🔌Agent Training

The jessexbt training architecture is built to operationalize Jesse Pollak’s knowledge, judgment, and public presence into an intelligent, always-on agent. It is designed to scale support to thousands of builders with practical advice and funding intelligence. The system combines fine-tuned language modeling with Retrieval-Augmented Generation (RAG), active learning loops, and real-time integrations.

🎯 Training Goals

• Capture Jesse’s Persona: Reflect Jesse’s tone, decision-making, and domain fluency.

• Stay Fresh & Real-Time: Sync continuously with builder queries and ecosystem updates.

• Learn from Feedback: Incorporate Jesse’s feedback and user signals in daily model updates.

• Support at Scale: Maintain high-quality interactions across Farcaster, X, Telegram.

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🧠 Training Pipeline

The training pipeline for jessexbt consists of four interconnected components: Pre-Training, Fine-Tuning, Retrieval-Augmented Generation (RAG), and Feedback Loop, as illustrated in the diagram below.

Pre-Training: Building Jesse’s Persona

• Goal: Establish baseline persona and communication style.

• Sources:

  • 164+ YouTube videos & podcasts

  • Historical posts on X

  • Farcaster threads and replies

• Curation Process:

  • Transcription → Cleaning → Synthetic Sample Generation (via Gemini)

• Output: Base model aligned with Jesse's tone and expertise.

Fine-Tuning: Specialization & Personality Alignment

• Model: Gemini 2.5

• Data: Curated public content + dashboard personalization (bio, tone examples, answer style)

• Focus Areas:

  • Align tone with Jesse’s communication

  • Minimize hallucination or generic output

  • Embed optimism, builder-first mindset

Retrieval-Augmented Generation (RAG): Real-Time Knowledge

RAG System: Real-Time Contextual Intelligence

• Vector DB (Pinecone):

  • Structured by namespace: jessexbt, builders, protocols

• Ingested Sources:

  • base.org (static)

  • Farcaster + Twitter posts (real-time)

  • PDFs, notes, URLs, GitHub (via Puppeteer w/ refresh)

• Latency Optimization:

  • Caching, response reranking, fast retrieval

• Moderation:

  • Filters for PII, toxicity, and spam

• Pending (◯): Intent recognition, data governance, sentiment engine, Knowledge Graph enrichment

Feedback Loop: Continuous Improvement

You can like and dislike, rate and prompt an specific feedback to the repliesof the agent

Feedback Loop: Active Learning + Evaluation

• Human-in-the-loop: Jesse reviews and scores responses in the Agent Dashboard

• Pipeline:

  • Good responses → Reinforced in training

  • Bad responses → Flagged and retrained

• Live Model Updates: Responses are iteratively polished and personalized via ZEP layer:

  • Dialogue tracking, intent classification, profile-based refinement

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📋 System Summary Table

Component	Key Features
Pre-Training	Video/audio transcription, X/Farcaster threads, synthetic tuning
Fine-Tuning	Gemini 2.5 + personality conditioning + example-driven tone control
RAG System	Pinecone + Puppeteer, real-time ingest from base.org, GitHub, Telegram
Feedback Loop	Human-in-the-loop scoring, retraining queue, ZEP polishing, active learning

🔗 Technical Integration Summary

• Model: Gemini 2.5, refined on Jesse’s content

• Data Infra: Pinecone DB with namespaces, cache layers

• Scraping: Puppeteer with both automatic + manual update modes

• ZEP Layer: Handles response reranking, state tracking, and personalization

• Moderation: Live filters for toxicity, PII, abuse

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🌌 Why This Training Pipeline Works

This stack turns Jesse’s public thinking and builder feedback loops into a scalable, high-precision copilot. It combines:

• Human tone + machine memory

• Builder context + Jesse’s judgment

• Feedback signals + RAG augmentation

The result is a system that improves daily, scales instantly, and supports builders with answers that matter.