🔌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.
🧠 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
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
📋 System Summary Table
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
🌌 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.
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