Developers are increasingly relying on AI coding assistants to accelerate our daily workflows. These tools can autocomplete functions, suggest bug fixes, and even generate entire modules or MVPs. Yet, as many of us have learned, the quality of the AI’s output depends largely on the quality of the prompt you provide. In other words, prompt engineering has become an essential skill. A poorly phrased
January 30, 2023 In this post, we'll implement a GPT from scratch in just 60 lines of numpy. We'll then load the trained GPT-2 model weights released by OpenAI into our implementation and generate some text. Note: This post assumes familiarity with Python, NumPy, and some basic experience with neural networks. This implementation is for educational purposes, so it's missing lots of features/improv
Sublime Text 4
The first stable release of Sublime Text 4 has finally arrived! We've worked hard on providing improvements without losing focus on what makes Sublime Text great. There are some new major features that we hope will significantly improve your workflow and a countless number of minor improvements across the board. A huge thanks goes out to all the beta testers on discord and all the contributors to
Incident Metrics in SRE
Štěpán Davidovič Incident Metrics in SRE Critically Evaluating MTTR and Friends Boston Farnham Sebastopol Tokyo Beijing Boston Farnham Sebastopol Tokyo Beijing 978-1-098-10313-2 [LSI] Incident Metrics in SRE by Štěpán Davidovič Copyright © 2021 O’Reilly Media, Inc. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebas
Golang Mini Reference 2022: A Quick Guide to the Modern Go Programming Language (REVIEW COPY)
Golang Mini Reference 2022 A Quick Guide to the Modern Go Programming Language (REVIEW COPY) Harry Yoon Version 0.9.0, 2022-08-24 REVIEW COPY This is review copy, not to be shared or distributed to others. Please forward any feedback or comments to the author. • feedback@codingbookspress.com The book is tentatively scheduled to be published on September 14th, 2022. We hope that when the release da
AST vs. Bytecode: Interpreters in the Age of Meta-Compilation
233 AST vs. Bytecode: Interpreters in the Age of Meta-Compilation OCTAVE LAROSE, University of Kent, UK SOPHIE KALEBA, University of Kent, UK HUMPHREY BURCHELL, University of Kent, UK STEFAN MARR, University of Kent, UK Thanks to partial evaluation and meta-tracing, it became practical to build language implementations that reach state-of-the-art peak performance by implementing only an interprete
Note: I received a lot of great feedback from the discussions at Mastodon and Hacker News, so I've updated the post with some improvements to the font! I've also added some further examples and acknowledgements at the end. Syntax Highlighting in Hand-Coded Websites The problem I have been trying to identify practical reasons why hand-coding websites with HTML and CSS is so hard (by hand-coding, I
Enriching Excel with higher-order functional programming
Ever since it was released in the 1980s, Microsoft Excel has changed how people organize, analyze, and visualize their data, providing a basis for decision-making for the millions of people who use it each day. It’s also the world’s most widely used programming language. Excel formulas are written by an order of magnitude more users than all the C, C++, C#, Java, and Python programmers in the worl
Patterns for Building LLM-based Systems & Products [ llm engineering production 🔥 ] · 66 min read Discussions on HackerNews, Twitter, and LinkedIn “There is a large class of problems that are easy to imagine and build demos for, but extremely hard to make products out of. For example, self-driving: It’s easy to demo a car self-driving around a block, but making it into a product takes a decade.”
The Andrej Karpathy episode. Andrej explains why reinforcement learning is terrible (but everything else is much worse), why model collapse prevents LLMs from learning the way humans do, why AGI will just blend into the previous ~2.5 centuries of 2% GDP growth, why self driving took so long to crack, and what he sees as the future of education. Watch on YouTube; listen on Apple Podcasts or Spotify
This article is a translation, the original version is available here. I started using the OCaml language regularly around 2012, and since then, my interest and enthusiasm for this language have only grown. It has become my preferred choice for almost all my personal projects, and it has also influenced my professional choices. Since 2014, I have been actively participating in public conferences d
Try Mastering Emacs for free! Are you struggling with the basics? Have you mastered movement and editing yet? When you have read Mastering Emacs you will understand Emacs. It’s that time again: there’s a new major version of Emacs and, with it, a treasure trove of new features and changes. Notable features include the formal inclusion of native compilation, a technique that will greatly speed up y
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Faster virtual machines: Speeding up programming language execution - Mort's Ramblings
Date: 2023-01-15 Git: https://gitlab.com/mort96/blog/blob/published/content/00000-home/00015-fast-interpreters.md In this post, I hope to explore how interpreters are often implemented, what a "virtual machine" means in this context, and how to make them faster. Note: This post will contain a lot of C source code. Most of it is fairly simple C which should be easy to follow, but some familiarity w
Large Text Compression Benchmark Matt Mahoney Last update: July 3, 2025. history This competition ranks lossless data compression programs by the compressed size (including the size of the decompression program) of the first 109 bytes of the XML text dump of the English version of Wikipedia on Mar. 3, 2006. About the test data. The goal of this benchmark is not to find the best overall compressi
An AI-native software engineer is one who deeply integrates AI into their daily workflow, treating it as a partner to amplify their abilities. This requires a fundamental mindset shift. Instead of thinking “AI might replace me” an AI-native engineer asks for every task: “Could AI help me do this faster, better, or differently?”. The mindset is optimistic and proactive - you see AI as a multiplier
While getting my degree in Physics, I had to take classes in both MatLab and Python for scientific computing. I preferred python, where we used the SciPy and NumPy packages. In fact, I used those packages again (along with matplotlib) in an undergraduate research project simulating bacteria films. There's a catch: I was also pursuing a degree in Computer Science, and Python just wasn't fast enough
I find blockchain fascinating because it extends open source software development to open source + state. This seems to be a genuine/exciting innovation in computing paradigms; We don’t just get to share code, we get to share a running computer, and anyone anywhere can use it in an open and permissionless manner. The seeds of this revolution arguably began with Bitcoin, so I became curious to dril
K-means clustering is a type of unsupervised learning when we have unlabeled data (i.e., data without defined categories or groups). Clustering refers to a collection of data points based on specific similarities. K-Means AlgorithmK-means aims to find groups in the data, with the number of groups represented by the variable K. Based on the provided features, the algorithm works iteratively to assi
JupyterLab Changelog — JupyterLab 4.5.0a3 documentation
JupyterLab Changelog# v4.4# JupyterLab 4.4 includes a number of new features (described below), bug fixes, and enhancements. This release is compatible with extensions supporting JupyterLab 4.0. Extension authors are encouraged to consult the Extension Migration Guide which lists deprecations and changes to the public API. Code console improvements# The code console prompt can now be positioned on
K-nearest neighbors (k-NN) is a Machine Learning algorithm for supervised machine learning type. It is used for both regression and classification tasks. As we already know, a supervised machine learning algorithm depends on labeled input data, which the algorithm learns to produce accurate outputs when input unlabeled data. k-NN aims to predict the test data set by calculating the distance betwee
Procedural macros under the hood: Part I | The RustRover Blog
IDEs CLion DataGrip DataSpell Fleet GoLand IntelliJ IDEA PhpStorm PyCharm RustRover Rider RubyMine WebStorm Plugins & Services Big Data Tools Code With Me JetBrains Platform Scala Toolbox App Writerside JetBrains AI Grazie Junie JetBrains for Data Kineto Team Tools Datalore Space TeamCity Upsource YouTrack Hub Qodana CodeCanvas .NET & Visual Studio .NET Tools ReSharper C++ Languages & Frameworks K
Scheduling Internals
A sneak peek to what's coming! I remember when I first learned that you can write a server handling millions of clients running on just a single thread, my mind was simply blown away 🤯 I used Node.js while knowing it is single threaded, I used async / await in Python, and I used threads, but never asked myself "How is any of this possible?". This post is written to spread the genius of concurrenc
GTF :: Why Haskell?
“Impractical”, “academic”, “niche”. These are a few of the reactions I get when someone discovers that my favourite programming language is Haskell, and not only my favourite in some sort of intellectually-masturbatory way, but favourite for building things, real things, mostly involving web servers. Hobby projects would be one thing, but it gets worse: I have actual teams at Converge working in H
It’s 11 o’clock. Do you know where your variables are pointing? def shout(obj) obj.to_s + "!" end It’s hard to tell just looking at the code what type obj is. We assume it has a to_s method, but many classes define methods named to_s. Which to_s method are we calling? What is the return type of shout? If to_s doesn’t return a String, it’s really hard to say. Adding type annotations would help… a l
Today, a lot of agentic applications (such as, Microsoft Copilot, ChatGPT plugins, AutoGPT, etc) automate a variety of tasks by LLM reasoning, the ability to split a complex task into simpler subtasks. Reasoning+Acting (shortly, ReAct) is essential and origin for these task completion, which is achieved by advanced LLM reasoning. To build such autonomous agents, here I’ll show you the fundamentals
The Decision Tree algorithm is a type of tree-based modeling under Supervised Machine Learning. Decision Trees are primarily used to solve classification problems (the algorithm, in this case, is called the Classification Tree), but they can also be used to solve regression problems (the algorithm, in this case, is called the Regression Tree). The concept of trees is found in graph theory and is u
v2022: Austin Huang, Suraj Subramanian, Jonathan Sum, Khalid Almubarak, and Stella Biderman. Original: Sasha Rush. The Transformer has been on a lot of people’s minds over the last year five years. This post presents an annotated version of the paper in the form of a line-by-line implementation. It reorders and deletes some sections from the original paper and adds comments throughout. This docume
My birthday just passed, and to relax I wrote a parser combinator library. Over the last few years, I have worked quite a bit with Ningning Xie and Jeremy Yallop on parser combinators, which has led to a family of parser combinators which have optimal linear-time performance in theory, and which are many times faster than lex+yacc in practice. But these use advanced multistage programming techniqu
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The Prompt Engineering Playbook for Programmers
GPT in 60 Lines of NumPy | Jay Mody
Font with Built-In Syntax Highlighting
Patterns for Building LLM-based Systems & Products
Andrej Karpathy — AGI is still a decade away
xvw.lol - Why I chose OCaml as my primary language
What's New in Emacs 28.1?
Vim9 script for Python Developers · GitHub
Large Text Compression Benchmark
The AI-Native Software Engineer
Scientific Computing in Rust - aftix's dominion
A from-scratch tour of Bitcoin in Python
K-Means Clustering for Unsupervised Machine Learning
k-NN (k-Nearest Neighbors) in Supervised Machine Learning
Interprocedural Sparse Conditional Type Propagation
ReAct (Reason+Act) prompting in LLMs
Introduction to Decision Trees in Supervised Learning
The Annotated Transformer
Linear-time parser combinators