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Deploy Node.js on AWS EC2 with NGINX and SSL

Deploying a Node.js App on AWS EC2 with PM2, NGINX Reverse Proxy, and Let's Encrypt SSL

1. Update all packages

sudo apt-get update
sudo apt-get upgrade

NanoClaw — Personal Claude Assistant

A self-hosted, compounding-memory AI assistant running on a Raspberry Pi.

What Is This?

NanoClaw is a personal AI assistant built on Anthropic's Claude that runs entirely on a Raspberry Pi. It connects to messaging channels (WhatsApp, Telegram, Slack, Discord), processes voice and images, schedules recurring tasks, and — unlike a standard chatbot — accumulates knowledge over time through a structured memory system.

Code Is the Source of Truth

When answering questions about how software in the current workspace actually behaves (runtime, configuration, APIs, CLI, builds, tests, deployment, or infrastructure), treat the current repository artifacts as the primary source of truth.

Prioritize evidence roughly in this order:

Executable source and configuration
- Application source

Scripts

Bedtools Cheatsheet

General:

Tools	Description
flank	Create new intervals from the flanks of existing intervals.
slop	Adjust the size of intervals.
shift	Adjust the position of intervals.
subtract	Remove intervals based on overlaps b/w two files.

Building Resilient AI-Driven Document Modules

Jawad Ul Hadi, Backend Lead / Architect — AI-First Systems Design

I built a NestJS module that turns timesheets in six formats (PDF, DOCX, XLSX, XLS, PNG, JPG) into one fixed eight-column payroll export. "AI-first" meant weighing AI at every design decision, and then using it only where it pays for itself. Deterministic parsing does the work. An LLM is a bounded, redacted, budget-capped fallback that can never fail an upload. AI was also part of how the module was built: planning, implementation, and an AI code review that caught ten real bugs before merge.

The problem

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

Best Instant Indexing Tools Compared by Speed, Price & Real Results (2026)

Choosing the best instant indexing tool is not only about speed. Many tools claim to get pages discovered quickly, but website owners need to compare them based on actual usefulness, pricing, workflow, reliability, and SEO value.

In 2026, instant indexing tools are widely used by bloggers, agencies, affiliate marketers, publishers, and eCommerce websites that want faster URL discovery.

Best Instant Indexing Tools You Should Go For

Rocket Indexer – https://snipr.is/Cq8vwDP 👈🏻 Click Here
2minuteindex.com – 👈🏻 Click Here

	# takes a ktx2 file from roblox cache and converts it to a PNG
	# ktx2 info was taken from https://registry.khronos.org/KTX/specs/2.0/ktxspec.v2.html
	# i (troy) also dont write python so this is kind of shitty

	import struct
	import zlib
	from pathlib import Path

	import zstandard as zstd
	import texture2ddecoder

	"""
	The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
	This file is the complete algorithm.
	Everything else is just efficiency.

	@karpathy
	"""

	import os # os.path.exists
	import math # math.log, math.exp