Exploring your use Cases with Goblin | Nashville .

Members-Only

Recent Talks & Demos are for members only

Exclusive feed

You must be an AI Tinkerers active member to view these talks and demos.

October 28, 2025 · Nashville

Goblin: AI Workflow Comparison

We'll demonstrate Goblin's workflow engine for side‑by‑by comparison of AI models, measuring their efficacy and effectiveness on real data analytics tasks.

Video
Overview
Tech stack
  • React
    React is an open-source JavaScript library for building dynamic user interfaces (UIs).
    React is a component-based JavaScript library, developed by Meta (Facebook), engineered for building fast, declarative UIs. It mandates a one-way data flow and utilizes a Virtual DOM mechanism to ensure efficient, predictable updates to the user interface. Developers construct complex UIs by composing small, encapsulated components; this architecture promotes code reusability and simplifies state management across large applications. The library employs JSX (a syntax extension) to integrate HTML-like markup directly within JavaScript logic, supporting development for both web (React DOM) and native mobile platforms (React Native).
  • Go
    Go is Google's open-source, compiled, and statically-typed language built for high-performance, scalable systems (microservices, cloud infrastructure) via simple, efficient concurrency (goroutines).
    Go (often called Golang) is a compiled, open-source language designed at Google by Robert Griesemer, Rob Pike, and Ken Thompson to solve modern software challenges: slow build times and complex dependencies. It is statically-typed and syntactically clean, drawing inspiration from C but adding key features like automatic garbage collection and a powerful, built-in concurrency model (goroutines and channels). This design delivers fast compilation and runtime efficiency, making it the premier choice for building scalable, reliable systems; major projects like Docker and Kubernetes rely on Go for their core infrastructure.
  • RabbitMQ
    RabbitMQ is the open-source message broker: it accepts messages from publishers, routes them via exchanges, and delivers them reliably to consumers using protocols like AMQP and MQTT.
    RabbitMQ is your enterprise-grade, open-source message broker, built on Erlang for high concurrency and reliability. It acts as a robust intermediary, decoupling your service components by accepting messages from producers and routing them to designated queues through 'exchanges' (a key concept). This architecture is critical for modern distributed systems, specifically microservices, where asynchronous communication is non-negotiable. It handles high-volume task queues, like image processing or email distribution, ensuring message delivery with features like persistence and acknowledgments. We support multiple protocols: AMQP 0-9-1 is the foundation, but it extends to MQTT and STOMP via its plug-in architecture.
  • Ollama
    Deploy and run open-source Large Language Models (LLMs) like Llama 3 and Mistral locally on your machine: achieve private, cost-effective AI via a simple command-line interface.
    Ollama is the essential tool for running LLMs locally: consider it the Docker for AI models. It packages complex models and dependencies into a single, easy-to-use application for macOS, Linux, and Windows systems. You get immediate access to models like Gemma 2 and DeepSeek-R1 via a straightforward CLI or REST API. This local-first approach guarantees data privacy and security, eliminating cloud dependency and high API costs. Ollama also optimizes performance on consumer hardware using techniques like quantization, ensuring efficient execution even on standard desktops.
  • LLMs
    Large Language Models (LLMs) are Transformer-architecture deep learning systems (e.g., GPT-4, Llama 3) trained on massive text corpora to generate, summarize, and reason over human language at scale.
    LLMs are advanced deep learning models, specifically Generative Pre-trained Transformers (GPTs), designed to process and generate human-like text. They are trained on vast, multi-trillion-token datasets, giving them billions of parameters to learn complex linguistic patterns (syntax, semantics). This scale enables emergent capabilities: few-shot learning, code generation, and complex reasoning. Key examples include OpenAI's GPT-4, Google's Gemini, and Meta's Llama 3. LLMs power applications from conversational AI (ChatGPT) to automated content creation, fundamentally shifting how machines handle unstructured language.

Related projects