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
GitHub - modelcontextprotocol/servers: Model Context Protocol Servers
Official integrations are maintained by companies building production ready MCP servers for their platforms. 21st.dev Magic - Create crafted UI components inspired by the best 21st.dev design engineers. ActionKit by Paragon - Connect to 130+ SaaS integrations (e.g. Slack, Salesforce, Gmail) with Paragon’s ActionKit API. Adfin - The only platform you need to get paid - all payments in one place, in
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
A few months ago, I set myself the challenge of writing a C compiler in 500 lines of Python1, after writing my SDF donut post. How hard could it be? The answer was, pretty hard, even when dropping quite a few features. But it was also pretty interesting, and the result is surprisingly functional and not too hard to understand! There's too much code for me to comprehensively cover in a single blog
Weird Lexical Syntax
I just learned 42 programming languages this month to build a new syntax highlighter for llamafile. I feel like I'm up to my eyeballs in programming languages right now. Now that it's halloween, I thought I'd share some of the spookiest most surprising syntax I've seen. The languages I decided to support are Ada, Assembly, BASIC, C, C#, C++, COBOL, CSS, D, FORTH, FORTRAN, Go, Haskell, HTML, Java,
How I developed a faster Ruby interpreter | Red Hat Developer
In this article, I will describe my efforts to implement a faster interpreter for CRuby, the Ruby language interpreter, using a dynamically specialized internal representation (IR). I believe this article will interest developers trying to improve the interpreter performance of dynamic programming languages (e.g., CPython developers). I will cover the following topics: Existing CRuby interpreter a
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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Plan 9 Desktop Guide
PLAN 9 DESKTOP GUIDE INDEX What is Plan 9? Limitations and Workarounds Connecting to Other Systems VNC RDP SSH 9P Other methods Porting Applications Emulating other Operating Systems Virtualizing other Operating Systems Basics Window Management Copy Pasting Essential Programs Manipulating Text in the Terminal Acme - The Do It All Application Multiple Workspaces Tiling Windows Plumbing System Admin
Motivation Ever since TJ said “Personalized Development Environment,” the phrase latched onto me like a cobweb in a mineshaft. A Personalized Development Environment (PDE) describes an ideal setup that is tailored to your needs and preferences – it lies between a bare-bone text editor and a full-fledged IDE. It is a place where you can be productive, efficient, and comfortable. It is a place that
After studying how companies deploy generative AI applications, I noticed many similarities in their platforms. This post outlines the common components of a generative AI platform, what they do, and how they are implemented. I try my best to keep the architecture general, but certain applications might deviate. This is what the overall architecture looks like. This is a pretty complex system. Thi
Building a type-safe dictionary in TypeScript - LogRocket Blog
Gapur Kassym I am a full-stack engineer and writer. I'm passionate about building excellent software that improves the lives of those around me. As a software engineer, I enjoy using my obsessive attention to detail and my unequivocal love for making things that change the world. Editor’s note: This article was last updated by Shalitha Suranga on 20 February 2024 to include advanced type checking
Raising code quality for Python applications using Amazon CodeGuru | Amazon Web Services
AWS DevOps & Developer Productivity Blog Raising code quality for Python applications using Amazon CodeGuru We are pleased to announce the launch of Python support for Amazon CodeGuru, a service for automated code reviews and application performance recommendations. CodeGuru is powered by program analysis and machine learning, and trained on best practices and hard-learned lessons across millions
Fitting a Forth in 512 bytes
Fitting a Forth in 512 bytes June 10, 2021 · 31 minute read This article is part of the Bootstrapping series, in which I start from a 512-byte seed and try to bootstrap a practical system. Software is full of circular dependencies if you look deep enough. Compilers written in the language they compile are the most obvious example, but not the only one. To compile a kernel, you need a running kerne
GitHub - ComfyUI-Workflow/awesome-comfyui: A collection of awesome custom nodes for ComfyUI
ComfyUI-Gemini_Flash_2.0_Exp (⭐+172): A ComfyUI custom node that integrates Google's Gemini Flash 2.0 Experimental model, enabling multimodal analysis of text, images, video frames, and audio directly within ComfyUI workflows. ComfyUI-ACE_Plus (⭐+115): Custom nodes for various visual generation and editing tasks using ACE_Plus FFT Model. ComfyUI-Manager (⭐+113): ComfyUI-Manager itself is also a cu
Django for Startup Founders: A better software architecture for SaaS startups and consumer apps
In an ideal world, startups would be easy. We'd run our idea by some potential customers, build the product, and then immediately ride that sweet exponential growth curve off into early retirement. Of course it doesn't actually work like that. Not even a little. In real life, even startups that go on to become billion-dollar companies typically go through phases like: Having little or no growth fo
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The Prompt Engineering Playbook for Programmers
GPT in 60 Lines of NumPy | Jay Mody
Writing a C compiler in 500 lines of Python
What's New in Emacs 28.1?
Vim9 script for Python Developers · GitHub
An Experienced (Neo)Vimmer's Workflow
Building A Generative AI Platform