# x86_64 Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/x86-64
> Markdown URL: https://aitinkerers.org/technologies/x86-64.md
> Technology record last updated: 2026-05-18T00:47:27Z
> Generated: 2026-09-22T13:40:18Z

A 64-bit instruction set architecture (ISA) that expands x86 registers and memory addressing while maintaining full backward compatibility.

Originally launched by AMD as AMD64 in 2003, x86_64 serves as the standard 64-bit instruction set for modern desktop and server CPUs. It expands the legacy 32-bit x86 architecture by doubling the number of general-purpose registers to 16 and increasing their width to 64 bits (using prefixes like RAX and RDI). This evolution removes the 4 GB memory ceiling of 32-bit systems: it supports a theoretical limit of 16 exabytes of RAM. Most implementations utilize a 48-bit or 52-bit virtual address space to handle current enterprise workloads. The architecture remains the backbone of global computing by maintaining native compatibility with legacy 16-bit and 32-bit software through its versatile Long Mode execution state.

- Official technology site: https://en.wikipedia.org/wiki/X86-64
- Public AI Tinkerers demos and talks: 2
- Result page: 1 of 1

## Recent Public Talks and Demos

### [EmergentDB: How Evolutionary Algorithms Made My Vector Database 82x Faster](https://singapore.aitinkerers.org/talks/rsvp_hHLd9-vXn8A)

Vector databases power semantic search, RAG, and recommendation systems—but tuning them is a nightmare. Should HNSW use M=8 or M=32? What's the right ef_construction value? Most teams guess and pray. EmergentDB solves this through evolutionary computation. Instead of manual tuning, it uses the MAP-Elites algorithm to evolve the optimal index configuration for your specific workload. The system maintains a "quality-diversity grid" that explores the trade-off space between recall, latency, and memory—automatically selecting between HNSW, Flat, and IVF indices with evolved hyperparameters.

- Event context: AI Tinkerers - The Age of AI & Infrastructure (Singapore) — 2026-02-11 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_hHLd9-vXn8A

### [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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