# Large Language Models Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/large-language-models
> Markdown URL: https://aitinkerers.org/technologies/large-language-models.md
> Technology record last updated: 2026-02-23T03:49:34Z
> Generated: 2026-09-23T02:32:39Z

Deep learning models (Transformer architecture) with billions of parameters, pre-trained on massive text corpora to execute complex natural language processing (NLP) and generative AI tasks.

Large Language Models (LLMs) are the core engine of generative AI: massive, Transformer-based neural networks (like GPT-4, LLaMA) trained on petabytes of text data. These models, often featuring billions of parameters (e.g., GPT-3 has 175 billion), function as sophisticated statistical prediction machines. They excel at a wide range of tasks: summarization, translation, code generation, and conversational AI (ChatGPT). While powerful, their output requires careful validation: they are prone to 'hallucination' (generating false information) and can reflect biases present in their training data.

- Official technology site: https://www.ibm.com/topics/large-language-models
- Public AI Tinkerers demos and talks: 8
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Teaching an LLM to Play Riftbound](https://ottawa.aitinkerers.org/talks/rsvp_CPcwK2QKRgg)

Samuel demonstrates a framework he built to teach AI agents to play Riftbound, the League of Legends-based trading card game. The system translates English card text and the current board state into legal actions, uses Gemma to choose moves in player-versus-bot or bot-versus-bot simulations, and is designed to generate training experience so a faster machine-learning policy can eventually select optimal plays without relying on slower LLM inference at the final decision stage.

- Event context: AI Tinkerers Ottawa Meetup - June 20th, 2026 — 2026-06-20 — Ottawa
- Public talk page: https://ottawa.aitinkerers.org/talks/rsvp_CPcwK2QKRgg

### [Stop Cloud Waste: AI Checks Costs Before Deploy in AWS](https://raleigh.aitinkerers.org/talks/rsvp_MX33E7LzJoM)

Ever had your IT team spin up cloud resources that cost way more than expected? I've developed an AI-powered assistant that reviews cloud infrastructure plans before deployment - like having a financial advisor check your shopping cart before checkout. Instead of finding out you spent $10,000 after the fact, teams get instant feedback: "This database is oversized for a dev environment - similar projects in your company use something 5x cheaper" or "You forgot the required cost tags that finance needs for billing." In this 5-minute demo, I'll show: Upload a cloud infrastructure plan (Terraform file) AI analyzes it against your company's cost policies written in plain English Get a breakdown: estimated monthly cost, policy violations, and smart suggestions See how it learns from your organization's past projects to give better recommendations The magic? It understands context - not just "this violates rule 247" but "this looks expensive for what you're building, here's what similar teams did."

- Event context: AI Tinkerers Raleigh Meetup — February 11, 2026 — 2026-02-11 — Raleigh
- Public talk page: https://raleigh.aitinkerers.org/talks/rsvp_MX33E7LzJoM

### [Personalized Product Discovery with Agentic AI: Beyond Traditional Recommendation Engines](https://singapore.aitinkerers.org/talks/rsvp_TYi5dO1vY58)

E-commerce platforms have long used recommendation engines to boost sales. Yet, these systems often fall short when it comes to true personalization. This talk introduces how Agentic AI can revolutionize recommendations for mobiles, gadgets, accessories, and insurance plans—shifting from static algorithms to adaptive, goal-oriented agents. We’ll discuss how these agents combine customer intent, multi-modal data, and conversational interactions to deliver next-generation recommendations that drive engagement, increase trust, and create measurable business impact.

- Event context: AIT Singapore - AI Agents Showcase - 7th Oct 2025 — 2025-10-07 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_TYi5dO1vY58

### [End-to-end AI Autograding](https://singapore.aitinkerers.org/talks/rsvp_cecnUyJmsIc)

Many AI autograding tools don't work as expected because Large Language Models are probabilistic. If these tools are not good at grading at this point in time, how can they still be a useful tool for educators. Built by a teacher for teachers, this tool shows the end-to-end pipeline of how an autograder can be built and deployed in the educational setting.

- Event context: AI Tinkerers Singapore: May Meetup - May 21st, 2025 — 2025-05-21 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_cecnUyJmsIc

### [Xelerit - AI Copilot for industrial robotics](https://zurich.aitinkerers.org/talks/rsvp_a35OOTyQzVs)

I will go through how our software works, which mirror the complete work of a robotics engineer, making it much faster. Our mvp has: • robot code generation (in the native robot-brand language) • copilot chat (for easy navigation of robot docs) • code translator between robot languages • I/O automatic configuration from PLC to robot. • Simulation

- Event context: AI Tinkerers Zurich - February 6 — 2025-02-06 — Zürich
- Public talk page: https://zurich.aitinkerers.org/talks/rsvp_a35OOTyQzVs

### [Cognitive Architectures -- A principled way to build agents](https://singapore.aitinkerers.org/talks/rsvp_9GgmNHjgPsk)

In the quest to build artificial intelligence, could understanding natural intelligence help? Cognitive architectures is the field of understanding and implementing the various building blocks of intelligence in cognitive science, offering a principled way to build agents. In this demo, I'll show some basic blocks that I've built and assembled into a coding agent.

- Event context: AI Tinkerers Singapore: 4th Meetup - January 10th, 2025 — 2025-01-10 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_9GgmNHjgPsk

### [Using LLMs for storytelling](https://singapore.aitinkerers.org/talks/rsvp_IItm85bd8Es)

Generate a story with different branches using a LLM generated DAG

- Event context: AI Tinkerers Singapore: 3rd Meetup - November 19th, 2024 — 2024-11-19 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_IItm85bd8Es

### [Milton - AI agent for economic data analysis](https://singapore.aitinkerers.org/talks/rsvp_Z0VAQIJHtw0)

Every single step of writing an economic data report can be automated using LLMs. I show a demo that does 1. Searching for the right datasets, self-critique and reflection 2. Automatic code-writing with self-debugging 3. Graphing according to the user needs 4. Packaging all of these into a report that rivals the quality of research made on Wall St

- Event context: AI Tinkerers Singapore: Inaugural Meetup - August 2nd, 2024 — 2024-08-02 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_Z0VAQIJHtw0

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