./c² cagataycali

Quick Start Implementation Guide: LLMs-from-Scratch Infrastructure Patterns

Overview

Practical implementation guide for adopting the sophisticated infrastructure patterns discovered in the LLMs-from-scratch repository. Focus on actionable steps and real-world implementation.

1. Multi-Environment Setup (Choose Your Path)

Option A: UV (Recommended for Speed)

# Install UV (ultra-fast package manager)

LLMs-from-Scratch: Package Architecture and Deployment Patterns

Overview

This document analyzes the sophisticated package architecture, deployment patterns, and production-ready implementation strategies demonstrated in the LLMs-from-scratch repository.

Package Structure Analysis

1. Modular Chapter-Based Organization

Directory Structure:

LLMs-from-Scratch: Development Environment Setup Guide

Overview

The LLMs-from-scratch repository demonstrates sophisticated multi-environment setup patterns for machine learning development, supporting multiple package managers and deployment scenarios.

Multi-Package Manager Strategy

1. Core Dependency Management

pyproject.toml - Modern Python Packaging

AWS Bedrock Usage Analysis Summary

Period: June 7 - September 5, 2025 (90 days) Region: us-west-2

🎯 Key Findings

Total Invocations: 58,995
Total Tokens Processed: 2.155B input + 31.4M output
Cache Hit Rate: 48.2% (saving ~$3,072)

ShipItFast: Key Technical Findings

🏗️ What ShipItFast Actually Is

NOT: Another framework, platform, or SaaS product IS: A curation ecosystem solving technology choice paralysis

The Problem They Solve

"Awesome" lists with 500+ dead projects from 2018
Choice paralysis in technology selection

S3 Memory Tool - Semantic Memory with Amazon S3 Vectors

A comprehensive semantic memory tool that leverages Amazon S3 Vectors for intelligent content storage and retrieval. Store any text content and find it later using natural language queries with vector similarity search.

🚀 Key Features

🧠 Semantic Search: Find content using natural language queries, not exact keywords
📚 Full Content Preservation: Stores complete content without truncation or data loss
⚡ Fast Vector Search: Powered by Amazon S3 Vectors native vector database capabilities
🎛️ Flexible Display Control: Configurable content limits and preview modes for optimal UX

Flipper Zero JavaScript Cheat Sheet

General Structure

Path and File Information:

print("Script path:", __filepath);
print("Script directory:", __dirpath);

GPIO Usage

Install

[wget ... or download](https://gist.github.com/acaa476865821b02813b8a8e88e59c13.git)
chmod +x run-local-multimodal-llm-openai-compatible.sh
./run-local-multimodal-llm-openai-compatible.sh

	import os
	import base64

	from strands import Agent
	from strands.models.ollama import OllamaModel
	from strands.telemetry import StrandsTelemetry

	from strands_tools import shell, editor

	os.environ["STRANDS_TOOL_CONSOLE_MODE"] = "enabled"

	"""GitHub GraphQL API integration tool for Strands Agents.

	This module provides a comprehensive interface to GitHub's v4 GraphQL API,
	allowing you to execute any GitHub GraphQL query or mutation directly from your Strands Agent.
	The tool handles authentication, parameter validation, response formatting,
	and provides user-friendly error messages with schema recommendations.

	Key Features:

	1. Universal GitHub GraphQL Access: