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🎓 Agentic Marketing Intelligence System

🧠 AgentDB Browser introduces a new class of in-browser AI systems that think, learn, and adapt without relying on cloud infrastructure. Built on AgentDB v1.3.9, it runs entirely inside the browser using WebAssembly AgentDB, combining local reasoning, vector memory, and causal inference into a single self-contained engine.

An intelligent marketing optimization system that uses AgentDB's ReasoningBank with SAFLA (Self-Adaptive Feedback Loop Architecture) to automatically optimize Meta Ads campaigns. It learns from past performance, discovers causal patterns, and reallocates budgets to maximize ROAS (Return on Ad Spend).

This demo showcases how intelligence can operate at the edge, learning from data directly on the client side, without APIs or external dependencies. The system uses ReasoningBank SAFLA (Self-Adaptive Feedback Loop Architecture) to observe outcomes, detect cause-effect relationships, and refine strategy automatically. Every decision is stored as a Refl

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

Reinforcement Learning for Language Models

Yoav Goldberg, April 2023.

Why RL?

With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much

Workshop Guide - Building Apps on FireFly

Welcome! We're glad you're here! This is the guide that we will be going through during the workshop.

Before the workshop

IMPORTANT: Please make sure you have installed the software listed in this section before the workshop so that we can hit the ground running when the workshop starts.

	# train_grpo.py
	#
	# See https://github.com/willccbb/verifiers for ongoing developments
	#
	"""
	citation:

	@misc{brown2025grpodemo,
	title={Granular Format Rewards for Eliciting Mathematical Reasoning Capabilities in Small Language Models},
	author={Brown, William},