# LLVM IR Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/llvm-ir
> Markdown URL: https://aitinkerers.org/technologies/llvm-ir.md
> Technology record last updated: 2026-02-24T06:28:29Z
> Generated: 2026-09-23T07:37:00Z

LLVM IR is a strictly typed, RISC-like intermediate language that serves as the universal bridge between high-level source code and machine-specific binary instructions.

This Static Single Assignment (SSA) based representation acts as the core interface for the LLVM compiler infrastructure. It utilizes a language-independent instruction set (including operations like `phi`, `getelementptr`, and `icmp`) to facilitate sophisticated middle-end optimizations. By standardizing the communication between front-ends like Clang or Rust and back-ends for x86, ARM, or RISC-V, LLVM IR ensures that a single optimization pass benefits multiple architectures. The format exists in three interchangeable forms: an in-memory C++ object model, a compact bitcode file (.bc), and a human-readable assembly text (.ll).

- Official technology site: https://llvm.org/docs/LangRef.html
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Building a Persistent Memory & Stateful Second Brain AI Agent](https://dhaka.aitinkerers.org/talks/rsvp_kSMaLnvvGGg)

Demonstrating context engineering in practice through Slatekore - an open-source starter kit that gives Gemini CLI persistent memory using Obsidian as the storage layer. 1. State Management Without Infrastructure How to use the file system as persistent state - your Obsidian vault becomes both the agent's memory and its knowledge base. No databases, no vector stores, no MLOps. 2. Context Engineering for Agent Behavior Crafting system prompts (GEMINI.md) and workflow files that define agent capabilities, constraints, and multi-step reasoning patterns. The prompt is the agent's configuration. 3. Tool Use Through Natural Interfaces Connecting the agent to real actions: creating files, managing tasks, building knowledge graphs - all through natural language commands backed by structured templates. Watch a stateful agent capture research, recall context from previous sessions, update its knowledge graph, and execute project workflows - without any model training or fine-tuning.

- Event context: AI Tinkerers Dhaka 2nd Meetup: Dhaka Builds with AI! — 2026-02-07 — Dhaka
- Public talk page: https://dhaka.aitinkerers.org/talks/rsvp_kSMaLnvvGGg

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