 4,000+ PDFs), and showcase Opik integration for the "Commit to Change" Hackathon. Critical Timeline:

• Contributions deadline: ~January 31, 2026
• Hackathon prototype delivery: ~mid-February 2026 (4-week sprint)
• Election period: ~March 15-22, 2026

Project Overview & Vision​

• The core idea is building an AI-powered civic transparency platform focused on local democracy, citizen engagement, and real-world issues (starting with inspiration from India, but with potential global scalability).
• Goal: Create a standardized, bullshit-free alternative to Twitter/X discussions for civic topics — avoiding noise and enabling structured transparency for citizens, investors, and "democracy islands" (e.g., places like Audierne).
• It's tied to the "Commit to Change" AI Agents Hackathon (powered by Opik / Comet), running ~4 weeks starting mid-January 2026, with categories like Community Impact.

AI-Powered Civic Transparency for Local Democracy

My 2026 Resolution​

Sound familiar? Every election cycle, millions of citizens want to participate but face the same wall: scattered documents, administrative jargon, and no time to dig through years of municipal decisions.

This January, I stopped just wishing — and started building.

This training consolidates the operational and technical foundations needed to run and execute agent/prompt optimization in team settings (e.g., hackathons and internal workshops).

It includes :

• eval-driven optimization of LLM agent prompts using measurable metrics and iterative loops,
• including meta-prompting, genetic/evolutionary methods, hierarchical/reflective optimizers (HRPO), few-shot Bayesian selection, and parameter tuning.

This lecture, led by Abby Morgan, an AI Research Engineer, introduces AI evaluation as a systematic feedback loop for transitioning prototypes to production-ready systems. It outlines the four key components of a useful evaluation: a target capability, a test set, a scoring method, and decision rules. The session differentiates between general benchmarks and specific product evaluations, emphasizing the need for observability in agent evaluation. It demonstrates using OPIK, an open-source tool, to track, debug, and evaluate LLM agents through features like traces, spans, 'LM as a judge', and regression testing datasets.