Branched Conversations
tldr engage with LLMs in branched conversations
Discounted Cash Flow Learnings
A DCF for Meta in 2024. This was recommended to me as an exercise in learning the basics of investing and business. These are my (high level) notes of what I learned along the way.
Drone Swarm Algorithms
Inspired by videos of drones used in shows that coordinate to create images in the sky.
LLM Memory and Latency Profiling
This post covers profiling an LLM, something I’ve wanted to do for a while.
Pairwise Comparisons
A foray into pairwise comparisons where a user is asked to ask a series of questions comparing two options at a time to determine the ranking of a list of options.
Lessons Learned from using HuggingFace for LLM Inference in Google Colab
My original goal was to set up an llm quickly in a google colab python notebook (because I didn’t want to execute locally + I wanted access to an nvidia gpu quickly for free). I originally was looking at Ollama but its client/server architecture didn’t seem to elegantly nicely with google colab. Side note, see this great blog post for an understanding of Ollama’s architecture.
Simple Graph Rag Application
A description of a simple graph rag application.
What is GraphRAG
A summary of a collection of (mostly Neo4j) resources on GraphRag.
AGENTiGraph An Interactive Knowledge Graph Platform for LLM-based Chatbots utilizing Private Data
A summary of the paper (link in resources section).
Intro to Neo4j (Graph Databases)
A primer on Graph Databases with Neo4j.
Multi Layer Perceptrons (with code)
Transformers feed the output of self attention blocks into a feed forward layer. We will look at one such example, a Multilayer Perceptron (MLP).
How (Multi-headed) Self Attention Blocks Work
What are multi-headed self attention blocks and how do they fit into transformers.
Intro to Tokenizers
Notes on the tokenizer space, what they are, what problems they solve, some example code, and some areas to explore in the future.
May 2024 Summary of Resources
Summary of resources I read/watched in May. The focus of this month was to get familiar with terms and concepts underlying Generative AI.
Song Sensei - Use an LLM for Song Suggestions
Visit song-sensei.vercel.app to chat with a bot llm to find the music you want on Spotify
Building a PC
Documenting my journey buying the parts for and building a pc.
Web App To Download Saved Reddit Posts
tldr visit this link to download your saved reddit posts.
Facebook's C++ String Class in Folly
In this blog post we will take an in depth look at how Facebook’s C++ String from Folly works. Specifically we will examine how the string class adapts to different operations depending on the size of the string.
Computer Hardware and Architecture - A Bird’s Eye View
In this blog post we take a step lower from our previous blog posts on compilers/virtual machines and operating systems. We will string together how physics and hardware are built and used to execute our abstract thoughts and ideas illustrated in higher level languages.
An Introduction to Compilers
This blog post introduces the world of compilers with an example in Golang. The code comes from the book Writing An Compiler In Go by Thorsten Ball.
The Producer Consumer Problem in C++
We will go over a solution to the Producer Consumer problem in concurrency with multiple producers and consumers in a buffer of bounded size. The solution is written in C++ and uses mutexes and condition variables. This post is based off of the blog post here by Baptiste Wicht.
Operating Systems in a Nutshell
This blog post introduces Operating Systems and the pieces involved, specifically: processes and threads, drivers, file systems, and virtual memory.
An Introduction to Interpreters
This blog post introduces the world of interpreters with an example in Golang. The code comes from the book Writing An Interpreter In Go by Thorsten Ball.
C Source Code to x86 Binary
This blog post details how C source code is compiled into x86 binary.
Semi Supervised Learning for Time Series Classification
An investigation in applying pseudo labeling, a common technique in semi supervised learning where we have limited labeled data, to time series classification.
Pseudo Random Number Generators and Sampling
An exploration of pseudo random number generators and their applications in sampling.
Unsupervised Feature Learning via Non-Parametric Instance Discrimination
A summary of the paper [Zhirong et al., 2018] as well as a mention of a followup paper MoCo [He et al., 2020] that builds upon the first.
Balls and Bins Proof
A full walk through of the balls and bins proof, split up into 4 parts.
Spam Detection Microservice Using Snorkel
This post walks through the process of putting a spam detection model using Snorkel into a microservice using Flask, Docker, and AWS.
Defusing CMU's Bomb Lab using GDB
This post walks through CMU’s ‘bomb’ lab, which involves defusing a ‘bomb’ by finding the correct inputs to successive phases in a binary executable using GDB.
Walkthrough of CMU's Attack Lab
This post walks through CMU’s ‘Attack’ lab, which involves exploiting the stack space of vulnerable binaries.
SIR Model From Scratch in Python
This post explains the SIR model and includes a Python implementation that generates a graphic describing a population’s infectious status over time.
Clock Replacer Algorithm in C++
This post introduces the Clock Replacer algorithm and an implementation in C++.
Perceptron in C++ and Python
In this post, I’m going to cover the Perceptron Algorithm and compare its implementation in Python and C++.