# AVX2 Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/avx2
> Markdown URL: https://aitinkerers.org/technologies/avx2.md
> Technology record last updated: 2026-02-24T06:28:56Z
> Generated: 2026-08-24T05:49:37Z

A 256-bit SIMD instruction set expansion that doubles integer throughput and introduces FMA3 for accelerated vector processing.

AVX2 (Advanced Vector Extensions 2) launched with Intel's Haswell architecture in 2013 to expand 128-bit integer SIMD instructions to full 256-bit widths. It adds critical features: Fused Multiply-Add (FMA3) for high-precision math and gather support for fetching non-adjacent data from memory. This instruction set is the standard for performance-critical software: 4K video encoding (x265), scientific simulations, and cryptographic hashing. By handling 32 bytes of data per instruction, AVX2 provides a significant efficiency ceiling over legacy SSE4.2 implementations.

- Official technology site: https://www.intel.com/content/www/us/en/developer/articles/technical/intel-advanced-vector-extensions-2-intel-avx2-instructions.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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